Wang, Yan and Zhang, Shuangquan and Yang, Lili and Yang, Sen and Tian, Yuan and Ma, Qin (2019) Measurement of Conditional Relatedness Between Genes Using Fully Convolutional Neural Network. Frontiers in Genetics, 10. ISSN 1664-8021
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Abstract
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.
Item Type: | Article |
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Subjects: | Euro Archives > Medical Science |
Depositing User: | Managing Editor |
Date Deposited: | 11 Feb 2023 04:31 |
Last Modified: | 05 Feb 2024 04:15 |
URI: | http://publish7promo.com/id/eprint/1954 |