Accepted Posters

Category 'Y'- Text Mining'

Poster Y01

Interaction Networks for Proteins Coded by Alternatively Spliced Human Genes

Bahar Taneri- Eastern Mediterranean University

Senay Kafkas (Eastern Mediterranean University, Computer Engineering); Ekrem Varoglu (Eastern Mediterranean University, Computer Engineering);

Short Abstract: A key process affecting interactomes is alternative splicing. We introduce a new approach for investigating how alternative splicing shapes the human interactome, by building an interaction network for proteins coded by alternatively spliced genes. Using text mining and machine learning techniques, we analyze 16,826 genes and obtain 88,345 interacting pairs.

Long Abstract: Click Here

Poster Y02

Identifying Associated Genes Specific to a Particular Disease Using Biomedical Literatures

Yeondae Kwon- National Institute of Genetics

Hideaki Sugawara (National Institute of Genetics, Center for Information Biology and DNA Data Bank of Japan);

Short Abstract: In this paper, we propose an algorithm that extracts a group of associated genes with a given disease from literatures and prioritizes them in terms of their specificity to the disease. This enables the identification of associated genes that do not have side-effects, which contributes to efficient drug developments.

Long Abstract: Click Here

Poster Y03

Towards Semantic Network of Bioinformatics Resources

Goran Nenadic- University of Manchester

Hammad Afzal (University of Manchester, School of Computer Science); Robert Stevens (University of Manchester, School of Computer Science);

Short Abstract: Bioinformatics has witnessed a huge number of tools, services and resources, but many of them are not accessible as their names and functionalities are unknown. We present a resource discovery approach based on bioinformatics literature. Almost 10,000 candidate resources were identified, terminologically profiled and organised in a searchable semantic network.

Long Abstract: Click Here

Poster Y04

Victoria: navigating to a new style of searching for web-services and workflows

Johan Karlsson- Malaga University

Javier Ríos (Malaga University, Department of Computer Architecture); Oswaldo Trelles (Malaga University, Department of Computer Architecture);

Short Abstract: The number of web-services in bioinformatics is rapidly increasing. However, lack of effective discovery tools for said applications is a barrier to wiring different services together as workflows. Victoria is a versatile Java library for dynamic service discovery and automatic service composition using public service metadata repositories.

Long Abstract: Click Here

Poster Y05

Text-mining of PubMed abstracts by natural language processing to create a free database on molecular mechanisms of bacterial pathogens – a year of use, feedback and development.

David Pot- SRA International

Sam Zaremba (SRA International, Health & Civil Services); Mila Ramos-Santacruz (SRA International, Health & Civil Services); Panna Shetty (SRA International, Health & Civil Services); Sri Iyer (SRA International, Health & Civil Services); Joel Fedorko (SRA International, Health & Civil Services); Jon Whitmore (SRA International, Health & Civil Services); Matthew Shaker (SRA International, Health & Civil Services); Nicole Perna (University of Wisconsin - Madison, Genome Center); Jeremy Glasner (University of Wisconsin - Madison, Genome Center); Guy Plunkett III (University of Wisconsin - Madison, Laboratory of Genetics); John Greene (SRA International, Health & Civil Services);

Short Abstract: The Enteropathogen Resource Integration Center (ERIC) text mining application has been released for a year+. The system extracts Gene - Roles; Mutation - Phenotypes; and Organism – Pathogenesis relationships from PubMed abstracts. We report progress of the system in the past year based on feedback from the user community.

Long Abstract: Click Here

Poster Y06

PubMedScan: an automatic paper recommendation system for newly published PubMed articles

Katsuhiko Murakami- Japan Biological Informatics Consortium

Fusano Todokoro (National Institute of Advanced Industrial Science and Technology, Biomedicinal Information Research Center); Yoshiharu Sato (National Institute of Advanced Industrial Science and Technology, Biomedicinal Information Research Center); Takashi Gojobori (National Institute of Advanced Industrial Science and Technology, Biomedicinal Information Research Center); Tadashi Imanishi (National Institute of Advanced Industrial Science and Technology, Biomedicinal Information Research Center);

Short Abstract: We introduce a paper-recommendation system in which a user specifies the search condition by multiple papers of the users’ interest. As recent improvements, we launched a new web sever of PubMedScan for anonymous users, and developed software to obtain PMIDs from PDF files of papers to input the users’ interest.

Long Abstract: Click Here

Poster Y07

CALBC – Collaborative Annotation of a Large Biomedical Corpus

Dietrich Rebholz-Schuhmann- European Bioinformatics Institute

Antonio Jimeno Yepes (European Bioinformatics Institute, Research Department); Erik van Mulligen (Erasmus Medical Center, Medical Informatics); Olivier Bodenreider (National Library of Medicine, Cognitive Science); David Milward (Linguamatics, Development); Udo Hahn (Friedrich Schiller Universitaet, Julie Laboratory);

Short Abstract: CALBC aims at creating a large annotated corpus (on the order of 150,000 documents) with about 5 to 10 different semantic types (genes, diseases) whose annotation is carried out automatically. The named entity annotations from the collaborators are fully integrated into a single corpus for future public annotation challenges.

Long Abstract: Click Here

Poster Y08

Whatizit-IeXML: Suite of Web services for biomedical information extraction

Dietrich Rebholz-Schuhmann- European Bioinformatics Institute

Antonio Jimeno Yepes (European Bioinformatics Institute, Research Department); Anika Oellrich (European Bioinformatics Institute, Research Department);

Short Abstract: Whatizit is a SOAP Web service infrastructure at the EBI to extract different semantic types from scientific literature: genes, proteins, diseases and composite solutions. Recently, methods for MeSH terms and chemical entities have been added. Whatizit-IeXML delivers the same results in the standardized IeXML format.

Long Abstract: Click Here

Poster Y09

Recognition of Synechocystis gene names in full papers

Hong-Woo Chun- Database Center for Life Science, Research Organization of Information and System, Japan

Shinobu Okamoto (Research Organization of Information and System, Database Center for Life Science); Yasunori Yamamoto (Research Organization of Information and System, Database Center for Life Science); Atsuko Yamaguchi (Research Organization of Information and System, Database Center for Life Science); Toshihisa Takagi (Research Organization of Information and System, Database Center for Life Science);

Short Abstract: The proposed approach aims to recognize Synechocystis gene names using features from not only the sentence but also the full paper. A rule-based and a dictionary-based candidate detection, and a machine learning-based classification were conducted. Experiments demonstrated that features from full papers have important roles in improving the performance.

Long Abstract: Click Here

Poster Y10

Selecting an Ontology for Biomedical Text Mining

He Tan- Linköpings universitet

Patrick Lambrix (Linköpings universitet, Institutionen för datavete);

Short Abstract: Biomedical ontologies provide the domain knowledge required by text mining applications in the area. In this poster we propose a framework for selecting an appropriate ontology for a particular text mining application, and present an experiment based on the framework choosing an ontology for a gene normalization system.

Long Abstract: Click Here

Accepted Posters

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• A) Bioinformatics of Health and Disease
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• F) Evolution
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• I) Genome Annotation
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• Q) Population Genetics and Variation
• R) Proteomics
• S) Regulation
• T) Reviews
• U) Sequence analysis
• V) Structural Genomics
• W) Structure and Function Prediction
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• Y) Text Mining
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