| Accepted Posters |
| Category 'S'- text mining' |
| Poster S01 |
| PubMeth: a cancer methylation database, based on text-mining |
| Maté Ongenaert- UGent |
| Leander Van Neste (Ugent, Molecular Biotechnology); Tim De Meyer (Ugent, Molecular Biotechnology); Gerben Menschaert (Ugent, Molecular Biotechnology); Sofie Bekaert (Ugent, Molecular Biotechnology); Wim Van Criekinge (Ugent, Molecular Biotechnology); |
| Short Abstract: None On File |
| Long Abstract: Click Here |
| Poster S02 |
| Leveraging the structure of the Semantic Web to enhance information retrieval for proteomics |
| Andrew Smith- Yale University |
| Kei Cheung (Yale University, Center for Medical Informatics); Michael Krauthammer (Yale University, Department of Pathology); Martin Schultz (Yale University, Department of Computer Science); Mark Gerstein (Yale University, Department of Molecular Biophysics and Biochemistry); |
| Short Abstract: None On File |
| Long Abstract: Click Here |
| Poster S03 |
| EB-eye : The EBI search engine |
| Franck Valentin- European Bioinformatics Institute |
| Mickael Goujon (European Bioinformatics Institute, External Services); Rodrigo Lopez (European Bioinformatics Institute, External Services); |
| Short Abstract: The EB-eye is an Apache Lucene-based search engine aimed at providing unified access to the EBI databases. The system generates indices using a condensed but meaningful subset of the original data and returns summary information and links to the original data as well as all EBI specific database cross-references. |
| Long Abstract: Click Here |
| Poster S04 |
| Extraction of Facts and Relationships Relevant to Molecular Mechanisms of Bacterial Pathogenesis through Natural Language Processing |
| David Pot- SRA International, Inc. |
| Sam Zaremba (SRA International, Inc., Global Health); Mila Ramos-Santacruz (SRA International, Inc., Global Health); Thomas Hampton (SRA International, Inc., Global Health); Panna Shetty (SRA International, Inc., Global Health); Joel Fedorko (SRA International, Inc., Global Health); Jon Whitmore (SRA International, Inc., Global Health); Nicole Perna (University of Wisconsin, Genome Center); Jeremy Glasner (University of Wisconsin, Genome Center); Guy Plunkett III (University of Wisconsin, Laboratory of Genetics); Matthew Shaker (SRA International, Inc., Global Health); John Greene (SRA International, Inc., Global Health); |
| Short Abstract: To help understand bacterial pathogenesis, the Enteropathogen Resource Integration Center (ERIC) Bioinformatics Resource Center (www.ericbrc.org) offers a proven text mining application to its user community, which extracts Gene - Roles; Mutation - Phenotypes; and Organism - Pathogenesis relationships from PubMed abstracts. The application and search tools are available (http://www.ericbrc.org/portal/eric/articles). |
| Long Abstract: Click Here |
| Poster S05 |
| Extracting protein complexes from biomedical literature |
| Wagied Davids- University of Toronto |
| No additional authors |
| Short Abstract: We have initiated the task of extracting protein complexes from the biomedical literature to provide an updated resource on protein complexes, not only limited to yeast but also other species. Our online web resource is available for users to query protein complexes but also provide annotation tools for users to extract protein complexes from PubMed. |
| Long Abstract: Click Here |
| Poster S06 |
| Using textual context for improving OCR performance in biomedical literature retrieval |
| Songhua Xu- Yale University |
| Martin Schultz (Yale University, Computer Science); Michael Krauthammer (Yale University, Pathology & Yale Center for Medical Informatics); |
| Short Abstract: Today’s information retrieval (IR) techniques are mostly text-based, which fail in situations when textual information is not easily accessible, such as in biomedical images and figures. We propose to augment IR with optical character recognition (OCR) capabilities, and describe a context-based method for boosting OCR performance. |
| Long Abstract: Click Here |
| Poster S07 |
| Surveying the Biomedical Literature Using Automatically Mined Gene-related Key Terms |
| Catalina Tudor- University of Delaware |
| K. Vijay-Shanker (University of Delaware, Computer Science); Carl Schmidt (University of Delaware, Animal and Food Sciences); |
| Short Abstract: We developed eGIFT, a system which aids annotators to quickly find articles describing gene functions and scientists surveying the results of high-throughput experiments to quickly extract information important to their hits. eGIFT users can learn about a gene by consulting a list of relevant key terms automatically mined from text. |
| Long Abstract: Click Here |
| Poster S08 |
| A literature-based dissimilarity measure to explore genome-wide gene relatedness and pathways |
| Zuoshuang Xiang- University of Michigan |
| Zhaohui Qin (University of Michigan, Biostatistics); Yongqun He (University of Michigan, Unit for Laboratory Animal Medicine, and Microbiology and Immunology); |
| Short Abstract: A MeSH-based dissimilarity score is developed to assess the relatedness between two genes based on the frequency of MeSH terms in the literature that refer to each gene. Studies based on Brucella and E. coli genes demonstrate that MeSHdisc can reveal gene relatedness and pathways among bacterial genomes. |
| Long Abstract: Click Here |
| Poster S09 |
| PubCurator - a text analysis platform. |
| Kai Schlamp- Johannes Gutenberg University Mainz |
| Markus Krupp (Johannes Gutenberg University Mainz, Department of Medicine I); Peter R. Galle (Johannes Gutenberg University Mainz, Department of Medicine I); Andreas Teufel (Johannes Gutenberg University Mainz, Department of Medicine I); |
| Short Abstract: PubCurator is a biomedical text analysis platform providing extensive support for text mining especially of the NCBI databases. Text analysis may be performed in manual or automatic mode with a full featured graphical frontend built upon the Eclipse RCP. The application is freely available from our website http://www.medicalgenomics.org. |
| Long Abstract: Click Here |
| Poster S10 |
| Mining Protein Interactions from Text using Convolution Kernels |
| Ramanathan Narayanan- Northwestern University |
| Alok Choudhary (Northwestern University, EECS); Simon Lin (Northwestern University, Northwestern Medical School); Sanchit Misra (Northwestern University, EECS); |
| Short Abstract: We examine the problem of identifying protein-protein interactions in biomedical literature databases by combining NLP and text mining techniques. We propose the use of a hierarchical framework to reduce the search space and introduce Convolution kernels in Support Vector Machines to accurately identify protein-protein interactions in biomedical literature databases. |
| Long Abstract: Click Here |
| Poster S11 |
| Entropy and enrichment-based approaches for annotating protein clusters using literature |
| Shirley Wu- Stanford University |
| Russ Altman (Stanford University, Bioengineering); |
| Short Abstract: Clustering algorithms produce groups of proteins that are similar in a way that may not immediately be apparent. We show that computational, literature-based approaches focused on term entropy and enrichment are able to derive comprehensive and informative terms describing clusters of proteins. |
| Long Abstract: Click Here |
| Poster S12 |
| Visualizing evolution and impact of biomedical fields |
| Murat Cokol- Harvard Medical School |
| Raul Rodriguez-Esteban (Columbia University, ); |
| Short Abstract: We describe a tool (www.scitrends.net) for visualization of more than 200 thousand biomedical scientific trends. The method captures variations in scientific impact over time to allow for a comparison of relative significance and evolution of fields similar to a financial market scorecard. |
| Long Abstract: Click Here |
| Poster S13 |
| Pubmeth: reviewed methylation database in cancer based on text-mining |
| Maté Ongenaert- Ghent University |
| Leander Van Neste (Ghent University, Molecular Biotechnology); Tim De Meyer (Ghent University, Molecular Biotechnology); Gerben Menschaert (Ghent University, Molecular Biotechnology); Sofie Bekaert (Ghent University, Molecular Biotechnology); Wim Van Criekinge (Ghent University, Molecular Biotechnology); |
| Short Abstract: PubMeth.org is a cancer methylation database that includes genes that are reported to be methylated in various cancer types. A query can be based either on genes or on cancer types. PubMeth is based on text-mining of PubMed abstracts, combined with manual reading and expert annotation of preselected abstracts. |
| Long Abstract: Click Here |
| Poster S14 |
| Towards Mining Images from Full-Text Articles: Associating Images with Reference Text |
| hong yu- University of Wisconsin-Milwaukee |
| Shashank Agarwal (UWM, Medical Informatics); Mary Shimoyama (UWM, Medical Informatics); |
| Short Abstract: Images are important part of experimental results reported in bioscience full-text articles. However, image-mining poses an important research challenge. Here we report our investigation and annotation of associating reference text with images. Our work is an important first step towards developing automated approaches for mining images in full-text biomedical articles. |
| Long Abstract: Click Here |
| Poster S15 |
| Searching PubMed articles queried by multiple articles |
| Katsuhiko Murakami- Japan Biological Informatics Consortium |
| Yoshiharu Sato (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); Takashi Gojobori (National Institute of Advanced Industrial Science and Technology, Biomedicinal Information Research Center); |
| Short Abstract: To obtain appropriate articles from PubMed, we developed a PubMed article search system that takes multiple articles as input. The system can suggest some directions of query-optimization by splitting the query, or deleting outliers. The system helps user find more appropriate articles. |
| Long Abstract: Click Here |
| Poster S16 |
| BioLexicon: Towards a reference terminological resource in the biomedical domain |
| Dietrich Rebholz-Schuhmann- European Bioinformatics Institute |
| Piotr Pezik (EMBL-EBI, Rebholz group); Vivian Lee (EMBL-EBI, Rebholz group); Jung-Jae Kim (EMBL-EBI, Rebholz group); Riccardo del Gratta (CNR, ILC); Yutaka Sasaki (University of Manchester, NaCTeM); Jock McNaught (University of Manchester, NaCTeM); Simonetta Montemagni (CNR, ILC); Monica Monachini (CNR, ILC); Nicoletta Calzolari (CNR, ILC); Sophia Ananiadou (University of Manchester, NaCTeM); |
| Short Abstract: The BioLexicon is a publicly available large-scale terminological resource which brings together potential terms from several resources representing selected semantic types (genes, proteins, chemicals, species, enzymes, selected ontological terms). The schema of the BioLexicon enables improved resolution of term ambiguity and follows lexical standards for terminological resources. |
| Long Abstract: Click Here |
| Poster S17 |
| Web-based literature mining tool for target identification and functional enrichment analysis |
| Junguk Hur- University of Michigan, Ann Arbor |
| Tim Wiggin (National Center for Integrative Biomedical Informatics, Bioinformatics); Alex Ade (National Center for Integrative Biomedical Informatics, Bioinformatics); Eva Feldman (University of Michigan, Ann Arbor, Neurology); David States (University of Michigan, Ann Arbor, Bioinformatics Program); |
| Short Abstract: Web-based JUMiner is a dictionary- and rule-based literature mining tool working on full text literature. Name-conflict issue is resolved by a scoring scheme based on co-occurrence of symbols and descriptions. It also features functional enrichments tests to find enriched targets, GO terms, MeSH terms, pathways, and protein-protein interactions. |
| Long Abstract: Click Here |
| Accepted Posters |
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- F) Evolution
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- I) Genome Annotation
- J) Genomics
- K) Interactions
- L) Machine Learning
- M) Population Genetics and Variation
- N) Proteomics
- O) Regulation
- P) Sequence Analysis
- Q) Structure and Function Prediction
- R) Systems Biology and Networks
- S) text mining
- T) Other (includes posters with fewer than 10 submissions)
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