Network completion for static gene expression data.

Natsu Nakajima,Tatsuya Akutsu

doi:10.1155/2014/382452

Abstract

We tackle the problem of completing and inferring genetic networks under stationary conditions from static data, where network completion is to make the minimum amount of modifications to an initial network so that the completed network is most consistent with the expression data in which addition of edges and deletion of edges are basic modification operations. For this problem, we present a new method for network completion using dynamic programming and least-squares fitting. This method can find an optimal solution in polynomial time if the maximum indegree of the network is bounded by a constant. We evaluate the effectiveness of our method through computational experiments using synthetic data. Furthermore, we demonstrate that our proposed method can distinguish the differences between two types of genetic networks under stationary conditions from lung cancer and normal gene expression data.

Highlights

Estimation of genetic interactions from gene expression microarray data is an interesting and important issue in bioinformatics
In order to assess the potential effectiveness of DPLSQ-SS, we begin with network inference using two kinds of synthetic data
We addressed the problem of completing and inferring gene networks under the stationary conditions from static gene expression data

Summary

Introduction

Estimation of genetic interactions from gene expression microarray data is an interesting and important issue in bioinformatics. To estimate the dynamics of gene regulatory networks such as cell cycle and life cycle processes, various mathematical models and methods have been proposed using time series data. A large number of nontime series data are available, for example, samples from normal people and patients of various types of diseases. These data are not necessarily static, we may regard these data as static data because these are averaged over a large amount of cells in rather steady states

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