Conference proceeding
A language modeling text mining approach to the annotation of protein community
BIBE '06 : sixth IEEE Symposium on BioInformatics and BioEngineering : 16-18 October 2006, v 6, pp 12-19
01 Jan 2006
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
This paper discusses an ontology based language modeling text mining approach to the annotation of protein community. Communities appear to play an important role in the functional properties of complex networks. Being able to annotate the identified the community structure in a biological network can help us to understand better the structure and dynamics of biological systems. Traditional method such as Gene Ontology (GO) provides information about the functionality of gene products, but they are not enough to annotate community as for only limited number of proteins in the database, limited protein properties available for annotation and the inability to annotate a group of gene products as a whole. Thus, we present an ontology based mixture language model approach to annotate protein community. Compared to traditional method, we have the following three advantages. First, biomedical literature mining brings much richer information than existed gene databases. Second, the mixture language model can help "purify" the document by eliminating some background noise. Third, using domain ontology, we extract biological concept and concept pairs from abstracts. Biological concept is more meaningful than word or multi-word phrases. Moreover, using concept pairs can deliver much more information and serve as evidence of annotation results. We test our approach on four communities SAGA-SRB, CCR-NOT, RFC and ARP2/3, detected from dataset of interactions for Saccharomyces cerevisae from the General Repository for Interaction Datasets (GRID). Annotation results provide a very coherent indication of functionality of each community.
Metrics
Details
- Title
- A language modeling text mining approach to the annotation of protein community
- Creators
- Xiaodan Zhang - Drexel UniversityDaniel D. Wu - Drexel Univ, Coll Informat Sci & Techno, 3141 Chestnut, Philadelphia, PA 19104 USAXiaohua Zhou - Drexel UniversityXiaohua Hu - Drexel University, Information Science
- Publication Details
- BIBE '06 : sixth IEEE Symposium on BioInformatics and BioEngineering : 16-18 October 2006, v 6, pp 12-19
- Publisher
- IEEE
- Number of pages
- 2
- Grant note
- 240205; 240196 / PA Dept of Health Tobacco Settlement Formula NSF IIS 0448023 / NSF Career; National Science Foundation (NSF); NSF - Office of the Director (OD) 239667 / PA Dept of Health CCF 0514679 / NSF; National Science Foundation (NSF)
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:000242503800002
- Scopus ID
- 2-s2.0-34547467089
- Other Identifier
- 991014632151604721
UN Sustainable Development Goals (SDGs)
This publication has contributed to the advancement of the following goals:
Source: SDGs in the Output
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Biochemical Research Methods
- Computer Science, Information Systems
- Computer Science, Interdisciplinary Applications