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BioCAD: an information fusion platform for bio-network inference and analysis

Authors
 Doheon Lee  ;  Sangwoo Kim  ;  Younghoon Kim 
Citation
 BMC BIOINFORMATICS, Vol.8(Suppl. 9) : 2, 2007 
Journal Title
BMC BIOINFORMATICS
Issue Date
2007
Abstract
Background : As systems biology has begun to draw growing attention, bio-network inference and analysis have become more and more important. Though there have been many efforts for bio-network inference, they are still far from practical applications due to too many false inferences and lack of comprehensible interpretation in the biological viewpoints. In order for applying to real problems, they should provide effective inference, reliable validation, rational elucidation, and sufficient extensibility to incorporate various relevant information sources.
Results : We have been developing an information fusion software platform called BioCAD. It is utilizing both of local and global optimization for bio-network inference, text mining techniques for network validation and annotation, and Web services-based workflow techniques. In addition, it includes an effective technique to elucidate network edges by integrating various information sources. This paper presents the architecture of BioCAD and essential modules for bio-network inference and analysis.
Conclusion : BioCAD provides a convenient infrastructure for network inference and network analysis. It automates series of users' processes by providing data preprocessing tools for various formats of data. It also helps inferring more accurate and reliable bio-networks by providing network inference tools which utilize information from distinct sources. And it can be used to analyze and validate the inferred bio-networks using information fusion tools.
Files in This Item:
T200705774.pdf Download
DOI
10.1186/1471-2105-8-S9-S2
Appears in Collections:
1. College of Medicine (의과대학) > Yonsei Biomedical Research Center (연세의생명연구원) > 1. Journal Papers
Yonsei Authors
Kim, Sangwoo(김상우) ORCID logo https://orcid.org/0000-0001-5356-0827
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/95733
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