A Learning Framework to Improve Unsupervised Gene Network Inference

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9729)

Abstract

Network inference through link prediction is an important data mining problem that finds many applications in computational social science and biomedicine. For example, by predicting links, i.e., regulatory relationships, between genes to infer gene regulatory networks (GRNs), computational biologists gain a better understanding of the functional elements and regulatory circuits in cells. Unsupervised methods have been widely used to infer GRNs; however, these methods often create missing and spurious links. In this paper, we propose a learning framework to improve the unsupervised methods. Given a network constructed by an unsupervised method, the proposed framework employs a graph sparsification technique for network sampling and principal component analysis for feature selection to obtain better quality training data, which guides three classifiers to predict and clean the links of the given network. The three classifiers include neural networks, random forests and support vector machines. Experimental results on several datasets demonstrate the good performance of the proposed learning framework and the classifiers used in the framework.

Keywords

Feature selection Graph mining Network analysis Applications in biology and medicine 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Computer Science DepartmentKing Abdulaziz UniversityJeddahSaudi Arabia
  2. 2.New Jersey Institute of Technology Bioinformatics Program and Department of Computer ScienceUniversity HeightsNewarkUSA

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