Bayesian methods for elucidating genetic regulatory networks

Although the Bayesian network has been notified as a prominent method to infer gene regulatory processes, learning the Bayesian network structure is NP hard and computationally intricate.Therefore, we propose a novel inference method based on low-order conditional independence that extends to the case of the Bayesian network to deal with a large number of genes and an insufficient sample size.Lastly, the proposed method was also applied to psychiatric disorder data in order to explore how the method works with real data.In order to understand more accurate causal relationships between a complex disease and genetic variations, we need to consider how the genotypic perturbations affect expression phenotypes that are potentially associated with a target disease.

Therefore it is important to evaluate how genetic perturbations affect genes on regulatory networks that are associated with a target disease phenotype.

The method of claim 2, wherein said step of minimizing a BNRC criterion comprises using a non-linear curve fitting method selected from the group consisting of polynomial bases, Fourier series, wavelet bases, regression spline bases and B-splines.15.

The method of claim 14, wherein said bootstrap method comprises the steps of: (1) providing a bootstrap gene expression matrix by randomly sampling a number of times, with replacement, from the original gene library expression data; (2) estimating the genetic network for gene,- and gene,-; (3) repeating steps (1) and (2) T times, thereby producing T genetic networks; and20.

Other embodiments include the use of bootstrapping methods and determination of edge effects to more accurately provide network information between expressed genes.

Methods of this invention were validated using information obtained from prior studies, as well as from newly carried out studies of gene expression.(a) providing a quantitative disruptant data library for a set of genes of an organism, said library including expression results based on disruption of each gene in said set of genes, quantifying an average effect and a measure of variability of each disruption on each other of said genes;3.

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Wen-Shyong Tzou is an associate professor at the Institute of Bioscience and Biotechnology, National Taiwan Ocean University, Keelung, Taiwan.

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