(SVM and RF) for classification and recommendation services. … advanced algorithms for massive image analysis. computational solutions. 4. massive data analysis toward massive imaging data sets. 1. high 1. for processing 1. legacy 1. data format for 1. security and -- 1. mobile throughput raw data in computing Genome privacy platforms for Genomic compressed data variant calls cluster and other browsers protection on physicians (300GB/day) 2. machine PaaS and IaaS health records accessing from various learning for (computing and clinical genomic data DNA sequencers complex analysis cluster) research (mobile device) 2. distributed on systematic 2. huge data databases 19 data source errors from storage in PB (sequencers) sequencing range (storage) 3. various file technologies are 3. Unix-based formats either hard to legacy structured and characterize sequencing unstructured data bioinformatics software (sw pkg) 1. multiple 1. huge data 1. real time 1. login security - 1. methods to -- centralized data 2. scalable storage interactive username and improve data sources RDMS for parallel bulk password quality required. analysis 2. proteins and heterogeneity loading 2. creation of 2. data their structural biological data capability user account to clustering, features, core 2. real-time rapid 2. interactive submit and classification, genomic data, and parallel bulk Web UI, backend access dataset to reduction. new types of loading precomputations, system via web 3. Integrate new data 3. Oracle batch job interface data / content “Omics” such as RDBMS, SQLite computation 3. single sign on into the system.s transcriptomics, files, flat text submission from capability (SSO) data store methylomics, files, Lucy (a the UL annotate data 20 and proteomics version of 3. download describing gene Lucene) for assembled and expression keyword annotated 3. Front real time searches, BLAST datasets for Web UI databases, offline analysis. interactive. Back USEARCH 4. ability to end data loading databases query and processing must 4. Linux cluster, browse data via keep up with Oracle RDBMS interactive Web exponential server, large UI. growth of memory 5. visualize sequence data machines, structure [of due to the rapid standard Linux data] at different
(SVM and RF) for classification and recommendation services. … advanced algorithms for massive image analysis. computational solutions. 4. massive data analysis toward massive imaging data sets. 19 Genomic 1. high throughput compressed data (300GB/day) from various DNA sequencers 2. distributed data source (sequencers) 3. various file formats either structured and unstructured data 1. for processing raw data in variant calls 2. machine learning for complex analysis on systematic errors from sequencing technologies are hard to characterize 1. legacy computing cluster and other PaaS and IaaS (computing cluster) 2. huge data storage in PB range (storage) 3. Unix-based legacy sequencing bioinformatics software (sw pkg) 1. data format for Genome browsers 1. security and privacy protection on health records and clinical research databases -- 1. mobile platforms for physicians accessing genomic data (mobile device) 20 analysis 1. multiple centralized data sources 2. proteins and their structural features, core genomic data, new types of “Omics” data such as transcriptomics, methylomics, and proteomics describing gene expression 3. Front real time Web UI interactive. Back end data loading processing must keep up with exponential growth of sequence data due to the rapid 2. scalable RDMS for heterogeneity biological data 2. real-time rapid and parallel bulk loading 3. Oracle RDBMS, SQLite files, flat text files, Lucy (a version of Lucene) for keyword searches, BLAST databases, USEARCH databases 4. Linux cluster, Oracle RDBMS server, large memory machines, standard Linux 1. huge data storage 1. real time interactive parallel bulk loading capability 2. interactive Web UI, backend precomputations, batch job computation submission from the UL 3. download assembled and annotated datasets for offline analysis. 4. ability to query and browse data via interactive Web UI. 5. visualize structure [of data] at different 1. login security - username and password 2. creation of user account to submit and access dataset to system via web interface 3. single sign on capability (SSO) 1. methods to improve data quality required. 2. data clustering, classification, reduction. 3. Integrate new data / content into the system.s data store annotate data --