Aaron

M.

Cohen

MD

is

a

postdoctoral

fellow

in

the

medical

informatics

programme

at

OHSU

Dr

Cohen

works

in

the

area

of

text

mining

focusing

on

issues

and

applications

important

to

biomedical

researchers

He

was

chairman

of

the

working

group

that

produced

version

of

the

Synchronized

Multimedia

Integration

Language

-LRB-

SMIL

-RRB-

William

Hersh

MD

is

Professor

and

Chair

of

the

Department

of

Medical

Informatics

Clinical

Epidemiology

in

the

School

of

Medicine

at

Oregon

Health

Science

University

-LRB-

OHSU

-RRB-

in

Portland

Oregon

Dr

Hersh

's

research

focuses

on

the

development

and

evaluation

of

information

retrieval

systems

for

biomedical

practitioners

and

researchers

A

survey

of

current

work

in

biomedical

text

mining

Aaron

M.

Cohen

and

William

R.

Hersh

Date

received

-LRB-

in

revised

form

-RRB-

October

Abstract

The

volume

of

published

biomedical

research

and

therefore

the

underlying

biomedical

knowledge

base

is

expanding

at

an

increasing

rate

Among

the

tools

that

can

aid

researchers

in

coping

with

this

information

overload

are

text

mining

and

knowledge

extraction

Significant

progress

has

been

made

in

applying

text

mining

to

named

entity

recognition

text

classification

terminology

extraction

relationship

extraction

and

hypothesis

generation

Several

research

groups

are

constructing

integrated

flexible

text-mining

systems

intended

for

multiple

uses

The

major

challenge

of

biomedical

text

mining

over

the

next

years

is

to

make

these

systems

useful

to

biomedical

researchers

This

will

require

enhanced

access

to

full

text

better

understanding

of

the

feature

space

of

biomedical

literature

better

methods

for

measuring

the

usefulness

of

systems

to

users

and

continued

cooperation

with

the

biomedical

research

community

to

ensure

that

their

needs

are

addressed

Keywords

text-mining

bioinformatics

natural

language

processing

INTRODUCTION

BACKGROUND

AND

PURPOSE

The

volume

of

published

biomedical

research

and

therefore

the

underlying

biomedical

knowledge

base

is

expanding

at

an

increasing

rate

While

scientific

information

in

general

has

been

growing

exponentially

for

several

centuries

the

absolute

numbers

specific

to

modern

medicine

are

very

impressive

The

MEDLINE

database

contains

over

million

records

and

the

database

is

currently

growing

at

the

rate

of

new

citations

each

year

With

such

explosive

growth

it

is

extremely

challenging

to

keep

up

to

date

with

all

of

the

new

discoveries

and

theories

even

within

one

's

own

field

of

biomedical

research

Biomedical

research

is

divided

into

highly

specialised

fields

and

subfields

with

poor

communication

between

disciplines

While

this

may

be

a

necessary

pre-condition

for

the

complex

and

detailed

research

that

biomedical

science

requires

it

also

tends

to

narrow

the

perspective

impeding

the

establishment

of

connections

between

discoveries

Aaron

Michael

Cohen

Postdoctoral

Fellow

Department

of

Medical

Informatics

and

Clinical

Epidemiology

School

of

Medicine

Oregon

Health

Science

University

S.W.

Sam

Jackson

Park

Road

Portland

OR

USA

Tel

503 494 0046

Fax

503 494 4551

E-mail

cohenaa@ohsu.edu

arising

from

different

research

specialties

With

the

recent

sequencing

of

the

human

genome

the

addition

of

detailed

genetic

information

to

biomedical

research

makes

the

situation

even

more

complicated

since

genetics

may

play

a

role

in

almost

all

areas

of

health

and

disease

and

it

is

likely

that

many

connections

between

different

branches

of

medicine

may

be

based

on

related

genomic

mechanisms

The

goal

of

biomedical

research

is

to

discover

knowledge

and

put

it

to

practical

use

in

the

forms

of

diagnosis

prevention

and

treatment

Clearly

with

the

current

rate

of

growth

in

published

biomedical

research

it

becomes

increasingly

likely

that

important

connections

between

individual

elements

of

biomedical

knowledge

that

could

lead

toward

practical

use

are

not

being

recognised

because

there

is

no

individual

in

a

position

to

make

the

necessary

connections

Methods

must

be

established

to

aid

researchers

and

physicians

in

making

more

efficient

use

of

the

existing

research

and

helping

them

take

this

research

to

the

next

step

along

the

path

to

practical

application

While

manual

curation

and

indexing

can

be

an

aid

to

researchers

searching

for

appropriate

literature

a

HENRY

STEWART

PUBLICATIONS

B

R

I

E

F

I

N

G

S

I

N

B

I

O

I

N

F

O

R

M

A

T

I

C

S

VOL

NO

MARCH

Cohen

and

Hersh

The

goal

of

biomedical

text

mining

is

to

shift

the

burden

of

information

overload

from

the

researcher

to

the

computer

Recognising

biological

entities

in

text

allows

for

further

extraction

of

relationships

and

other

information

by

identifying

the

key

concepts

of

interest

recent

study

of

the

information

content

of

MEDLINE

records

by

Kostoff

et

al.

found

a

significant

amount

of

conceptual

information

present

only

in

the

abstract

field

and

missing

from

the

MeSH

terms

This

is

not

surprising

since

the

MEDLINE

indexers

and

the

MeSH

vocabulary

while

broadly

based

can

not

be

expected

to

represent

all

of

the

concepts

of

interest

for

all

potential

users

Clearly

the

full

text

of

biomedical

literature

contains

a

wealth

of

information

important

to

users

that

may

not

be

completely

captured

by

reviewers

and

curators

Text

mining

and

knowledge

extraction

are

ways

to

aid

researchers

in

coping

with

information

overload

Text

mining

is

differentiated

from

both

information

retrieval

-LRB-

IR

-RRB-

and

text

summarisation

-LRB-

TS

-RRB-

in

that

while

IR

and

TS

focus

on

the

larger

units

of

text

such

as

documents

text

mining

operates

at

a

finer

level

of

granularity

and

examines

the

relationships

between

specific

kinds

of

information

contained

both

within

and

between

documents

Text

mining

is

also

differentiated

from

full-blown

natural

language

processing

-LRB-

NLP

-RRB-

in

that

NLP

attempts

to

understand

the

meaning

of

text

as

a

whole

while

text

mining

and

knowledge

extraction

concentrate

on

solving

a

specific

problem

in

a

specific

domain

identified

a

priori

-LRB-

possibly

using

some

NLP

techniques

in

the

process

-RRB-

For

example

text

mining

can

aid

database

curators

by

selecting

articles

most

likely

to

contain

information

of

interest

or

potential

new

treatments

for

migraine

may

be

determined

by

looking

for

pharmacological

substances

that

are

associated

with

biological

processes

associated

with

migraine

The

goal

of

biomedical

text

mining

is

therefore

to

allow

researchers

to

identify

needed

information

more

efficiently

uncover

relationships

obscured

by

the

sheer

volume

of

available

information

and

in

general

shift

the

burden

of

information

overload

from

the

researcher

to

the

computer

by

applying

algorithmic

statistical

and

data

management

methods

to

the

vast

amount

of

biomedical

knowledge

that

exists

in

the

literature

as

well

as

the

free

text

fields

of

biomedical

databases

This

paper

surveys

the

state

of

the

art

in

biomedical

text

mining

over

the

past

months

The

next

section

covers

current

active

areas

of

research

including

the

specific

problems

that

are

being

addressed

and

the

approaches

used

This

is

followed

by

an

examination

of

the

current

issues

and

future

challenges

of

biomedical

text

mining

CURRENT

AREAS

OF

RESEARCH

While

other

authors

have

proposed

categorisations

based

on

stages

of

information

extraction

of

increasing

sophistication

here

recent

work

is

grouped

pragmatically

with

separate

categories

for

each

distinct

type

of

textmining

task

This

is

because

current

work

centres

around

several

common

textmining

themes

Named

entity

recognition

At

first

glace

the

task

of

named

entity

recognition

-LRB-

NER

-RRB-

appears

straightforward

The

goal

is

to

identify

within

a

collection

of

text

all

of

the

instances

of

a

name

for

a

specific

type

of

thing

for

example

all

of

the

drug

names

within

a

collection

of

journal

articles

or

all

of

the

gene

names

and

symbols

within

a

collection

of

MEDLINE

abstracts

Hansich

and

de

Bruijn

and

believed

that

solving

this

problem

would

allow

more

complex

text-mining

tasks

to

be

addressed

The

idea

is

that

recognising

biological

entities

in

text

allows

for

further

extraction

of

relationships

and

other

information

by

identifying

the

key

concepts

of

interest

and

allowing

those

concepts

to

be

represented

in

some

consistent

normalised

form

This

task

has

been

challenging

for

several

reasons

First

there

does

not

exist

a

complete

dictionary

for

most

types

of

biological

named

entities

so

simple

textmatching

algorithms

do

not

suffice

In

addition

the

same

word

or

phrase

can

HENRY

STEWART

PUBLICATIONS

B

R

I

E

F

I

N

G

S

I

N

B

I

O

I

N

F

O

R

M

A

T

I

C

S

VOL

NO

MARCH

Current

work

in

biomedical

text

mining

Approaches

to

NER

generally

fall

into

three

categories

lexiconbased

rules-based

and

statistically-based

refer

to

a

different

thing

depending

upon

context

-LRB-

eg

ferritin

can

be

a

biological

substance

or

a

laboratory

test

-RRB-

Conversely

many

biological

entities

have

several

names

-LRB-

eg

PTEN

and

refer

the

same

gene

-RRB-

Biological

entities

may

also

have

multi-word

names

-LRB-

eg

carotid

artery

-RRB-

so

the

problem

is

additionally

complicated

by

the

need

to

determine

name

boundaries

and

resolve

overlap

of

candidate

names

Because

of

the

potential

utility

and

complexity

of

the

problem

NER

has

attracted

the

interest

of

many

researchers

and

there

is

a

tremendous

amount

of

published

research

in

this

topic

With

the

large

amount

of

genomic

information

being

generated

by

biomedical

researchers

it

should

not

be

surprising

that

in

the

genomics

era

much

of

the

work

in

biomedical

NER

has

focused

on

recognising

gene

and

protein

names

in

free

text

The

approaches

generally

fall

into

three

categories

lexicon-based

rules-based

and

statistically

based

Combined

approaches

also

have

been

used

The

output

may

be

a

set

of

tags

assigning

a

predicted

type

to

each

word

or

phrase

of

interest

as

in

part-of-speech

-LRB-

POS

-RRB-

tagging

or

as

a

score

designating

the

confidence

that

a

word

or

phrase

is

of

a

given

type

of

interest

Systems

are

typically

measured

in

terms

of

precision

-LRB-

number

of

correct

predictions

divided

by

total

number

of

predictions

-RRB-

and

recall

-LRB-

number

of

correct

predictions

divided

by

number

of

actual

named

entities

in

the

text

-RRB-

Precision

and

recall

are

often

combined

into

a

single

measure

either

using

the

F-score

defined

as

the

harmonic

mean

of

precision

and

recall

-LRB-

-LSB-

P+R

-RSB-

-RRB-

or

by

reporting

the

balanced

precision

and

recall

defined

as

the

point

where

precision

and

recall

are

equal

One

of

the

most

successful

rules-based

approaches

to

gene

and

protein

NER

in

biomedical

texts

has

been

the

AbGene

system

of

Tanabe

and

Wilbur

It

has

been

used

as

the

NER

component

in

extracting

relationships

by

several

other

researchers

AbGene

works

by

extending

the

Brill

POS

to

include

gene

and

protein

names

as

a

tag

type

with

the

system

trained

on

hand-tagged

sentences

from

biomedical

text

AbGene

then

applies

manually

generated

post-processing

rules

based

on

lexical-statistical

characteristics

that

help

further

identify

the

context

in

which

gene

names

are

used

and

eliminate

false

positives

and

negatives

The

system

achieved

a

precision

of

per

cent

at

a

recall

of

per

cent

In

contrast

to

the

tagging

approach

used

by

Tanabe

and

Wilbur

Chang

et

al.

created

the

GAPSCORE

system

which

assigns

a

numerical

score

to

each

word

within

a

sentence

by

examining

the

appearance

morphology

and

context

of

the

word

and

then

applying

a

classifier

trained

on

these

features

Words

with

higher

scores

are

more

likely

to

be

gene

and

protein

names

or

symbols

After

training

on

the

Yapex

corpus

precision

recall

and

F-score

were

computed

for

both

the

exact

matches

and

sloppy

matches

-LRB-

defined

as

a

true

positive

if

any

part

of

gene

name

is

predicted

correctly

-RRB-

with

the

system

performing

much

better

with

sloppy

matches

-LRB-

precision

per

cent

recall

per

cent

F-measure

per

cent

-RRB-

than

with

exact

matches

-LRB-

precision

per

cent

recall

per

cent

F-measure

per

cent

-RRB-

A

number

of

other

groups

have

worked

in

this

area

Hanisch

et

al.

used

a

large

dictionary

of

gene

and

protein

names

and

semantically

classified

words

that

tend

to

appear

in

context

with

protein

names

reporting

a

specificity

of

per

cent

and

sensitivity

of

per

cent

Zhou

et

al.

trained

a

hidden

Markov

model

-LRB-

HMM

-RRB-

on

a

set

of

features

based

on

word

formation

-LRB-

ie

capitalisation

-RRB-

morphology

-LRB-

ie

prefix

and

suffix

-RRB-

POS

semantic

triggers

-LRB-

head

nouns

and

verbs

-RRB-

and

intra-document

name

aliases

They

reported

an

overall

precision

of

per

cent

at

a

recall

of

per

cent

on

the

GENIA

corpus

Other

gene

and

protein

NER

systems

include

those

by

Narayanaswamy

et

al.

Settles

and

Mika

and

Rost

HENRY

STEWART

PUBLICATIONS

B

R

I

E

F

I

N

G

S

I

N

B

I

O

I

N

F

O

R

M

A

T

I

C

S

VOL

NO

MARCH

Cohen

and

Hersh

Overall

the

performance

of

state-ofthe-art

gene

and

protein

NER

systems

achieves

F-scores

between

and

percent

Chen

and

Friedman

have

adapted

the

MEDLEE

system

to

recognise

phrases

that

correspond

to

phenotype

information

within

biomedical

text

This

system

uses

natural

language

techniques

to

identify

phenotypic

phrases

present

in

journal

article

abstracts

and

recognises

phrases

containing

words

separated

in

the

text

This

area

of

biological

NER

is

much

less

well

studied

than

recognising

gene

protein

or

chemical

names

and

therefore

a

smaller

knowledge

base

of

phenotypeassociated

terms

is

available

Nevertheless

the

investigators

were

able

to

automatically

import

thousands

of

UMLS

terms

associated

with

semantic

categories

such

as

cellular

body

functions

and

cellular

dysfunction

as

well

as

several

hundred

terms

from

the

Mammalian

Ontology

A

few

hundred

other

terms

were

added

manually

In

a

feasibility

study

of

documents

the

system

achieved

a

precision

of

per

cent

with

a

recall

of

per

cent

While

as

expected

for

a

new

area

of

study

this

performance

is

lower

than

that

of

the

gene

and

protein

NER

systems

these

results

were

found

to

be

about

the

same

as

that

of

the

individual

experts

used

to

create

the

study

's

gold

standard

Overall

the

performance

of

state-ofthe-art

gene

and

protein

NER

systems

achieves

F-scores

between

and

per

cent

This

number

is

consistent

with

that

found

by

Hirschman

et

al.

in

and

the

results

of

Task

for

the

BioCreative

workshop

While

peak

performance

does

not

appear

to

have

increased

over

the

past

few

years

investigators

are

obtaining

consistent

results

using

a

variety

of

approaches

on

different

data

sets

To

address

this

performance

plateau

and

to

decrease

the

computational

burden

contribution

of

NER

to

text

mining

Tanabe

and

Wilbur

have

used

AbGene

to

generate

a

large

and

high-quality

gene

and

protein

lexicon

of

names

found

in

biomedical

text

The

application

of

AbGene

to

the

MEDLINE

database

has

resulted

in

an

initial

collection

of

over

two

million

putative

gene

and

protein

names

This

list

was

purified

by

applying

thematic

analysis

to

the

names

and

then

using

inductive

logic

programming

to

learn

rules

for

differentiating

gene

names

from

non-gene

names

within

a

theme

Finally

a

simple

false-positive

filter

was

applied

that

removed

obviously

incorrect

names

such

as

those

containing

http

or

ending

in

tion

Their

approach

yielded

a

final

set

of

gene

names

Assessment

of

a

random

sample

determined

the

precision

to

be

approximately

per

cent

Comparison

with

a

gold

standard

gave

an

estimated

recall

of

per

cent

for

exact

matches

and

per

cent

for

partial

matches

The

quality

of

this

lexicon

is

about

equivalent

to

the

performance

of

the

NER

systems

and

the

large

size

of

the

lexicon

is

a

definite

advantage

The

lexicon

could

be

used

with

simple

or

fuzzy

matching

to

efficiently

identify

gene

and

protein

names

as

a

first

step

in

future

text-mining

systems

However

the

list

has

been

generated

with

a

snapshot

of

MEDLINE

and

given

the

pace

of

genomic

research

will

soon

be

out

of

date

if

it

is

not

updated

Also

the

list

was

built

from

MEDLINE

and

not

from

full

text

articles

so

it

is

possible

that

a

significant

number

of

gene

and

protein

names

exist

in

the

literature

and

are

not

found

in

MEDLINE

It

is

a

subject

of

current

debate

how

well

NER

must

perform

in

order

to

be

useful

for

text

mining

If

one

assumes

that

relationship

extraction

requires

identification

of

three

biomedical

terms

-LRB-

two

entities

and

one

relationship

-RRB-

the

performance

of

relationship

extraction

should

be

approximately

equal

to

the

cube

of

the

performance

of

NER

This

independence

assumption

appears

to

be

true

for

news

article

extraction

Systems

performing

named

entity

extraction

on

news

stories

typically

perform

at

an

F-score

over

per

cent

and

the

F-score

for

new

relations

is

about

per

cent

For

many

biomedical

applications

the

F-score

performance

rates

of

relationship

mining

has

often

been

found

to

be

approximately

equal

to

that

of

biological

HENRY

STEWART

PUBLICATIONS

B

R

I

E

F

I

N

G

S

I

N

B

I

O

I

N

F

O

R

M

A

T

I

C

S

VOL

NO

MARCH

Current

work

in

biomedical

text

mining

The

independence

assumption

does

not

seem

to

hold

for

biological

relations

NER

rather

than

the

per

cent

expected

by

the

independence

assumption

Therefore

the

assumption

does

not

seem

to

hold

for

biological

relations

It

may

be

easier

to

extract

concepts

in

combination

with

the

relationship

between

them

owing

to

the

increased

local

context

that

relationships

provide

While

some

form

of

NER

is

useful

in

most

text-mining

tasks

the

performance

level

of

biological

NER

is

not

necessarily

rate

limiting

for

other

biological

text-mining

tasks

Nevertheless

we

have

not

reached

the

point

of

having

standard

methods

of

NER

or

updated

lexicons

for

biomedical

text

mining

-LRB-

whatever

the

asymptotic

performance

level

-RRB-

so

work

must

continue

in

this

area

Text

classification

Text

classification

attempts

to

automatically

determine

whether

a

document

or

part

of

a

document

has

particular

characteristics

of

interest

usually

based

on

whether

the

document

discusses

a

given

topic

or

contains

a

certain

type

of

information

Typically

the

information

of

interest

is

not

specified

explicitly

by

the

users

and

instead

they

provide

a

set

of

documents

that

have

been

found

to

contain

the

characteristics

of

interest

-LRB-

the

positive

training

set

-RRB-

and

another

set

that

does

not

-LRB-

the

negative

training

set

-RRB-

Text

classification

systems

must

automatically

extract

the

features

that

help

determine

positives

from

negatives

and

apply

those

features

to

candidate

documents

using

some

kind

of

decision-making

process

Accurate

text

classification

systems

can

be

especially

valuable

to

database

curators

who

may

have

to

review

many

documents

to

find

a

few

that

contain

the

kind

of

information

they

are

collecting

in

their

database

Because

more

biomedical

information

is

being

created

in

text

form

then

ever

before

and

because

there

are

more

ongoing

database

curation

efforts

to

organise

this

information

into

coded

databases

than

before

there

is

a

strong

need

to

find

useful

ways

to

apply

text

classification

methods

to

biomedical

text

Accurate

text

classification

systems

can

be

especially

valuable

to

database

curators

Yeh

et

al.

ran

a

text-mining

competition

as

part

of

the

Knowledge

Discovery

in

Databases

-LRB-

KDD

-RRB-

Challenge

Cup

The

task

was

a

curation

problem

to

evaluate

papers

from

the

FlyBase

data

set

and

determine

whether

the

paper

should

be

curated

based

on

the

presence

of

experimental

evidence

of

Drosophila

gene

products

The

bestperforming

entry

used

a

set

of

manually

constructed

rules

based

on

POS

tagging

a

lexicon

and

semantic

constraints

determined

by

examining

the

training

documents

The

system

focused

on

figure

captions

which

were

found

to

be

useful

An

F-score

of

per

cent

was

achieved

on

determining

whether

to

curate

a

paper

based

on

the

presence

of

experimental

evidence

Another

effective

approach

looked

for

manually

chosen

keywords

and

computed

the

distance

between

keywords

and

gene

names

Two

other

well-performing

systems

used

regular

expressions

to

find

patterns

of

words

and

then

used

a

support

vector

machine

-LRB-

SVM

-RRB-

to

classify

the

papers

In

related

work

Donaldson

et

al.

used

an

SVM

trained

on

the

words

in

MEDLINE

abstracts

to

distinguish

abstracts

containing

information

on

protein

protein

interactions

prior

to

curating

this

information

into

their

BIND

database

They

used

the

bag-of-words

approach

with

an

SVM

classifier

A

small

evaluation

with

abstracts

found

a

precision

of

per

cent

with

a

recall

of

per

cent

They

estimated

that

the

classification

system

would

reduce

the

number

of

abstracts

that

the

curators

needed

to

read

by

about

two-thirds

Another

investigation

in

this

area

used

a

Probabilistic

Latent

Categoriser

-LRB-

PLC

-RRB-

with

Kullback

Leibler

-LRB-

KL

-RRB-

divergence

to

re-rank

documents

returned

by

PubMed

searching

for

the

purposes

of

curating

information

into

the

Swiss-Prot

database

Evaluation

showed

a

per

cent

precision

improvement

with

a

balanced

precision

and

recall

point

of

about

per

cent

compared

with

about

per

cent

for

the

basic

PubMed

ranking

Liu

et

al.

performed

a

unique

application

HENRY

STEWART

PUBLICATIONS

B

R

I

E

F

I

N

G

S

I

N

B

I

O

I

N

F

O

R

M

A

T

I

C

S

VOL

NO

MARCH

Cohen

and

Hersh

Because

of

the

potential

to

improve

annotator

productivity

work

on

improving

biomedical

text

classification

must

continue

for

the

foreseeable

future

of

text

classification

on

figure

captions

In

a

pilot

study

they

classified

the

text

in

figure

legends

in

order

to

find

figures

containing

representations

of

protein

interactions

and

signalling

events

Applying

research

in

text

classification

to

the

work

processes

of

actual

biomedical

curators

and

annotators

is

just

beginning

The

Text

Retrieval

Conference

-LRB-

TREC

-RRB-

Genomics

Track

has

a

classification

problem

as

one

of

its

tasks

The

task

is

meant

to

mimic

the

process

that

the

human

annotators

in

the

Mouse

Genome

Informatics

-LRB-

MGI

-RRB-

system

go

through

in

order

to

find

documents

that

contain

experimental

evidence

about

genes

that

they

are

annotating

using

Gene

Ontology

-LRB-

GO

-RRB-

codes

A

full

text

collection

in

SGML

format

has

been

assembled

realistically

reflecting

the

articles

that

MGI

annotators

currently

read

In

additional

the

utility

measure

used

to

evaluate

performance

of

the

task

aims

to

reflect

the

priorities

of

the

MGI

annotators

Because

of

the

potential

to

improve

annotator

productivity

work

on

improving

biomedical

text

classification

to

meet

the

needs

of

curators

and

other

users

must

continue

for

the

foreseeable

future

Synonym

and

abbreviation

extraction

Paralleling

the

growth

of

the

increase

in

biomedical

literature

is

the

growth

in

biomedical

terminology

Because

many

biomedical

entities

have

multiple

names

and

abbreviations

it

would

be

advantageous

to

have

an

automated

means

to

collect

these

synonyms

and

abbreviations

to

aid

users

doing

literature

searches

Furthermore

other

text-mining

tasks

could

be

done

more

efficiently

if

all

of

the

synonyms

and

abbreviations

for

an

entity

could

be

mapped

to

a

single

term

representing

the

concept

Most

of

the

work

in

this

type

of

extraction

has

focused

on

uncovering

gene

name

synonyms

and

biomedical

term

abbreviations

Several

investigators

have

used

gene

name

synonym

lists

created

from

online

databases

as

a

basis

for

further

text

mining

However

these

gene

databases

focus

on

official

names

and

alternates

and

are

incomplete

with

respect

to

the

gene

names

actually

found

in

the

literature

In

order

to

create

gene

and

protein

name

synonym

lists

representative

of

the

names

used

in

the

literature

Yu

and

and

have

investigated

automatic

means

of

extracting

gene

name

synonyms

from

biomedical

free

text

Yu

and

Agichtein

applied

a

combination

of

four

algorithms

to

full

text

journal

articles

Their

system

combined

the

AbGene

gene

NER

system

with

statistical

SVM

classifier-based

automatic

pattern-based

and

manual

rules

algorithms

The

combined

system

produced

a

recall

of

about

per

cent

with

a

precision

of

about

per

cent

giving

an

overall

F-measure

of

about

per

cent

Cohen

applied

an

automatic

pattern

extraction

method

to

MEDLINE

abstracts

and

a

numeric

analysis

metric

on

the

resulting

name

co-occurrence

network

to

select

the

best

synonym

extraction

patterns

While

no

sophisticated

gene

NER

was

used

evaluation

showed

a

precision

of

per

cent

a

recall

of

per

cent

and

an

F-score

of

per

cent

The

system

was

also

notable

for

inferring

synonyms

based

on

the

logical

relationship

between

synonyms

found

explicitly

in

the

text

increasing

recall

by

about

per

cent

over

the

same

system

without

inference

Other

investigators

have

applied

textmining

methods

to

extracting

lists

of

biomedical

abbreviations

and

their

fully

specified

forms

These

methods

rely

on

the

proximity

of

full

forms

and

their

abbreviations

and

the

fact

that

either

the

full

form

or

the

abbreviation

is

often

enclosed

in

parentheses

The

problem

is

often

reduced

to

finding

the

best

alignment

of

the

characters

in

the

abbreviation

to

those

in

the

full

form

A

variety

of

alignment

and

scoring

methods

have

been

applied

to

this

basic

approach

Liu

and

Friedman

used

a

large

HENRY

STEWART

PUBLICATIONS

B

R

I

E

F

I

N

G

S

I

N

B

I

O

I

N

F

O

R

M

A

T

I

C

S

VOL

NO

MARCH

Current

work

in

biomedical

text

mining

It

is

thought

that

grouping

genes

by

functional

relationships

could

aid

gene

expression

analysis

and

database

annotation

collection

of

MEDLINE

abstracts

to

determine

abbreviations

and

phrases

that

were

statistically

significantly

colocated

They

reported

a

precision

of

per

cent

with

a

recall

of

per

cent

Yu

et

al.

and

Schwartz

and

applied

manually

created

set

of

pattern-matching

rules

to

identify

abbreviations

and

their

full

form

Yu

et

al.

achieved

a

precision

of

per

cent

with

per

cent

recall

while

Schwartz

and

Hearst

achieved

a

precision

of

per

cent

at

per

cent

recall

for

a

set

of

MEDLINE

abstracts

mentioning

yeast

Chang

et

al.

trained

a

logistic

regression

model

with

abbreviation

specific

features

and

used

it

to

score

candidate

full

forms

achieving

a

precision

of

per

cent

with

per

cent

recall

on

the

Medstract

corpus

The

automatic

extraction

of

biomedical

abbreviations

and

their

corresponding

definition

as

used

within

an

individual

journal

article

is

close

to

being

a

solved

problem

Research

systems

uniformly

produce

high

precision

and

recall

The

next

step

is

to

integrate

these

automated

extraction

capabilities

into

user

systems

For

example

an

online

dictionary

of

medical

abbreviations

could

be

integrated

into

PubMed

to

augment

search

queries

The

more

general

problem

of

resolving

common

domain

abbreviations

undefined

in

a

given

journal

article

is

a

much

more

difficult

problem

dependent

on

expert

knowledge

of

the

specific

field

and

additional

possibly

subtle

context

from

the

surrounding

text

Gene

and

protein

name

synonym

extraction

has

proven

to

be

a

more

challenging

problem

While

an

automatically

updated

synonym

list

would

be

of

great

value

in

augmenting

literature

searching

and

text

mining

the

precision

of

automatic

extraction

systems

is

low

enough

to

introduce

an

unacceptable

level

of

noise

However

work

is

being

undertaken

to

standardise

the

use

of

official

gene

and

protein

names

and

symbols

so

this

problem

may

lessen

in

the

future

On

the

other

hand

there

will

still

be

a

large

legacy

of

literature

that

uses

non-official

names

Relationship

extraction

The

goal

of

relationship

extraction

is

to

detect

occurrences

of

a

prespecified

type

of

relationship

between

a

pair

of

entities

of

given

types

While

the

type

of

the

entities

is

usually

very

specific

-LRB-

eg

genes

proteins

or

drugs

-RRB-

the

type

of

relationship

may

be

very

general

-LRB-

eg

any

biochemical

association

-RRB-

or

very

specific

-LRB-

eg

a

regulatory

relationship

-RRB-

Several

approaches

to

extracting

relations

of

interest

have

been

reported

in

the

literature

and

are

applicable

to

this

work

Manually

generated

template-based

methods

use

patterns

-LRB-

usually

in

the

form

of

regular

expressions

-RRB-

generated

by

domain

experts

to

extract

concepts

connected

by

a

specific

relation

from

text

Automatic

template

methods

create

similar

templates

automatically

by

generalising

patterns

from

text

surrounding

concept

pairs

known

to

have

the

relationship

of

interest

Statistical

methods

identify

relationships

by

looking

for

concepts

that

are

found

with

each

other

more

often

than

would

be

predicted

by

chance

Finally

NLP-based

methods

perform

a

substantial

amount

of

sentence

parsing

to

decompose

the

text

into

a

structure

from

which

relationships

can

be

readily

extracted

In

the

current

genomic

era

most

investigation

of

this

type

has

centred

around

relationships

between

genes

and

proteins

It

is

thought

that

grouping

genes

by

functional

relationships

could

aid

gene

expression

analysis

and

database

annotation

Several

researchers

have

investigated

the

extraction

of

general

relationships

between

genes

Genes

can

be

grouped

or

clustered

based

on

how

strongly

they

share

words

in

text

containing

their

names

Raychaudhuri

et

al.

used

a

measure

of

neighbour

divergence

to

measure

the

functional

coherence

of

a

group

of

genes

They

obtained

per

cent

sensitivity

at

per

cent

specificity

for

distinguishing

true

gene

groups

from

HENRY

STEWART

PUBLICATIONS

B

R

I

E

F

I

N

G

S

I

N

B

I

O

I

N

F

O

R

M

A

T

I

C

S

VOL

NO

MARCH

Cohen

and

Hersh

Currently

the

precision

and

recall

obtained

for

relationship

extraction

is

dependent

on

the

type

of

relationship

to

be

extracted

and

literature

corpus

to

be

processed

randomly

assembled

groups

of

yeast

genes

They

later

extended

their

work

to

include

mouse

fly

worm

and

yeast

genes

and

obtained

functional

gene

groups

with

and

per

cent

sensitivity

at

per

cent

specificity

Glenisson

et

al.

similarly

investigated

text-based

gene

clustering

using

a

vector

space

approach

and

the

k-medoids

algorithm

with

a

cosine

similarity

metric

Wren

and

Garner

identified

related

genes

by

analysing

the

cohesiveness

and

specificity

of

the

graph

structure

created

by

the

gene

gene

co-occurrences

in

MEDLINE

records

They

obtained

similar

results

to

Raychaudhuri

et

al.

of

about

per

cent

specificity

at

per

cent

sensitivity

Other

research

has

concentrated

extracting

specific

kinds

of

relationships

between

genes

protein

or

other

biological

entities

Gaizauskas

et

al.

's

Protein

Active

Site

Template

Acquisition

system

-LRB-

PASTA

-RRB-

uses

type

and

POS

tagging

along

with

manually

created

templates

and

lexicons

assembled

from

biological

databases

to

extract

relationships

between

amino

acid

residues

and

their

function

within

a

protein

Balanced

recall

and

precision

was

approximately

per

cent

using

a

manually

annotated

corpus

of

MEDLINE

abstracts

as

a

gold

standard

Albert

et

al.

used

dictionaries

of

protein

and

interaction

terms

to

identify

trioccurrences

of

two

proteins

and

one

interaction

within

a

sentence

Applying

this

approach

to

the

full

MEDLINE

database

looking

for

interactions

between

proteins

and

nuclear

receptors

they

found

positive

interactions

giving

a

precision

of

per

cent

McDonald

et

al.

combined

a

hybrid

syntactic/semantic

grammar

in

a

single

parsing

process

to

extract

a

variety

of

gene

pathway

relationships

Evaluation

using

abstracts

manually

reviewed

by

a

biologist

showed

per

cent

precision

at

per

cent

recall

Extracting

relationships

between

genes

or

proteins

and

GO

codes

is

a

task

with

immediate

practical

potential

that

has

received

much

attention

lately

The

MeKE

system

of

Chiang

and

Yu

used

GO

codes

as

a

lexicon

of

function

names

combining

it

with

a

lexicon

of

gene

and

gene

product

names

from

LocusLink

and

used

a

sentence

alignment

system

to

determine

patterns

associated

with

statements

about

gene

function

They

then

used

the

patterns

with

a

Naive

Bayes

classifier

to

extract

sentences

containing

information

about

gene

product

function

Raychaudhuri

et

al.

assigned

GO

codes

by

training

text

classifiers

to

associate

GO

codes

with

abstracts

and

then

assigning

to

a

gene

the

strongest

maximum

entropy

associated

GO

code

from

the

abstracts

in

which

that

gene

appeared

Evaluation

using

a

subset

of

yeast

genes

and

GO

codes

showed

that

the

strongest

predicted

GO

code

was

accurate

about

per

cent

of

the

time

Pan

et

al.

's

Dragon

TF

association

miner

system

used

linear

discriminate

analysis

on

terms

and

neural

networks

to

create

models

that

recognised

abstracts

that

contained

information

relating

transcription

factors

-LRB-

TFs

-RRB-

with

GO

codes

and

diseases

Balanced

sensitivity

and

specificity

was

about

per

cent

Task

of

the

BioCreative

workshop

similarly

focused

on

extracting

relevant

GO

codes

for

genes

from

free

text

The

task

was

to

identify

text

that

contained

evidence

for

GO

code

assignment

and

to

predict

the

correct

GO

code

that

should

be

assigned

by

that

text

The

task

was

also

notable

in

that

full

text

journal

articles

were

used

and

the

evaluation

was

carried

out

by

MGI

curators

who

rated

how

useful

the

system

output

was

for

annotation

In

general

this

was

a

difficult

task

and

was

rated

very

hard

by

the

annotators

System

precision

ranged

between

and

per

cent

with

the

average

about

per

cent

Recall

was

not

evaluated

Part

of

the

difficulty

was

that

the

systems

had

to

get

the

text

the

gene

and

the

GO

code

simultaneously

all

correct

perhaps

an

unnecessarily

high

standard

A

number

of

other

investigators

have

applied

text

mining

to

extract

novel

interesting

relationships

Eskin

and

HENRY

STEWART

PUBLICATIONS

B

R

I

E

F

I

N

G

S

I

N

B

I

O

I

N

F

O

R

M

A

T

I

C

S

VOL

NO

MARCH

Current

work

in

biomedical

text

mining

Extraction

of

very

general

non-specific

relationships

appears

to

be

straightforward

Agichtein

combined

text

and

sequence

mining

with

an

SVM

combined

text

and

genome

sequence

kernel

to

predict

protein

subcellular

localisation

Performance

ranged

from

a

precision

of

per

cent

with

per

cent

recall

for

proteins

located

in

the

cytoplasm

to

a

precision

of

per

cent

with

per

cent

recall

for

proteins

located

in

the

peroxisomes

Srinivasan

and

Wedemeyer

have

studied

the

relationship

between

a

disease

's

incidence

and

the

countries

in

which

it

is

studied

Kostoff

used

simple

MEDLINE

querying

to

compute

organ

cancer

asymmetries

and

found

very

similar

results

to

numbers

in

the

National

Cancer

Institutes

SEER

database

Xu

et

al.

adapted

MEDLEE

to

transform

text

into

coded

data

from

pathology

reports

to

facilitate

a

breast

cancer

study

It

is

clear

from

the

foregoing

work

that

some

types

of

relationships

are

simpler

to

extract

than

others

Very

general

nonspecific

relationships

-LRB-

eg

gene

groups

-RRB-

seem

to

be

fairly

straightforward

while

very

specific

relationships

that

have

to

be

substantiated

by

the

precise

supporting

text

-LRB-

eg

GO

code

assignment

-RRB-

remain

challenging

Since

the

value

of

identifying

very

specific

relationships

with

accompanying

supporting

text

is

high

this

work

must

receive

continued

attention

Hypothesis

generation

While

relationship

extraction

focuses

on

the

extraction

of

relationships

between

entities

explicitly

found

in

the

text

hypothesis

generation

attempts

to

uncover

relationships

that

are

not

present

in

the

text

but

instead

are

inferred

by

the

presence

of

other

more

explicit

relationships

The

goal

is

to

uncover

previously

unrecognised

relationships

worthy

of

further

investigation

Practically

all

of

the

work

in

hypothesis

generation

makes

use

of

an

idea

originated

by

Swanson

in

the

called

the

complementary

structures

in

disjoint

literatures

-LRB-

CSD

-RRB-

Swanson

realised

that

large

databases

of

scientific

literature

could

allow

discoveries

to

be

made

by

connecting

concepts

using

logical

Current

work

in

hypothesis

generation

makes

use

of

complementary

structures

in

disjoint

literatures

inference

He

proposed

a

simple

A

influences

B

and

B

influences

C

therefore

A

may

influence

C

model

for

detecting

instances

of

CSD

that

is

commonly

referred

to

as

Swanson

's

ABC

model

In

several

published

papers

in

the

and

early

Swanson

gave

examples

of

discovering

new

hypotheses

by

manually

connecting

concepts

between

journal

articles

In

he

found

a

connection

implying

patient

benefit

between

fish

oil

and

Raynaud

's

syndrome

two

years

before

clinical

trials

established

that

the

benefit

was

real

In

another

article

he

traced

indirect

connections

between

migraine

and

magnesium

using

summarisations

of

published

that

were

later

experimentally

verified

While

Swanson

applied

his

model

manually

several

investigators

have

tried

to

automate

the

process

Automated

hypothesis

generation

systems

may

generate

many

potential

hypotheses

and

therefore

some

method

of

evaluating

these

systems

is

necessary

One

way

these

evaluations

have

been

done

is

by

attempting

to

recreate

Swanson

's

discoveries

Gordon

and

Lindsay

were

probably

the

first

to

use

this

approach

followed

a

few

years

later

by

Weeber

et

al.

More

recently

Srinivasan

has

used

this

approach

to

demonstrate

the

feasibility

of

her

approach

based

on

MeSH

terms

and

UMLS

semantic

types

Another

way

that

hypothesis

discovery

systems

are

evaluated

is

by

manually

reviewing

the

literature

supporting

the

extracted

hypothesis

for

scientific

plausibility

and

relevance

This

is

a

natural

next

step

after

reproducing

Swanson

's

discoveries

Using

their

literature-based

scientific

discovery

tool

that

examined

term

co-occurrences

in

MEDLINE

titles

and

abstracts

Weeber

et

al.

found

potential

new

uses

for

thalidomide

Srinivasin

et

al.

continued

to

refine

their

system

discovering

implicit

evidence

of

a

therapeutic

effect

of

Curcuma

longa

-LRB-

turmeric

-RRB-

on

retinal

diseases

Crohn

's

disease

and

spinal

cord

injuries

The

combined

increase

in

scientific

HENRY

STEWART

PUBLICATIONS

B

R

I

E

F

I

N

G

S

I

N

B

I

O

I

N

F

O

R

M

A

T

I

C

S

VOL

NO

MARCH

Cohen

and

Hersh

Hypothesis

discovery

systems

are

not

yet

a

standard

tool

of

biologists

literature

and

genome

expression

data

may

leave

many

scientists

with

the

uneasy

feeling

that

important

discoveries

are

buried

under

the

information

explosion

such

that

computerised

tools

to

help

them

sort

through

the

vast

amount

of

available

information

While

hypothesis

discovery

systems

are

not

yet

a

standard

tool

of

biologists

one

day

they

may

be

Continued

work

is

needed

to

enhance

these

systems

to

handle

the

vast

amounts

of

different

types

of

data

that

scientists

currently

must

explore

manually

Additionally

better

methods

are

needed

to

evaluate

and

compare

the

results

of

these

systems

so

that

improvement

can

be

documented

and

clear

choices

can

be

made

All

these

systems

are

still

in

the

research

and

development

phase

At

this

point

evaluations

tend

to

be

brief

and

these

systems

have

not

been

subjected

to

thorough

user

evaluations

It

remains

to

be

seen

whether

these

systems

will

address

the

needs

of

the

biomedical

research

community

but

it

is

clear

that

they

are

a

step

towards

addressing

the

needs

of

biomedical

researchers

beyond

those

served

by

search

engines

such

as

PubMed

and

Google

CHALLENGES

FUTURE

DIRECTIONS

AND

CONCLUSIONS

From

all

of

the

foregoing

it

is

clear

that

biomedical

text

mining

has

great

potential

However

that

potential

is

yet

unrealised

Text-mining

tools

are

not

part

of

the

standard

arsenal

of

the

biomedical

researcher

in

the

way

that

search

engines

and

sequence

alignment

tools

are

The

major

challenge

for

the

next

years

of

text-mining

work

is

the

creation

of

text-mining

tools

to

provide

a

clear

benefit

to

these

researchers

allowing

them

to

be

more

productive

given

increasing

challenges

due

to

information

growth

The

focus

must

be

more

on

helping

biomedical

researchers

to

solve

real-world

problems

that

are

inhibiting

the

pace

of

research

and

less

on

evaluations

based

on

system

output

independent

of

meeting

user

needs

Advances

on

several

fronts

are

necessary

for

this

to

become

a

reality

First

there

must

be

greater

access

to

full

text

and

test

collections

that

use

it

Much

of

the

scientific

information

contained

in

journal

articles

is

not

present

or

mentioned

in

abstracts

or

MeSH

terms

Text-mining

research

has

recently

been

moving

away

from

abstracts

and

titles

towards

full

text

but

access

to

full

text

is

still

often

limited

by

copyright

restrictions

preventing

work

from

being

done

or

reproduced

The

research

community

must

work

with

publishers

to

make

a

wider

range

of

content

available

to

text-mining

applications

Next

more

work

is

necessary

to

Integration

frameworks

Several

research

groups

are

developing

integrated

text-mining

frameworks

intended

to

be

able

to

address

a

variety

of

user

needs

The

MedScan

system

of

Novichkova

et

al.

combines

lexicons

with

syntactic

and

semantic

templates

into

a

general-purpose

text-mining

system

to

extract

relationships

between

biomedical

entities

Glenisson

et

al.

have

developed

TXTGate

which

performs

gene-based

text

profiling

and

clustering

using

the

information

contained

on

multiple

on-line

biological

databases

Becker

et

al.

created

PubMatrix

a

tool

that

displays

two-dimensional

comparisons

of

gene

names

and

functional

terms

based

on

combining

the

results

of

multiple

queries

to

PubMed

The

BioRAT

system

of

Corney

et

al.

is

another

template-based

system

that

combines

a

template

design

tool

with

a

web

spider

that

locates

and

retrieves

full

text

journal

articles

The

Textpresso

system

of

Muller

et

al.

uses

a

specially

created

ontology

to

flexibly

combine

keyword

and

concept

cooccurrence

searching

of

Caenorhabditis

elegans

-LRB-

a

small

nematode

worm

-RRB-

literature

at

the

sentence

level

Other

generalised

text-mining

frameworks

have

been

reported

by

Nenadic

et

al.

and

Chiang

et

al.

Greater

access

to

full

text

collections

is

essential

to

progress

in

biomedical

text-mining

research

HENRY

STEWART

PUBLICATIONS

B

R

I

E

F

I

N

G

S

I

N

B

I

O

I

N

F

O

R

M

A

T

I

C

S

VOL

NO

MARCH

Current

work

in

biomedical

text

mining

Better

ways

are

needed

to

assess

the

real-world

value

of

text-mining

systems

to

their

intended

users

determine

what

features

and

types

of

features

are

useful

in

addressing

particular

text-mining

tasks

The

feature

space

available

to

text

mining

is

large

and

includes

a

huge

array

of

feature

types

including

-LRB-

but

not

limited

to

-RRB-

words

concepts

headings

formatting

authors

references

and

links

The

bag-of-words

approach

-LRB-

both

stemmed

and

unstemmed

-RRB-

has

been

popular

for

quite

a

long

time

largely

because

it

is

easily

applied

to

text

from

a

large

variety

of

sources

However

this

approach

ignores

document

positional

and

sectional

information

and

may

not

result

in

the

most

discriminating

feature

set

from

fully

marked-up

text

that

provides

this

information

Using

concepts

as

a

basic

unit

instead

of

words

has

been

shown

to

be

and

useful

Including

other

contexts

such

as

the

section

in

which

the

text

occurs

has

also

been

shown

to

help

Full

text

with

XML

mark-up

may

provide

many

more

possible

features

and

feature

types

than

plain

text

The

potential

feature

space

of

XML

full

text

has

only

begun

to

be

explored

With

such

a

wide

range

of

possible

features

and

feature

types

additional

analytical

methods

are

needed

to

determine

the

optimal

feature

set

for

a

particular

application

Text-mining

researchers

also

need

to

better

understand

what

measures

can

be

used

to

assess

value

to

actual

users

and

how

to

tailor

their

algorithms

to

meet

their

needs

It

is

well

known

in

information

retrieval

that

increases

in

precision

or

recall

do

not

necessarily

correlate

with

user

success

in

the

searching

task

As

such

simply

optimising

these

metrics

may

not

result

in

systems

that

meet

users

needs

The

triage

task

of

the

TREC

Genomics

Track

begins

to

address

this

with

an

evaluation

measure

based

on

estimating

the

utility

of

MGI

's

current

triage

process

Other

researchers

have

addressed

this

as

well

In

order

to

design

systems

that

deliver

value

to

biomedical

researchers

much

more

work

is

needed

in

the

area

of

creating

evaluation

metrics

and

methods

that

measure

the

real-world

value

of

text

mining

systems

This

is

an

especially

challenging

problem

for

hypothesis

generation

systems

eg

how

does

one

measure

the

value

of

an

untested

set

of

hypotheses

Nevertheless

robust

evaluation

measures

are

necessary

to

compare

hypothesis

generation

systems

determine

the

best

approaches

and

improve

the

state

of

the

art

of

these

systems

Verifying

that

these

systems

produce

suggestions

that

biomedical

researchers

are

motivated

to

experimentally

test

is

especially

important

Finally

the

approach

of

shared

challenge

tasks

with

consistent

evaluation

based

on

biomedical

domain

expertise

must

continue

More

progress

must

be

made

toward

choosing

tasks

and

evaluating

results

based

on

real-world

needs

Recent

examples

of

this

type

of

cooperation

include

the

BioCreative

workshop

and

the

TREC

Genomics

Track

both

of

which

used

assessments

made

by

biological

database

curators

in

their

normal

workflow

processes

as

the

gold

standard

Clearly

the

main

theme

for

future

progress

is

interdisciplinary

coordination

and

cooperation

Text-mining

researchers

must

work

with

each

other

publishers

and

biomedical

researchers

to

begin

to

meet

user

needs

with

systems

that

produce

consistent

measurable

and

verifiable

results

This

is

an

exciting

time

in

biomedical

text

mining

full

of

promise

Researchers

must

lead

the

coordination

effort

to

realise

the

full

scientific

potential

of

biomedical

text

mining

Annotated

bibliography

Papers

of

particular

interest

published

within

the

period

of

this

review

Papers

of

extreme

interest

published

within

the

period

of

this

review

Yeh

A.

S.

Hirschman

L.

and

Morgan

A.

A.

-LRB-

-RRB-

Evaluation

of

text

data

mining

for

database

curation

Lessons

learned

from

the

KDD

Challenge

Cup

Bioinformatics

Vol

Suppl

pp

Tanabe

L.

and

Wilbur

W.

J.

-LRB-

-RRB-

Tagging

gene

and

protein

names

in

biomedical

text

Bioinformatics

Vol

-LRB-

-RRB-

pp.

Chang

J.

T.

Schutze

H.

and

Altman

R.

B.

HENRY

STEWART

PUBLICATIONS

B

R

I

E

F

I

N

G

S

I

N

B

I

O

I

N

F

O

R

M

A

T

I

C

S

VOL

NO

MARCH

Cohen

and

Hersh

-LRB-

-RRB-

GAPSCORE

Finding

gene

and

protein

names

one

word

at

a

time

Bioinformatics

Vol

-LRB-

-RRB-

pp.

Hirschman

L.

Morgan

A.

A.

and

Yeh

A.

S.

-LRB-

-RRB-

Rutabaga

by

any

other

name

Extracting

biological

names

J.

Biomed

Inform.

Vol

-LRB-

-RRB-

pp.

Tanabe

L.

and

Wilbur

W.

J.

-LRB-

-RRB-

Generation

of

a

large

gene/protein

lexicon

by

morphological

pattern

analysis

J.

Bioinform

Comput

Biol.

Vol

-LRB-

-RRB-

pp.

Schwartz

A.

S.

and

Hearst

M.

A.

-LRB-

-RRB-

A

simple

algorithm

for

identifying

abbreviation

definitions

in

biomedical

text

in

Proceedings

of

the

Pacific

Symposium

on

Biocomputing

January

Hawaii

pp.

Liu

H.

and

Friedman

C.

-LRB-

-RRB-

Mining

terminological

knowledge

in

large

biomedical

corpora

in

Proceedings

of

the

Pacific

Symposium

on

Biocomputing

January

Hawaii

pp.

Gaizauskas

R.

Demetriou

G.

Artymiuk

P.

J.

and

Willett

P.

-LRB-

-RRB-

Protein

structures

and

information

extraction

from

biological

texts

The

PASTA

system

Bioinformatics

Vol

-LRB-

-RRB-

pp.

Friedman

C.

Kra

P.

Yu

H.

et

al.

-LRB-

-RRB-

GENIES

A

natural-language

processing

system

for

the

extraction

of

molecular

pathways

from

journal

articles

Bioinformatics

Vol

Suppl

pp

Chiang

J.

H.

and

Yu

H.

C.

-LRB-

-RRB-

MeKE

Discovering

the

functions

of

gene

products

from

biomedical

literature

via

sentence

alignment

Bioinformatics

Vol

-LRB-

-RRB-

pp.

Eskin

E.

and

Agichtein

E.

-LRB-

-RRB-

Combining

text

mining

and

sequence

analysis

to

discover

protein

functional

regions

in

Proceedings

of

the

Pacific

Symposium

on

Biocomputing

January

Hawaii

pp.

Swanson

D.

R.

-LRB-

-RRB-

Complementary

structures

in

disjoint

science

literatures

in

Proceedings

of

the

Annual

International

ACM

SIGIR

Conference

on

Research

and

Development

in

Information

Retrieval

ACM

Press

Chicago

IL

pp.

Weeber

M.

Vos

R.

Klein

H.

et

al.

-LRB-

-RRB-

Generating

hypotheses

by

discovering

implicit

associations

in

the

literature

A

case

report

of

a

search

for

new

potential

therapeutic

uses

for

thalidomide

J.

Amer

Med

Inform

Assoc.

Vol

-LRB-

-RRB-

pp.

Srinivasan

P.

-LRB-

-RRB-

Text

mining

Generating

hypothesis

from

MEDLINE

J.

Amer

Soc

Inf

Sci

Technol.

Vol

pp.

Srinivasan

P.

Libbus

B.

and

Sehgal

A.

K.

-LRB-

-RRB-

Mining

MEDLINE

Postulating

a

beneficial

role

for

curcumin

longa

in

retinal

diseases

in

BioLINK

Linking

Biological

Literature

Ontologies

and

Databases

Boston

MA

Association

for

Computational

Linguistics

p.

-LRB-

URL

-RRB-

Corney

D.

P.

Buxton

B.

F.

Langdon

W.

B.

and

Jones

D.

T.

-LRB-

-RRB-

BioRAT

Extracting

biological

information

from

fulllength

papers

Bioinformatics

-LRB-

in

press

-RRB-

Aronson

A.

R.

-LRB-

-RRB-

Effective

mapping

of

biomedical

text

to

the

UMLS

Metathesaurus

the

MetaMap

program

in

Proceedings

of

the

AMIA

Symposium

November

Washington

DC

pp.

Muller

H.

Kenny

E.

E.

and

Sternberg

P.

W.

-LRB-

-RRB-

Textpresso

An

ontology-based

information

retrieval

and

extraction

system

for

biological

literature

PLoS

Biol.

Vol

-LRB-

-RRB-

Donaldson

I.

Martin

J.

de

Bruijn

B.

et

al.

-LRB-

-RRB-

PreBIND

and

Textomy

mining

the

biomedical

literature

for

protein

protein

interactions

using

a

support

vector

machine

BMC

Bioinformatics

Vol

-LRB-

-RRB-

p.

Dobrokhotov

P.

B.

Goutte

C.

Veuthey

A.

and

Gaussier

E.

-LRB-

-RRB-

Combining

NLP

and

probabilistic

categorisation

of

document

and

term

selection

for

Swiss-Prot

medical

annotation

Bioinformatics

Vol

Suppl

pp

Oregon

Health

Science

University

-LRB-

-RRB-

TREC

Genomics

Track

Protocol

-LRB-

URL

accessed

August

-RRB-

The

Human

Genome

Organisation

-LRB-

-RRB-

HUGO

Gene

Nomenclature

Committee

-LRB-

URL

nomenclature

accessed

September

-RRB-

Yu

H.

and

Agichtein

E.

-LRB-

-RRB-

Extracting

synonymous

gene

and

protein

terms

from

biological

literature

Bioinformatics

Vol

Suppl

pp

Chang

J.

T.

Schutze

H.

and

Altman

R.

B.

-LRB-

-RRB-

Creating

an

online

dictionary

of

abbreviations

from

MEDLINE

J.

Amer

Med

Inform

Assoc.

Vol

-LRB-

-RRB-

pp.

References

Hersh

W.

R.

-LRB-

-RRB-

Information

Retrieval

A

Health

and

Biomedical

Perspective

edn

Springer

New

York

Mitchell

J.

A.

Aronson

A.

R.

Mork

J.

G.

et

al.

-LRB-

-RRB-

Gene

indexing

Characterization

and

analysis

of

NLM

's

GeneRIFs

in

Proceedings

of

the

AMIA

Symposium

November

Washington

DC

pp.

Weeber

M.

Klein

H.

Aronson

A.

R.

et

al.

HENRY

STEWART

PUBLICATIONS

B

R

I

E

F

I

N

G

S

I

N

B

I

O

I

N

F

O

R

M

A

T

I

C

S

VOL

NO

MARCH

Current

work

in

biomedical

text

mining

-LRB-

-RRB-

Text-based

discovery

in

biomedicine

The

architecture

of

the

DAD-system

in

Proc

AMIA

Symposium

November

Los

Angeles

CA

pp.

Kostoff

R.

N.

Block

J.

A.

Stump

J.

A.

and

Pfeil

K.

M.

-LRB-

-RRB-

Information

content

in

Medline

record

fields

Int

J.

Med

Inf.

Vol

-LRB-

-RRB-

pp.

Hersh

W.

Bhupatiraju

R.

T.

Oregon

Health

Science

University

-LRB-

-RRB-

TREC

Genomics

Track

Overview

-LRB-

URL

overview.pdf

accessed

September

-RRB-

Yeh

A.

S.

Hirschman

L.

and

Morgan

A.

A.

-LRB-

-RRB-

Evaluation

of

text

data

mining

for

database

curation

Lessons

learned

from

the

KDD

Challenge

Cup

Bioinformatics

Vol

Suppl

pp

Lindsay

R.

K.

and

Gordon

M.

D.

-LRB-

-RRB-

Literature-based

discovery

by

lexical

statistics

J.

Amer

Soc

Information

Sci.

Vol

-LRB-

-RRB-

pp.

Swanson

D.

R.

-LRB-

-RRB-

Medical

literature

as

a

potential

source

of

new

knowledge

Bull

Med

Libr

Assoc.

Vol

-LRB-

-RRB-

pp.

de

Bruijn

B.

and

Martin

J.

-LRB-

-RRB-

Getting

to

the

-LRB-

c

-RRB-

ore

of

knowledge

Mining

biomedical

literature

Int

J.

Med

Inf.

Vol

-LRB-

-RRB-

pp.

without

natural

language

processing

in

Computational

Linguistics

and

Intelligent

Text

Processing

Proceedings

Lecture

Notes

in

Computer

Science

Vol

Gelbukh

A.

F.

Ed

Springer

pp.

Brill

E.

and

Mooney

R.

J.

-LRB-

-RRB-

An

overview

of

empirical

natural

language

processing

AI

Magazine

Vol

-LRB-

-RRB-

pp.

Chang

J.

T.

Schutze

H.

and

Altman

R.

B.

-LRB-

-RRB-

GAPSCORE

Finding

gene

and

protein

names

one

word

at

a

time

Bioinformatics

Vol

-LRB-

-RRB-

pp.

Franzen

K.

Eriksson

G.

Olsson

F.

et

al.

-LRB-

-RRB-

Protein

names

and

how

to

find

them

Int

J.

Med

Inf.

Vol

-LRB-

-RRB-

pp.

Zhou

G.

Zhang

J.

Su

J.

et

al.

-LRB-

-RRB-

Recognizing

names

in

biomedical

texts

A

machine

learning

approach

Bioinformatics

Vol

-LRB-

-RRB-

pp.

Kim

J.

D.

Ohta

T.

Tateisi

Y.

and

Tsujii

J.

-LRB-

-RRB-

GENIA

corpus

a

semantically

annotated

corpus

for

bio-textmining

Bioinformatics

Vol

Suppl

pp

Narayanaswamy

M.

Ravikumar

K.

E.

and

Vijay-Shanker

K.

-LRB-

-RRB-

A

biological

named

entity

recognizer

in

Proceedings

of

the

Pacific

Symposium

on

Biocomputing

January

Hawaii

pp.

Settles

B.

-LRB-

-RRB-

Biomedical

named

entity

recognition

using

conditional

random

fields

and

rich

feature

sets

in

Proceedings

of

the

International

Joint

Workshop

on

Natural

Language

Processing

in

Biomedicine

and

its

Applications

-LRB-

NLPBA

-RRB-

Geneva

Switzerland

Mika

S.

and

Rost

B.

-LRB-

-RRB-

Protein

names

precisely

peeled

off

free

text

Bioinformatics

Vol

Suppl

pp

Chen

L.

and

Friedman

C.

-LRB-

-RRB-

Extracting

phenotypic

information

from

the

literature

via

natural

language

processing

in

Proceedings

of

the

World

Congress

on

Medical

Informatics

IMIA

San

Francisco

CA

pp.

Tanabe

L.

and

Wilbur

W.

J.

-LRB-

-RRB-

Generation

of

a

large

gene/protein

lexicon

by

morphological

pattern

analysis

J.

Bioinform

Comput

Biol.

Vol

-LRB-

-RRB-

pp.

Wren

J.

D.

and

Garner

H.

R.

-LRB-

-RRB-

Shared

relationship

analysis

Ranking

set

cohesion

and

commonalities

within

a

literature-derived

relationship

network

Bioinformatics

Vol

-LRB-

-RRB-

pp.

Schwartz

A.

S.

and

Hearst

M.

A.

-LRB-

-RRB-

A

simple

algorithm

for

identifying

abbreviation

definitions

in

biomedical

text

in

Proceedings

of

the

Pacific

Symposium

on

Biocomputing

January

Hawaii

pp.

McDonald

D.

M.

Chen

H.

Su

H.

and

Marshall

B.

B.

-LRB-

-RRB-

Extracting

gene

Hanisch

D.

Fluck

J.

Mevissen

H.

T.

and

Zimmer

R.

-LRB-

-RRB-

Playing

biology

's

name

game

Identifying

protein

names

in

scientific

text

in

Proceedings

of

the

Pacific

Symposium

on

Biocomputing

January

Hawaii

pp.

Brill

E.

-LRB-

-RRB-

Transformation-based

errordriven

learning

and

natural

language

processing

A

case

study

in

part-of-speech

tagging

Comput

Linguistics

Vol

-LRB-

-RRB-

pp.

Hirschman

L.

Morgan

A.

A.

and

Yeh

A.

S.

-LRB-

-RRB-

Rutabaga

by

any

other

name

Extracting

biological

names

J.

Biomed

Inform.

Vol

-LRB-

-RRB-

pp.

Tanabe

L.

and

Wilbur

W.

J.

-LRB-

-RRB-

Tagging

gene

and

protein

names

in

biomedical

text

Bioinformatics

Vol

-LRB-

-RRB-

pp.

Yu

H.

Hatzivassiloglou

V.

Friedman

C.

et

al.

-LRB-

-RRB-

Automatic

extraction

of

gene

and

protein

synonyms

from

MEDLINE

and

journal

articles

in

Proceedings

of

the

AMIA

Symposium

November

San

Antonio

TX

pp.

Blaschke

C.

-LRB-

-RRB-

Biocreative

Critical

Assessment

for

Information

Extraction

in

Biology

Granada

Spain

-LRB-

URL

http

handout

index.html

-RRB-

Brill

E.

-LRB-

-RRB-

Processing

natural

language

HENRY

STEWART

PUBLICATIONS

B

R

I

E

F

I

N

G

S

I

N

B

I

O

I

N

F

O

R

M

A

T

I

C

S

VOL

NO

MARCH

Cohen

and

Hersh

pathway

relations

using

a

hybrid

grammar

The

Arizona

relation

parser

Bioinformatics

-LRB-

in

press

-RRB-

Gaizauskas

R.

Demetriou

G.

Artymiuk

P.

J.

and

Willett

P.

-LRB-

-RRB-

Protein

structures

and

information

extraction

from

biological

texts

The

PASTA

system

Bioinformatics

Vol

-LRB-

-RRB-

pp.

Friedman

C.

Kra

P.

Yu

H.

et

al.

-LRB-

-RRB-

GENIES

A

natural-language

processing

system

for

the

extraction

of

molecular

pathways

from

journal

articles

Bioinformatics

Vol

Suppl

pp

Liu

H.

and

Friedman

C.

-LRB-

-RRB-

Mining

terminological

knowledge

in

large

biomedical

corpora

in

Proceedings

of

the

Pacific

Symposium

on

Biocomputing

January

Hawaii

pp.

Regev

Y.

Finkelstein-Landau

M.

and

Feldman

R.

-LRB-

-RRB-

Rule-based

extraction

of

experimental

evidence

in

the

biomedical

domain

The

KDD

Cup

-LRB-

task

-RRB-

ACM

SIGKDD

Explorations

Newsletter

Vol

-LRB-

-RRB-

pp.

Shi

M.

Edwin

D.

S.

Menon

R.

et

al.

-LRB-

-RRB-

A

machine

learning

approach

for

the

curation

of

biomedical

literature-KDD

Cup

-LRB-

task

-RRB-

ACM

SIGKDD

Explorations

Newsletter

Vol

-LRB-

-RRB-

pp.

Ghanem

M.

M.

Guo

Y.

Lodhi

H.

and

Zhang

Y.

-LRB-

-RRB-

Automatic

scientific

text

classification

using

local

patterns

KDD

Cup

-LRB-

task

-RRB-

ACM

SIGKDD

Explorations

Newsletter

Vol

-LRB-

-RRB-

pp.

Donaldson

I.

Martin

J.

de

Bruijn

B.

et

al.

-LRB-

-RRB-

PreBIND

and

Textomy

mining

the

biomedical

literature

for

protein

protein

interactions

using

a

support

vector

machine

BMC

Bioinformatics

Vol

-LRB-

-RRB-

p.

Dobrokhotov

P.

B.

Goutte

C.

Veuthey

A.

and

Gaussier

E.

-LRB-

-RRB-

Combining

NLP

and

probabilistic

categorisation

of

document

and

term

selection

for

Swiss-Prot

medical

annotation

Bioinformatics

Vol

Suppl

pp

Liu

F.

Jenssen

T.

K.

Nygaard

V.

et

al.

-LRB-

-RRB-

FigSearch

A

figure

legend

indexing

and

classification

system

Bioinformatics

Vol

pp.

Hersh

W.R.

and

Oregon

Health

Science

University

-LRB-

-RRB-

TREC

Genomics

Track

Protocol

-LRB-

URL

accessed

August

-RRB-

Chiang

J.

H.

Yu

H.

C.

and

Hsu

H.

J.

-LRB-

-RRB-

GIS

A

biomedical

text-mining

system

for

gene

information

discovery

Bioinformatics

Vol

-LRB-

-RRB-

pp.

Albert

S.

Gaudan

S.

Knigge

H.

et

al.

-LRB-

-RRB-

Computer-assisted

generation

of

a

protein-interaction

database

for

nuclear

receptors

Mol

Endocrinol.

Vol

-LRB-

-RRB-

pp.

Editorial

-LRB-

-RRB-

HUGO

a

UN

for

the

human

genome

Nat

Genet.

Vol

-LRB-

-RRB-

pp.

The

Human

Genome

Organisation

HUGO

Gene

Nomenclature

Committee

-LRB-

URL

accessed

September

-RRB-

Yu

H.

and

Agichtein

E.

-LRB-

-RRB-

Extracting

synonymous

gene

and

protein

terms

from

biological

literature

Bioinformatics

Vol

Suppl

pp

Cohen

A.

M.

-LRB-

-RRB-

Using

symbolic

network

logical

analysis

as

a

knowledge

extraction

method

on

MEDLINE

abstracts

BMC

Bioinformatics

-LRB-

in

press

-RRB-

Yu

H.

Hripcsak

G.

and

Friedman

C.

-LRB-

-RRB-

Mapping

abbreviations

to

full

forms

in

biomedical

articles

J.

Amer

Med

Inform

Assoc.

Vol

-LRB-

-RRB-

pp.

Chang

J.

T.

Schutze

H.

and

Altman

R.

B.

-LRB-

-RRB-

Creating

an

online

dictionary

of

abbreviations

from

MEDLINE

J.

Amer

Med

Inform

Assoc.

Vol

-LRB-

-RRB-

pp.

Brandeis

University

Medstract

Project

Initial

Annotion

Corpora

-LRB-

-RRB-

-LRB-

URL

accessed

June

-RRB-

Raychaudhuri

S.

Schutze

H.

and

Altman

R.

B.

-LRB-

-RRB-

Using

text

analysis

to

identify

functionally

coherent

gene

groups

Genome

Res.

Vol

-LRB-

-RRB-

pp.

Raychaudhuri

S.

and

Altman

R.

B.

-LRB-

-RRB-

A

literature-based

method

for

assessing

the

functional

coherence

of

a

gene

group

Bioinformatics

Vol

-LRB-

-RRB-

pp.

Glenisson

P.

Antal

P.

Mathys

J.

et

al.

-LRB-

-RRB-

Evaluation

of

the

vector

space

representation

in

text-based

gene

clustering

in

Proceedings

of

the

Pacific

Symposium

on

Biocomputing

January

Hawaii

pp.

Chiang

J.

H.

and

Yu

H.

C.

-LRB-

-RRB-

MeKE

Discovering

the

functions

of

gene

products

from

biomedical

literature

via

sentence

alignment

Bioinformatics

Vol

-LRB-

-RRB-

pp.

Raychaudhuri

S.

Chang

J.

T.

Sutphin

P.

D.

and

Altman

R.

B.

-LRB-

-RRB-

Associating

genes

with

gene

ontology

codes

using

a

maximum

entropy

analysis

of

biomedical

literature

Genome

Res.

Vol

-LRB-

-RRB-

pp.

Pan

H.

Zuo

L.

Choudhary

V.

et

al.

-LRB-

-RRB-

Dragon

TF

Association

Miner

A

system

for

exploring

transcription

factor

associations

through

text-mining

Nucleic

Acids

Res.

Vol

-LRB-

Web

Server

issue

-RRB-

pp

Krallinger

M.

-LRB-

-RRB-

BioCreAtIvE

critical

assessment

of

information

extraction

systems

in

HENRY

STEWART

PUBLICATIONS

B

R

I

E

F

I

N

G

S

I

N

B

I

O

I

N

F

O

R

M

A

T

I

C

S

VOL

NO

MARCH

Current

work

in

biomedical

text

mining

biology

-LRB-

URL

BioLINK/BioCreative.eval.html

accessed

September

-RRB-

Eskin

E.

and

Agichtein

E.

-LRB-

-RRB-

Combining

text

mining

and

sequence

analysis

to

discover

protein

functional

regions

in

Proceedings

of

the

Pacific

Symposium

on

Biocomputing

January

Hawaii

pp.

Srinivasan

P.

and

Wedemeyer

M.

-LRB-

-RRB-

Mining

concept

profiles

with

the

vector

model

or

where

on

earth

are

diseases

being

studied

in

Proceedings

of

the

Text

Mining

Workshop

Third

SIAM

International

Conference

on

Data

Mining

San

Francisco

Kostoff

R.

N.

-LRB-

-RRB-

Bilateral

asymmetry

prediction

Med

Hypotheses

Vol

-LRB-

-RRB-

pp.

Xu

H.

Anderson

K.

Grann

V.

and

Friedman

C.

-LRB-

-RRB-

Facilitating

cancer

research

using

natural

language

processing

of

pathology

reports

in

Proceedings

of

the

World

Congress

on

Medical

Informatics

IMIA

San

Francisco

CA

pp.

Swanson

D.R.

-LRB-

-RRB-

Complementary

structures

in

disjoint

science

literatures

in

Proceedings

of

the

Annual

International

ACM

SIGIR

Conference

on

Research

and

Development

in

Information

Retrieval

ACM

Press

Chicago

IL

pp.

Weeber

M.

Vos

R.

Klein

H.

et

al.

-LRB-

-RRB-

Generating

hypotheses

by

discovering

implicit

associations

in

the

literature

A

case

report

of

a

search

for

new

potential

therapeutic

uses

for

thalidomide

J.

Amer

Med

Inform

Assoc.

Vol

-LRB-

-RRB-

pp.

Swanson

D.

R.

-LRB-

-RRB-

Fish

oil

Raynaud

's

syndrome

and

undiscovered

public

knowledge

Perspect

Biol

Med.

Vol

-LRB-

-RRB-

pp.

Ramadan

N.

M.

Halvorson

H.

Vande-Linde

A.

et

al.

-LRB-

-RRB-

Low

brain

magnesium

in

migraine

Headache

Vol

-LRB-

-RRB-

pp.

Ferrari

M.

D.

-LRB-

-RRB-

Biochemistry

of

migraine

Pathol

Biol

-LRB-

Paris

-RRB-

Vol

-LRB-

-RRB-

pp.

Gordon

M.

D.

and

Lindsay

R.

K.

-LRB-

-RRB-

Toward

discovery

support

systems

A

replication

re-examination

and

extension

of

Swanson

's

work

on

literature-based

discovery

of

a

connection

between

Raynaud

's

and

fish

oil

J.

Amer

Soc

Inf

Sci.

Vol

-LRB-

-RRB-

pp.

Srinivasan

P.

-LRB-

-RRB-

Text

mining

Generating

hypothesis

from

MEDLINE

J.

Amer

Soc

Inf

Sci

Technol.

Vol

pp.

Srinivasan

P.

and

Libbus

B.

-LRB-

-RRB-

Mining

MEDLINE

for

implicit

links

between

dietary

substances

and

diseases

Bioinformatics

Vol

Suppl

pp

Srinivasan

P.

Libbus

B.

and

Sehgal

A.

K.

-LRB-

-RRB-

Mining

MEDLINE

Postulating

a

beneficial

role

for

curcumin

longa

in

retinal

diseases

in

BioLINK

Linking

Biological

Literature

Ontologies

and

Databases

Boston

MA

Association

for

Computational

Linguistics

pp.

-LRB-

URL

http

-RRB-

Novichkova

S.

Egorov

S.

and

Daraselia

N.

-LRB-

-RRB-

MedScan

a

natural

language

processing

engine

for

MEDLINE

abstracts

Bioinformatics

Vol

-LRB-

-RRB-

pp.

Glenisson

P.

Coessens

B.

Van

Vooren

S.

et

al.

-LRB-

-RRB-

TXTGate

Profiling

gene

groups

with

text-based

information

Genome

Biol.

Vol

-LRB-

-RRB-

p.

Becker

K.

G.

Hosack

D.

A.

Dennis

G.

Jr

et

al.

-LRB-

-RRB-

PubMatrix

A

tool

for

multiplex

literature

mining

BMC

Bioinformatics

Vol

-LRB-

-RRB-

p.

Corney

D.

P.

Buxton

B.

F.

Langdon

W.

B.

and

Jones

D.

T.

-LRB-

-RRB-

BioRAT

Extracting

biological

information

from

full-length

papers

Bioinformatics

-LRB-

in

press

-RRB-

Muller

H.

Kenny

E.

E.

and

Sternberg

P.

W.

-LRB-

-RRB-

Textpresso

An

ontology-based

information

retrieval

and

extraction

system

for

biological

literature

PLoS

Biol.

Vol

-LRB-

-RRB-

Nenadic

G.

Spasic

I.

and

Ananiadou

S.

-LRB-

-RRB-

Terminology-driven

mining

of

biomedical

literature

Bioinformatics

Vol

-LRB-

-RRB-

pp.

Aronson

A.

R.

-LRB-

-RRB-

Effective

mapping

of

biomedical

text

to

the

UMLS

Metathesaurus

The

MetaMap

program

in

Proc

AMIA

Symp

November

Washington

DC

pp.

Rindflesch

T.

C.

Hunter

L.

and

Aronson

A.

R.

-LRB-

-RRB-

Mining

molecular

binding

terminology

from

biomedical

text

in

Proc

AMIA

Symp

November

Washington

DC

pp.

Majoros

W.

H.

Subramanian

G.

M.

and

Yandell

M.

D.

-LRB-

-RRB-

Identification

of

key

concepts

in

biomedical

literature

using

a

modified

Markov

heuristic

Bioinformatics

Vol

-LRB-

-RRB-

pp.

Regev

Y.

Finkelstein-Landau

M.

and

Feldman

R.

-LRB-

-RRB-

Rule-based

extraction

of

experimental

evidence

in

the

biomedical

domain

The

KDD

Cup

-LRB-

task

-RRB-

ACM

SIGKDD

Explorations

Newsletter

Vol

-LRB-

-RRB-

pp.

Hersh

W.

R.

Crabtree

M.

K.

Hickam

D.

H.

et

al.

-LRB-

-RRB-

Factors

associated

with

success

in

searching

MEDLINE

and

applying

evidence

to

answer

clinical

questions

J.

Amer

Med

Inform

Assoc.

Vol

-LRB-

-RRB-

pp.

HENRY

STEWART

PUBLICATIONS

B

R

I

E

F

I

N

G

S

I

N

B

I

O

I

N

F

O

R

M

A

T

I

C

S

VOL

NO

MARCH