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

Authors:
Doheon Lee;Sangwoo Kim;Younghoon Kim
Affiliations:
KAIST, Daejeon, Republic of Korea;KAIST, Daejeon, Republic of Korea;KAIST, Daejeon, Republic of Korea
Venue:
TMBIO '06 Proceedings of the 1st international workshop on Text mining in bioinformatics
Year:
2006

Citing 9
Cited 0

Using Bayesian networks to analyze expression data

RECOMB '00 Proceedings of the fourth annual international conference on Computational molecular biology
Construction of Large-Scale Bayesian Networks by Local to Global Search

PRICAI '02 Proceedings of the 7th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
On Learning Gene Regulatory Networks Under the Boolean Network Model

Machine Learning
Selective integration of multiple biological data for supervised network inference

Bioinformatics
Inferring genetic regulatory logic from expression data

Bioinformatics
Modularized learning of genetic interaction networks from biological annotations and mRNA expression data

Bioinformatics
Growing Bayesian network models of gene networks from seed genes

Bioinformatics
Extraction of regulatory gene/protein networks from Medline

Bioinformatics
Extracting regulatory gene expression networks from PubMed

ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics

Quantified Score

Hi-index	0.00

Visualization

Abstract

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. To address these requirements, 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 whole architecture of BioCAD and essential modules for bio-network inference and analysis.