Gene networks (GNs) capture the knowledge of diverse interactions among genes, which are conducive to discovering disease gene sets and locate the target of the drug. Discovering gene interactions via biological experiments is always exhaustive and expensive. The application of combining gene networks with integrated computing for effectively guiding experiments is widely used in gene interaction prediction. Some of work has been designed to integrate the neighbor information of Heterogeneous Data (HD) to predict gene interaction, but ignored the difference among gene networks and interaction types. Here, we proposed an auto-weighted framework integrating heterogeneous networks for gene interaction prediction (called AWGI), which evaluates and aggregates the neighbor information of five GNs to infer the associations among genes. In this paper, we compared the prediction ability of our method with other state-of-the-art methods. The experimental results show that AWGI can learn more informative gene representations from the HD and achieved a significant improvement in terms of the accuracy of interaction prediction.
{"title":"AWGI","authors":"Han Luo, Zhenfeng Lei, Hanping Ke","doi":"10.1145/3354031.3354047","DOIUrl":"https://doi.org/10.1145/3354031.3354047","url":null,"abstract":"Gene networks (GNs) capture the knowledge of diverse interactions among genes, which are conducive to discovering disease gene sets and locate the target of the drug. Discovering gene interactions via biological experiments is always exhaustive and expensive. The application of combining gene networks with integrated computing for effectively guiding experiments is widely used in gene interaction prediction. Some of work has been designed to integrate the neighbor information of Heterogeneous Data (HD) to predict gene interaction, but ignored the difference among gene networks and interaction types. Here, we proposed an auto-weighted framework integrating heterogeneous networks for gene interaction prediction (called AWGI), which evaluates and aggregates the neighbor information of five GNs to infer the associations among genes. In this paper, we compared the prediction ability of our method with other state-of-the-art methods. The experimental results show that AWGI can learn more informative gene representations from the HD and achieved a significant improvement in terms of the accuracy of interaction prediction.","PeriodicalId":244981,"journal":{"name":"Proceedings of the 2019 4th International Conference on Biomedical Signal and Image Processing (ICBIP 2019) - ICBIP '19","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114815732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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