Pacific Symposium on Biocomputing 2006 : Maui, Hawaii, 3-7 January 2006 / edited by Russ B. Altman ... [et al.].

Format:

Book

Conference/Event

Contributor:

Altman, Russ.

Conference Name:

Pacific Symposium on Biocomputing (13th : 2006 : Maui, Hawaii)

Language:

English

Subjects (All):

Biology--Computer simulation--Congresses.

Biology.

Biology--Mathematical models--Congresses.

Molecular biology--Computer simulation--Congresses.

Molecular biology.

Molecular biology--Mathematical models--Congresses.

Physical Description:

1 online resource (627 p.)

Other Title:

Biocomputing 2006

Place of Publication:

Hackensack, NJ : World Scientific, c2006.

Language Note:

English

Summary:

The Pacific Symposium on Biocomputing (PSB) 2006 is an international, multidisciplinary conference for the presentation and discussion of current research in the theory and application of computational methods in problems of biological significance. Presentations are rigorously peer reviewed and are published in an archival proceedings volume. PSB 2006 will be held January 3-7, 2006 at the Grand Wailea, Maui. Tutorials will be offered prior to the start of the conference. PSB 2006 will bring together top researchers from the US, the Asian Pacific nations, and around the world to exchange research results and address open issues in all aspects of computational biology. It is a forum for the presentation of work in databases, algorithms, interfaces, visualization, modeling, and other computational methods, as applied to biological problems, with emphasis on applications in data-rich areas of molecular biology. The PSB has been designed to be responsive to the need for critical mass in sub-disciplines within biocomputing. For that reason, it is the only meeting whose sessions are defined dynamically each year in response to specific proposals. PSB sessions are organized by leaders of research in biocomputing's "hot topics". In this way, the meeting provides an early forum for serious examination of emerging methods and approaches in this rapidly changing field.

Contents:

CONTENTS; Preface; LINKING BIOMEDICAL INFORMATION THROUGH TEXT MINING; Session Introduction K. Bretonnel Cohen, Olivier Bodenreider, and Lynette Hirschman; References; Extraction of Gene-Disease Relations from Medline Using Domain Dictionaries and Machine Learning Hong- Woo Chun, Yoshimasa Tsuruoka, Jin-Dong Kim, Rie Shiba, Naoki Nagata, Teruyoshi Hishiki, and Jun'ichi Tsujii; 1. Introduction; 2. Relation Extraction using Dictionaries and Machine Learning; 2.1. Construction of the Gene and Disease Dictionaries; 2.1.1. The gene dictionary; 2.1.2. The disease dictionary

2.2. Annotation of Corpus2.3. Filtering with a Maximum Entropy-based NER Classifier; 2.3.1. Features for NER; 3. Experimental Results; 3.1. Ezperiments without Filtering (Baseline); 3.2. Experiments with Filtering; 4. Conclusion and Future work; References; Significantly Improved Prediction of Subcellular Localization by Integrating Text and Protein Sequence Data Annette Hoglund, Torsten Blum, Scott Brady, Pierre Donnes, John San Miguel, Matthew Rocheford, Oliver Kohlbacher, and Hagit Shatkay; 1. Introduction; 2. Methods; 2.1. Sequence-based methods; 2.2. Text-based method

2.3. Integrated method3. Experiments and Results; 3.1. Experimental setting; 3.2. Result; 4. Discussion and Conclusion; References; Evaluation of Lexical Methods for Detecting Relationships Between Concepts from Multiple Ontologies Helen L. Johnson, K. Bretonnel Cohen, William A. Baumgartner Jr., Zhiyong Lu, Michael Bada, Todd Kester, Hyunmin Kim, and Lawrence Hunter; 1. Introduction; 1.1. Context and motivation; 2. Methods; 3. Results; 3.1. Linguistic techniques i n relationship searches; 4. Discussion and conclusions; References

Automatically Generating Gene Summaries from Biomedical Literature X u Ling, Jing Jiang, Xin He, Qiaozhu Mei, Chengxiang Zhai, and Bruce Schatz1. Introduction; 2. Related Work; 3. Automatic Gene Summarization; 3.1. Overview; 3.2. Keyword Retrieval Module; 3.2.1. Gene SynSet Construction; 3.2.2. Synonym Filtering; 3.3. Information Extraction Module; 3.3.1. Training Data Generation; 3.3.2. Sentence Extraction; 4. Experiments and Evaluation; 4.1. Experiment Setup; 4.2. Evaluation and Discussion; 5 . Conclusion and Future Work; References

Finding GeneRIFs via Gene Ontology Annotations Zhiyong Lu, K. Bretonnel Cohen, and Lawrence Hunter1. Introduction; 2. Related Work; 3. System and Method; 3.1. Data; 3.2. The relationship between GeneRIFs and their sources; 3.3. System; 4. Results; 5. Discussion; 5.1. Comparison with other features and methods; 5.2. GeneRIF prediction as automatic summarization; 6. Conclusion; 7. Acknowledgments; References; PhenoGO: Assigning Phenotypic Context to Gene Ontology Annotations with Natural Language Processing Yves Lussier, Tara Borlawsky, Daniel Rappaport, Yang Liu, and Carol Friedman

1 Introduction, Related Work and Background

Notes:

Description based upon print version of record.

Includes bibliographical references.

ISBN:

9786611897246

9781281897244

1281897248

9789812701626

9812701621

OCLC:

922951817