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A New Supervised Learning for Gene Regulatory Network Inference with Novel Filtering Method

Volume 14, Number 5, May 2018, pp. 945-954
DOI: 10.23940/ijpe.18.05.p13.945954

Bin Yang, Wei Zhang, and Jiaguo Lv

School of Information Science and Engineering, Zaozhuang University, Zaozhuang, 277160, China

(Submitted on January 29, 2018; Revised on March 2, 2018; Accepted on April 23, 2018)

Abstract:

Gene regulatory network (GRN) inference from gene expression data plays an important role in understanding the intricacies of the complex biological regulations for researchers. In this paper, a new hybrid supervised learning method (HSL) is proposed to infer gene regulatory network. In HSL, according to the data imbalance ratio, three different supervised learning methods: direct classification, K-Nearest Neighbor (KNN) method and complex-valued version of flexible neural tree (CVFNT) model are chosen to classify. A novel filtering method based on integration of mutual information (MI) and maximum information coefficient (MIC) is proposed to eliminate the redundant regulations inferred by HSL. Benchmark data from DREAM 5 are used to test the performance of our approach. The results show that our approach performs better than the popular unsupervised Learning methods and supervised Learning methods.

 

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