{"title":"用特征选择方法预测不同治疗下乳腺癌的预后","authors":"H. Pham, L. Rueda, A. Ngom","doi":"10.1145/3107411.3108226","DOIUrl":null,"url":null,"abstract":"Gene expression data have been used in many researches to help reveal the underlying mechanism of many diseases. In this study, we applied feature selection techniques on breast cancer patients in the METABRIC Study to predict whether patients will be disease free or not, under different treatments. Our models for prediction are of high performance, thus, the genes in those models might help reveal the mechanism of the disease, and these potential biomarkers can become targets for new therapies.","PeriodicalId":246388,"journal":{"name":"Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics","volume":"148 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Predicting Breast Cancer Outcome under Different Treatments by Feature Selection Approaches\",\"authors\":\"H. Pham, L. Rueda, A. Ngom\",\"doi\":\"10.1145/3107411.3108226\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Gene expression data have been used in many researches to help reveal the underlying mechanism of many diseases. In this study, we applied feature selection techniques on breast cancer patients in the METABRIC Study to predict whether patients will be disease free or not, under different treatments. Our models for prediction are of high performance, thus, the genes in those models might help reveal the mechanism of the disease, and these potential biomarkers can become targets for new therapies.\",\"PeriodicalId\":246388,\"journal\":{\"name\":\"Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics\",\"volume\":\"148 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-08-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3107411.3108226\",\"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 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3107411.3108226","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Predicting Breast Cancer Outcome under Different Treatments by Feature Selection Approaches
Gene expression data have been used in many researches to help reveal the underlying mechanism of many diseases. In this study, we applied feature selection techniques on breast cancer patients in the METABRIC Study to predict whether patients will be disease free or not, under different treatments. Our models for prediction are of high performance, thus, the genes in those models might help reveal the mechanism of the disease, and these potential biomarkers can become targets for new therapies.