A hybrid algorithm to infer genetic networks

Authors:
Cheng-Long Chuang;Chung-Ming Chen;Grace S. Shieh
Affiliations:
Institute of Biomedical Engineering, National Taiwan University, Taipei City, Taiwan;Institute of Biomedical Engineering, National Taiwan University, Taipei City, Taiwan;Institute of Statistical Science, Academia Sinica, Taipei City, Taiwan
Venue:
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
Year:
2006

Citing 4
Cited 0

Using Bayesian networks to analyze expression data

RECOMB '00 Proceedings of the fourth annual international conference on Computational molecular biology
DBRF--MEGN method: an algorithm for deducing minimum equivalent gene networks from large-scale gene expression profiles of gene deletion mutants

Bioinformatics
Discovery of meaningful associations in genomic data using partial correlation coefficients

Bioinformatics
An empirical Bayes approach to inferring large-scale gene association networks

Bioinformatics

Quantified Score

Hi-index	0.00

Visualization

Abstract

A pattern recognition approach, based on shape feature extraction, is proposed to infer genetic networks from time course microarray data. The proposed algorithm learns patterns from known genetic interactions, such as RT-PCR confirmed gene pairs, and tunes the parameters using particle swarm optimization algorithm. This work also incorporates a score function to separate significant predictions from non-significant ones. The prediction accuracy of the proposed method applied to data sets in Spellman et al. (1998) is as high as 91%, and true-positive rate and false-negative rate are about 61% and 1%, respectively. Therefore, the proposed algorithm may be useful for inferring genetic interactions.