JOURNAL OF COMPUTERS (JCP)
ISSN : 1796-203X
Volume : 1 Issue : 7 Date : October/November 2006
CF-GeNe: Fuzzy Framework for Robust Gene Regulatory Network Inference
Muhammad Shoaib B. Sehgal, Iqbal Gondal, and Laurence S. Dooley
Full Text: PDF (375 KB)
Most Gene Regulatory Network (GRN) studies ignore the impact of the noisy nature of gene
expression data despite its significant influence upon inferred results. This paper presents an
innovative Collateral-Fuzzy Gene Regulatory Network Reconstruction (CF-GeNe) framework for
Gene Regulatory Network (GRN) inference. The approach uses the Collateral Missing Value
Estimation (CMVE) algorithm as its core to estimate missing values in microarray gene expression
data. CF-GeNe also mimics the inherent fuzzy nature of gene co-regulation by applying fuzzy
clustering principles using the well-established fuzzy cmeans algorithm, with the model adapting to
the data distribution by automatically determining key parameters, like the number of clusters.
Empirical results confirm that the CMVE-based CF-GeNe paradigm infers the majority of
co-regulated links even in the presence of large numbers of missing values, compared to other
data imputation methods including: Least Square Impute (LSImpute), K-Nearest Neighbour Impute
(KNN), Bayesian Principal Component Analysis Impute (BPCA) and ZeroImpute. The statistical
significance of this improved performance has been underscored by gene selection and also by
applying the Wilcoxon Ranksum Significance Test, with results corroborating the ability of CF-GeNe
to successfully infer GRN interactions in noisy gene expression data.
Gene Regulatory Networks, Significant Gene Selection, Missing Value Imputation, Collateral Missing
Values Estimation (CMVE), Fuzzy Clustering.