Wei Du, Kristin A Dickinson, Calvin A. Johnson, L. Saligan
{"title":"用机器学习方法识别基因来预测癌症放疗相关的疲劳","authors":"Wei Du, Kristin A Dickinson, Calvin A. Johnson, L. Saligan","doi":"10.1145/3233547.3233636","DOIUrl":null,"url":null,"abstract":"While many factors influence the fatigue experienced by patients undergoing radiation therapy (RT), we hypothesize that expression of genes related to oxidative stress can be predictive of RT-related fatigue. In this work, we present a two-phase scheme which first selects a limited subset of genes deemed most predictive by a regularized elastic net, followed by a widely used classifier, the regularized random forest, to discriminate patients having high fatigue from low fatigue during RT. The model predicted 80% accuracy (0.80 AUC) in cross-validation. Initial results suggest that several genes are consistently selected in the proposed scheme, such as PRDX5, FHL2 and GPX4, showing promise as potential predictors for RT-related fatigue, and may provide information of its biologic underpinnings.","PeriodicalId":131906,"journal":{"name":"Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics","volume":"55 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Identifying Genes to Predict Cancer Radiotherapy-Related Fatigue with Machine-Learning Methods\",\"authors\":\"Wei Du, Kristin A Dickinson, Calvin A. Johnson, L. Saligan\",\"doi\":\"10.1145/3233547.3233636\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"While many factors influence the fatigue experienced by patients undergoing radiation therapy (RT), we hypothesize that expression of genes related to oxidative stress can be predictive of RT-related fatigue. In this work, we present a two-phase scheme which first selects a limited subset of genes deemed most predictive by a regularized elastic net, followed by a widely used classifier, the regularized random forest, to discriminate patients having high fatigue from low fatigue during RT. The model predicted 80% accuracy (0.80 AUC) in cross-validation. Initial results suggest that several genes are consistently selected in the proposed scheme, such as PRDX5, FHL2 and GPX4, showing promise as potential predictors for RT-related fatigue, and may provide information of its biologic underpinnings.\",\"PeriodicalId\":131906,\"journal\":{\"name\":\"Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics\",\"volume\":\"55 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-08-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3233547.3233636\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3233547.3233636","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Identifying Genes to Predict Cancer Radiotherapy-Related Fatigue with Machine-Learning Methods
While many factors influence the fatigue experienced by patients undergoing radiation therapy (RT), we hypothesize that expression of genes related to oxidative stress can be predictive of RT-related fatigue. In this work, we present a two-phase scheme which first selects a limited subset of genes deemed most predictive by a regularized elastic net, followed by a widely used classifier, the regularized random forest, to discriminate patients having high fatigue from low fatigue during RT. The model predicted 80% accuracy (0.80 AUC) in cross-validation. Initial results suggest that several genes are consistently selected in the proposed scheme, such as PRDX5, FHL2 and GPX4, showing promise as potential predictors for RT-related fatigue, and may provide information of its biologic underpinnings.