Measurement of Conditional Relatedness Between Genes Using Fully Convolutional Neural Network
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...
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doaj-fe7fce49b97d446c8d672fde1fcdfead2020-11-25T01:16:36ZengFrontiers Media S.A.Frontiers in Genetics1664-80212019-10-011010.3389/fgene.2019.01009463811Measurement of Conditional Relatedness Between Genes Using Fully Convolutional Neural NetworkYan Wang0Yan Wang1Shuangquan Zhang2Lili Yang3Sen Yang4Yuan Tian5Qin Ma6Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, ChinaSchool of Artificial Intelligence, Jilin University, Changchun, ChinaKey Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, ChinaDepartment of Obstetrics, The First Hospital of Jilin University, Changchun, ChinaKey Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, ChinaSchool of Artificial Intelligence, Jilin University, Changchun, ChinaDepartment of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, United StatesMeasuring 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. https://www.frontiersin.org/article/10.3389/fgene.2019.01009/fullconditional relatedness between genesfully convolutional neural networkco-expression similarityprior-knowledge similaritygene network |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yan Wang Yan Wang Shuangquan Zhang Lili Yang Sen Yang Yuan Tian Qin Ma |
spellingShingle |
Yan Wang Yan Wang Shuangquan Zhang Lili Yang Sen Yang Yuan Tian Qin Ma Measurement of Conditional Relatedness Between Genes Using Fully Convolutional Neural Network Frontiers in Genetics conditional relatedness between genes fully convolutional neural network co-expression similarity prior-knowledge similarity gene network |
author_facet |
Yan Wang Yan Wang Shuangquan Zhang Lili Yang Sen Yang Yuan Tian Qin Ma |
author_sort |
Yan Wang |
title |
Measurement of Conditional Relatedness Between Genes Using Fully Convolutional Neural Network |
title_short |
Measurement of Conditional Relatedness Between Genes Using Fully Convolutional Neural Network |
title_full |
Measurement of Conditional Relatedness Between Genes Using Fully Convolutional Neural Network |
title_fullStr |
Measurement of Conditional Relatedness Between Genes Using Fully Convolutional Neural Network |
title_full_unstemmed |
Measurement of Conditional Relatedness Between Genes Using Fully Convolutional Neural Network |
title_sort |
measurement of conditional relatedness between genes using fully convolutional neural network |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Genetics |
issn |
1664-8021 |
publishDate |
2019-10-01 |
description |
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. |
topic |
conditional relatedness between genes fully convolutional neural network co-expression similarity prior-knowledge similarity gene network |
url |
https://www.frontiersin.org/article/10.3389/fgene.2019.01009/full |
work_keys_str_mv |
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