{"title":"支持向量回归在药物毒性多目标预测中的应用","authors":"F. Adilova, Alisher Ikramov","doi":"10.1109/AICT50176.2020.9368837","DOIUrl":null,"url":null,"abstract":"We consider the task of drug activity prediction, specifically we predict the toxicity of fullerene-based nanoparticles in interaction with 1117 proteins. We use a multi-target Support Vector Regression model with a greedy feature selection technique to achieve RMSE of 362.9 on a test set. We also demonstrate the impact of hyperparameter tuning on model performance.","PeriodicalId":136491,"journal":{"name":"2020 IEEE 14th International Conference on Application of Information and Communication Technologies (AICT)","volume":"335 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Using Support Vector Regression in multi-target prediction of drug toxicity\",\"authors\":\"F. Adilova, Alisher Ikramov\",\"doi\":\"10.1109/AICT50176.2020.9368837\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We consider the task of drug activity prediction, specifically we predict the toxicity of fullerene-based nanoparticles in interaction with 1117 proteins. We use a multi-target Support Vector Regression model with a greedy feature selection technique to achieve RMSE of 362.9 on a test set. We also demonstrate the impact of hyperparameter tuning on model performance.\",\"PeriodicalId\":136491,\"journal\":{\"name\":\"2020 IEEE 14th International Conference on Application of Information and Communication Technologies (AICT)\",\"volume\":\"335 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-10-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 IEEE 14th International Conference on Application of Information and Communication Technologies (AICT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/AICT50176.2020.9368837\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE 14th International Conference on Application of Information and Communication Technologies (AICT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AICT50176.2020.9368837","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Using Support Vector Regression in multi-target prediction of drug toxicity
We consider the task of drug activity prediction, specifically we predict the toxicity of fullerene-based nanoparticles in interaction with 1117 proteins. We use a multi-target Support Vector Regression model with a greedy feature selection technique to achieve RMSE of 362.9 on a test set. We also demonstrate the impact of hyperparameter tuning on model performance.
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