Dependency parsing for interaction detection in pharmacogenomics

We give an overview of our approach to the extraction of interactions between pharmacogenomic entities like drugs, genes and diseases and suggest classes of interaction types driven by data from PharmGKB and partly following the top level ontology WordNet and biomedical types from BioNLP. Our text mining approach to the extraction of interactions is based on syntactic analysis. We use syntactic analyses to explore domain events and to suggest a set of interaction labels for the pharmacogenomics domain.


Introduction
Pharmacogenomics is the discipline which studies the mutual interactions among drugs, genes, diseases, in particular in relation to specific individual mutations, which can affect the reactions to drugs and the susceptibility to diseases. One important database that aims at providing a reference repository for such information is PharmGKB (Sangkuhl et al., 2008). The information contained in PharmGKB is obtained from a combination of submitted experimental results and literature curation. Literature curation is the knowledge-intensive process which aims at extracting from the primary literature (scientific publications) the most relevant results obtained by the authors in their scientific experiments. Despite some support by text mining tools, it is still the case that the process of curation involves extensive human intervention, which is time consuming and expensive. In this paper we describe research conducted by the On-toGene group within the scope of the SASEBio project (Semi-Automated Semantic Enrichment of the Biomedical Literature 1 ), which aims at producing novel efficient text mining tools which provide better support for the process of biomedical literature curation. In particular we have recently used the PharmGKB database in order to derive interaction indicators from the literature. The OntoGene research group has participated in several text mining shared tasks, such as BioCreative (Rinaldi et al., 2008;Rinaldi et al., 2010b;Schneider et al., 2011), CALBC (Rinaldi et al., 2010a) and BioNLP , which present structural similarities with the extraction of interactions in the pharmacogenomics domain, as we discuss later. We describe applications of the text mining technologies developed for the problem of finding head words (so called "triggers") and categorize entity interactions into classes relevant to the pharmacogenomics domain.

The OntoGene text mining system
Biomedical researchers studying various biological processes need to find supporting evidence for specific relationships among entities of interest, such as protein-protein 1 http://www.sasebio.org/ interactions, or influence of genes on specific diseases. These activities can profit from text mining systems, which not only can find relevant publications, but also deliver small passages describing the interactions that they need. This capability obviates the need to read entire documents, and allows researchers to find answers to their questions more quickly. Many interaction detection approaches in the pharmacogenomics domain use untyped interactions, or the labels mirror the types of the participants such as drug or disease. Often, interactions could be classed into meaningful types. For example, proteins and genes, may bind, block, inhibit etc. This need is recognized by some of the biomedical text mining competitions, for example BioNLP (Cohen et al., 2009), which uses classes of interactions, and finding the interaction class label is an integral part of the competition. Intuitively, interactions between other biomedical entities also fall into clearly distinguishable classes. For example, a certain gene can increase the risk for a disease, a certain drug can inhibit a gene, or have a healing effect on a disease, or have side-effects. Most approaches going beyond gene and protein interactions use unlabelled interactions, or at best the interaction type follows deterministically from the involved entitities. We believe that a more detailed inventory of classes is beneficial and feasible.

Our syntax-based approach
Approaches towards identification of entity interactions based on their coccurrence in a given text span are quite common (e.g. (Rebholz-Schuhmann et al., 2006)). Some approaches apply handcrafted rules, for example regular expressions for surface searches (Giuliano et al., 2006), or syntactic patterns on automatically parsed corpora (Rinaldi et al., 2006;Fundel et al., 2007a). These approaches typically achieve high precision at the cost of recall. There have recently been numerous publications showing the potential of dependency-based language analysis for text mining (e.g. (Clegg and Shepherd, 2007;Fundel et al., 2007b)). (Pyysalo et al., 2007) describes a manually annotated corpus which includes a dependency based analysis of each sentence. (Clegg and Shepherd, 2007) uses dependency graphs in order to benchmark four publicly available Figure 1: Simplified internal syntactic representation of the sentence "The neuronal nicotinic acetylcholine receptor alpha7 (nAChR alpha7) may be involved in cognitive deficits in Schizophrenia and Alzheimer's disease." from PubMed abstract 15695160. The curved arrows and dark red notes are aimed at illustrating the path features.
natural-language parsers. (Fundel et al., 2007b) describes a large-scale relation mining application using the Stanford Lexicalized Parser. These approaches can be further enhanced using machine learning methods, by extracting meaningful features from the dependency parse trees and from other intermediate stages of processing (e.g. (Erkan et al., 2007;Kim et al., 2008;Van Landeghem et al., 2008)). In previous work, we used manually-constructed syntactic patterns in order to filter candidate protein-protein interactions (Rinaldi et al., 2007;Rinaldi et al., 2008). This approach was later enhanced with automatic learning of useful syntactic configuration from a training corpus Rinaldi et al., 2010b). In the following we describe how such an approach has been adapted to Phar-mGKB. We have parsed all sentences in the PharmGKB gold standard with our own dependency parser (Schneider, 2008). Entities are recognized and disambiguated using the Onto-Gene pipeline. All entities that appear in the same sentence are potentially interacting, so we record the syntactic path that connects them as candidate path. If the gold standard contains the information that these two entities really interact, then we mark the path that connects them as relevant path. The calculation of candidate path divided by relevant path gives us the Maximum-Likelihood probability that a path is relevant: The most frequent path types in the training set are given in table 2. The third line, where the head word is effect, for example, has a modification by an of-PP to one of the entities in the relation, and a nested on-PP and of-PP modification. It covers patterns like the effect of X on the increase of Y or no effect of X on the development of Y, where X and Y are domain entities like drug, disease and protein. The most frequent dependency types of the Pro3Gres parser are given in table 1. The dependency set is closely related to GREVAL (Carroll et al., 2003), to which we have mapped for evalutations (Schneider, 2008). It can also be mapped to the Stanford scheme (Haverinen et al., 2008 directly during the application phase: whenever two entities occurring in the same sentence of the application corpus, for example a drug and a disease, have a probability of being relevant above a certain threshold, the systems reports the interaction. p(relevant) can then be interpreted as the potential precision of such direct application. As syntactic path, we record the dependency labels that connect the two entities, and the topmost word connecting them. A sample path is provided in Figure 1. Such a direct application, however, suffers from sparse data problems. If possible, we use a single feature for the entire path. In the majority of cases, we need to split the path into two halves: from the top-word down to one of the entities as feature 1, and from the top-word down the other entity as feature 2.
We also use lexical information on transparent words (Meyers et al., 1998) to avoid data sparseness, as follows: • First, entities occurring inside noun chunks are allowed to replace the head of the chunk if the head is a transparent word.
• Second (if still no relevant path exists), the relations for appositions, conjunctions and hyphens are cut.
• Third (if still no relevant path exists), parts of trees which are headed by a transparent word are cut. analyze subj modpp-in pobj-in modpart pobj-with 14 14 Table 2: Some of the most frequent path types in the training set A transparent word is a word that does not affect the meaning of sentence fundamentally if it is left out. For example, if drug A affects groups of patients then the sentence drug A affects patients, which does not contain the transparent word group, has a very similar meaning. We have learnt transparent words using a machine learning approach: words that occur particularly often inside paths are regarded as transparent . Three additional factors are used to calculate a score. First f (c 1 ), the frequency of the entities in the document, as the most relevant entities in the given document are typically mentioned several times. Reporting interactions based on the frequencies of entities leads to a very high baseline in protein-protein interaction (Rinaldi et al., 2010b). Second f (c 2 ), the probability of the entity types to enter interactions is used. For example, the probability that a drug and a disease in the same sentence have an interaction is relatively high (about 12%), while the probability that two drugs appearing in the same sentence interact is low (about 1%). Third, we use a simple zoning factor: the title is given ten time the weight of the rest of the text. A score is assigned to every candidate interaction according to the following formula: This score is based on probabilites, but it does not express a probability. It can be used for ranking candidates, report those above a threshold and use it as confidence measure, for example for ranking different interactions that may be expressed in the sentence. Our syntax-based approach in its current version only has two backoffs: it splits the path into a left and right half, and transparent words are filtered. It can reach relatively high precision at the cost of low recall. The path contains important information on the type of interaction as we discuss in section 3.

Evaluation
We have applied our approach to a manually verified test set from the pharmacogenomics domain. In collaboration with PharmGKB we conducted a separate experiment to test the usefulness of our text mining technologies and curation interface for a simple revalidation experiment which is described in detail in (Rinaldi et al., 2012). This experiment produced abstracts where all interactions have been reliably curated by PharmGKB domain experts. We have used 75 of these documents as a test corpus, and the rest of the Phar-mGKB dataset was used for training purposes, excluding also all documents that contain more than 20 interactions. Evaluation results are given in table 3. The method syn is purely our syntactic method, as described in section 2.1. We see that it has higher precision than recall. Recall can be increased by including sentencecoocurrence, which the method syn+cooc does. We can see on the one hand that recall increases at the cost of precision, on the other hand that it is still below 50%, which indicates that many interactions are expressed across several sentences. The method syn+cooc2 extends the sentencecoocurrence score to including the neighbouring sentence. The increase in recall indicates that context of more than one sentence is often necessary. The method syn+cooc2w weighs the sentence-coocurrence score by distance, giving higher scores to entities that appear closer. The method syn+cooc2wf is identical but does not use a score threshold, thus returning all results, which increases recall and reduces precision. It aims to give an upper bound on recall. Results using only the first n reported hits are also given. The method syn+cooc2wb is identical to syn+cooc2wf but uses a relatively high score threshold aiming for a precision/recall balanced output. In addition to being a useful component of an interaction detection approach, the syntactic approach detects the lexemes appearing at the top of the syntactic path, as we discuss now.

Data-driven exploration of trigger words
The most frequent true positive types are given in table 2, broken down by left-path, right-path and top word, i.e. the word at which the two paths from the entities up to the root node meet. The top word is often the keyword expressing that an interaction takes place, the so-called trigger word. The counts are sorted by inverse frequency, the most frequent path type has 53 instances. Path1 is the half from the top word (Head) of the path to the first entity, path2 the half to the second entity. The last column lists how often the path occurs in the entire training corpus, irrespective whether it expresses relevant interactions or not, which we refer to as candidate path. The probability p(relevant|candidate path), which is the main factor in the syntactic feature, is given in the first column. We can  Table 3: Evaluation of 75 manually annotated documents. The first column gives the approach used. The second column reports the number of documents with a least one response hit. The third to the fifth column give true positives (TP), false positives (FP) and false negatives (FN). The sixth column contains the macro averaged AUCiP/R. The seventh column contains the cut-off value n used by the BioCreative evaluation tool as a threshold on the number of response hits when computing these results. In rows with n = all no threshold was applied. The eighth column reports macro precision, the ninth macro recall.  Table 4: Syntactic paths with high probability of expressing an interaction see, for example, that the verb be is generally unlikely to head a relevant path, while cause, association, associate, and analyze have much higher probabilities. Also obvious, short and easily interpretable paths such as the first one of table 2 ("X associates with Y") only have relatively low chances of expressing relevant entities, which indicates that naive implementations of the syntactic feature would have low precision. The very specific and long path in the last row always expresses a relevant interaction. There are 15 paths occurring more than 3 times which have a 100% probability.
A benefit of the syntactic approach is that it detects the lexemes appearing at the top of the path (column 'Head' in the tables), which can be used as keywords for other approaches and may also help to distinguish interaction classes. All paths that are not cases of self-reference and are relevant with at least 60% are given in table 4. 2 Except for be in a very specific configuration, all head words in table 4 are good keyword candidates. In the case of be the words inside the path often contain interaction type information. In table 5 we see, for example, that there are 30 cases in which a drug is an inhibitor of a gene. As classes for gene and protein interactions have already been suggested, we restricted the interactions in table specifically to drugs, genes, and/or diseases. Figure 1 portrays a gold standard interaction which correspond to the fifth row in  leads via apposition and subject relation to the verb 'involve'. The apposition relation is semantically void and thus gets cut. Path2 is up from 'Schizophrenia' via the relations modpp-in and pobj-in to 'involve', which is suggested as the head because the paths meet here. Head words like 'involve' are quite unspecific, the type of interaction is left underspecified. The verb group (may be involved) clarifies, however, that the class of interaction is the subject of the investigation. A possible interaction could thus be 'speculation'. The top rows in table 2 (associate, effect, association) are also unspecific. Looking at the data, however, reveals that the article context specifies the role in the vast majority of cases, although often outside the clause containing the interaction type. The first three instances of the top row, for example, are: (1) " In conclusion, our data suggest that the TT MTHFR 677 genotype is associated with marked MTX -induced hyperhomocysteinemia ... "; (2) " In cell-based , transactivation assays , OATP-C expression was associated with increased cellular rifampin retention ... "; (3) " The objective of this study was to evaluate whether the MDR1 exon21 and exon26 polymorphisms and the CYP3A5 polymorphism are associated with tacrolimus disposition ... "; Markers pointing to specific interpretations are given in boldface. The sentences indicate that specific interpretations such as 'increase' (example 2), 'decrease' or 'speculation' (examples 1 and 3) are often intended, but the task of detecting them can be demanding. In cases where no specific interpretation marker exists, the default for associate is usually 'increase'. Looking again at the data, the first instance of the second row of table 2 illustrates this.
(4) " A coding polymorphism in the receptor with reduced affinity to LTD4 is associated with asthma. "; The fact that the association with asthma is positive is not specified, but the negative affinity (BioNLP class binding) is explicitly marked. 'increase' marks a positive association, 'decrease' a negative association.
Sometimes, it is explicitly underspecified whether an association is positive or negative, as in the following example. The polarity of the association is not mentioned, the polarity of the expression is explicitly underspecified.
(5) " Some genetic studies have found that C -to-T singlenucleotide polymorphism ( C-509T ) in the TGF-beta1 gene promoter may be associated with altered gene expression and asthma phenotype . "; Some of the specific roles directly appear as the head word, for example inhibit, cause, increase, treat, risk in table 5.
Cause and increase can be seen as positive association, inhibit as negative association, and risk as speculative association. A head word like 'treat' could even be seen as speculative positive association (because it is hoped that the patient's health will improve), but it is ontologically difficult to assess how far one can subsume events under the same label.

Event Ontologies
Research on semantic primitives (Wierzbicka, 1996) is often contested (Goddard, 1998)   for example express and localize are not present in Word-Net in their biomedical sense. File 36 contains verbs like cause and make, and is also an important concept in the biomedical domain. The senses given for associate in WordNet are on the one hand mental connections in class 31, or social activities "He associates with strange people" in class 41. Examples 1-5 all use a reading of "associate" in the sense of correlation, which expresses increase or decrease, and actually falls into file 30. While risk may refer to dangerous social behaviour also in the pharmacogenomics domain, it can equally be meant as potential, speculative causing factor. Genetic factors, for example, are a risk that is not caused by social behaviour, and could also be subsumed to file 36. To summarize, based on our inspection of the paths in tables 3 and 4, the example sentences that they cover, and additional random samples further down in the lists we find that the interaction classes that have been suggested for the interaction of genes and proteins, for example in the BioNLP shared task, are useful, but they do not cover all cases that are needed in the broader pharmacogenomics domain. The top level ontology, on the other hand, is too broad; it suffices to use a subset of the available classes. We would like to suggest the following additional event labels for the pharmacogenomics domain: • treat: WordNet file 29. Could also be conflated with the following event class • associate/increase/decrease/correlate: as used in examples (1) and (2), WordNet file 30 • analyze/examine/investigate/show/prove: as used in (3), WordNet file 31 • cause/risk: WordNet file 36

Polarity and speculation
We have mentioned that WordNet file 30 and our suggested associate class is extremely broad. Increase or heal express positive associations, decrease a negative association. They refer to the same type of event, but with opposite polarity.
We have suggested to keep the BioNLP event type labels. They are: The BioNLP label Regulation has the additional types Positive Regulation (for example activation) and Negative Regulation (for example inhibition). They can be seen as subclass of the Regulation type and express the inherent polarity of an event. Research on detecting the inherent polarity of events has been popular recently under the name of sentiment detection. Inherent polarity is orthogonal to the event type, we would therefore suggest to use orthogonal additional classes, and only use one Regulation class. BioNLP uses orthogonal classes for expressing polarity in the form of negation and speculation. There has recently been increased interest in detecting negation and speculation in the biomedical domain, for example (Sarafraz and Nenadic, 2010). Both polarity and speculation can be expressed explicitly or be inherent properties of an event. Negative polarity is explicitly expressed by a negation, speculative polarity by using modal verbs such as may (example (5)) and could. Inherent negative polarity is e.g. expressed by decrease for associate, inherent speculative polarity by word semantics, for example we show versus we investigate or cause versus risk. Often polarity is supported by the syntactic context (we investigate whether or In conclusion, we show). We suggest these classes as a working hypothesis. Since we have derived them from frequent syntactic patterns and from clear textual features, they could strike a reasonable balance between too specific and too general, and they could probably be detected by text mining approaches.

Conclusion
Based on our pilot study, we believe that interaction classes are both beneficial and feasible. The set of classes will need to be discussed and tested in more detail in future research. We have suggested the following labels as a working hypothesis: treat (WordNet file 29), associate/correlate (WordNet file 30), investigate (WordNet file 31) and cause (WordNet file 36) in addition to the BioNLP labels. The orthogonal modification labels speculation and polarity have also been suggested to play an important role, and allowing us to reduce the number of event classes that are needed. risk is e.g. a speculative negative event of the cause class, increase a positive polarity version of associate/correlate. We have also given an overview of our current approach to the extraction of interactions between pharmacogenomical entities like drugs, genes and diseases. Our approach is based on syntactic analysis. We have used the top words of the syntactic classes to explore the patterns of domain events, and we suggest a set of interaction labels for the pharmacogenomics domain. Future research will show whether they can be detected reasonably well by text mining approaches, as we speculate.