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Reconstruction of gene regulatory networks by stepwise multiple linear regression from time-series microarray data
Conference proceeding

Reconstruction of gene regulatory networks by stepwise multiple linear regression from time-series microarray data

Yiqian Zhou, R Qureshi and A Sacan
2012 7th International Symposium on Health Informatics and Bioinformatics, pp 76-81
Apr 2012
DOI: https://doi.org/10.1109/HIBIT.2012.6209046

Abstract

Biological system modeling Computational modeling Data models Mathematical model Predictive models Gene Expression
Gene regulatory networks provide a powerful abstraction of the complex interactions among genes involved in functional pathways. Experimental determination of these interactions using a classical experimental method, although of extreme value, is laborious and prohibitive at large scales. Over the last decade, a number of computational approaches have been developed to infer gene regulatory networks from high-throughput experimental data. In this study, we introduce a new algorithm for regulatory network inference, based on stepwise multiple regression of time-series microarray data. Compared to other existing methods, our regression-based method provides a clear interpretation of the inferred interactions. The statistical significance associated with each prediction can be utilized to rank the interactions, which is important in prioritization of predictions for further experimental verification. We demonstrate the performance of our approach on a well-known yeast cell cycle pathway and show that it makes more accurate predictions than existing methods.

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