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Front. Genet. | doi: 10.3389/fgene.2019.01009

Measurement of Conditional Relatedness Between Genes Using Fully Convolutional Neural Network

  • 1College of Computer Science and Technology; School of Artificial Intelligence, Jilin University, China
  • 2Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, China
  • 3the First Bethune Hospital, Jilin University, China
  • 4School of Artificial Intelligence, Jilin University, China
  • 5Department of Biomedical Informatics, College of Medicine, The Ohio State University, United States

Measuring conditional relatedness, the degree of relation between a pair of genes in a certain condition, is a basic but difficult task in bioinformatics, as traditional co-expression analysis methods rely on co-expression similarities, well known with high false positive rate. Complement with prior-knowledge similarities is a feasible way to tackle the problem. However, classical combination machine learning algorithms fail in detection and application of the complex mapping relations between similarities and conditional relatedness, so a powerful predictive model will have enormous benefit for measuring this kind of complex mapping relations. To this need, we propose a novel deep learning model of convolutional neural network with a fully connected first layer, named fully convolutional neural network (FCNN) to measure conditional relatedness between genes using both co-expression and prior-knowledge similarities. The results on validation and test datasets show FCNN model yields an average 3.0% and 2.7% higher accuracy values for identifying gene-gene interactions collected from the COXPRESdb, KEGG and TRRUST databases, and a benchmark dataset of Xiao-Yong et al. research, by grid-search 10-fold cross validation, respectively. In order to estimate the FCNN model, we conduct a further verification on the GeneFriends and DIP datasets, and the FCNN model obtains an average of 1.8% and 7.6% higher accuracy, respectively. Then the FCNN model is applied to construct cancer gene networks, and also calls more practical results than other compared models and methods. A website of the FCNN model and relevant datasets can be accessed from https://bmbl.bmi.osumc.edu/FCNN.

Keywords: conditional relatedness between genes, Fully convolutional neural network, co-expression similarity, prior-knowledge similarity, Gene network

Received: 18 Apr 2019; Accepted: 23 Sep 2019.

Copyright: © 2019 Wang, Zhang, Yang, Yang, Tian and Ma. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Dr. Yuan Tian, Jilin University, School of Artificial Intelligence, Changchun, China, yuantian@jlu.edu.cn