A new supervised learning for gene regulatory network inference with novel filtering method

作者：Yang Bin^*; Zhang Wei; Lv Jiaguo

来源：International Journal of Performability Engineering, 2018, 14(5): 945-954.

DOI：10.23940/ijpe.18.05.p13.945954

• Bio-inspired algorithms
• Flexible neural trees
• Gene Expression Data
• Gene regulatory networks
• Information coefficient
• K-nearest neighbor method
• Supervised learning methods
• Unsupervised learning method

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.

• 出版日期2018