{"title":"不同分类算法对RNA-Seq癌症数据的比较研究","authors":"N. Şimşek, B. Haznedar, Cihan Kuzudişli","doi":"10.18844/gjpaas.v0i12.4983","DOIUrl":null,"url":null,"abstract":"Gene mutations are the most important reason of cancer diseases, and there are different kind of causing genes across these diseases. RNA-Seq technology enables us to allow for gathering information about many genes simultaneously; hence, RNA-Seq data can be used for cancer diagnosis and classification. In this study, RNA-Seq dataset for renal cell cancer is analysed using three different developed classification methods: random forest (RF), artificial neural network (ANN) and deep learning (DL). The genes in our dataset are related to the following cancer types: kidney renal papillary cell, kidney renal clear cell and kidney chromophore carcinomas. It suggests that the DL method gives the highest accuracy rate compared to RF and ANN for 95.15%, 91.83% and 89.22%, respectively. We believe that the results acquired in this study will make a contribution to the classification of cancer types and support doctors in their processes of decision making. \n \nKeywords: Classification, gene-expression, RNA-Seq, DL.","PeriodicalId":210768,"journal":{"name":"New Trends and Issues Proceedings on Advances in Pure and Applied Sciences","volume":"10 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"A comparative study of different classification algorithms on RNA-Seq cancer data\",\"authors\":\"N. Şimşek, B. Haznedar, Cihan Kuzudişli\",\"doi\":\"10.18844/gjpaas.v0i12.4983\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Gene mutations are the most important reason of cancer diseases, and there are different kind of causing genes across these diseases. RNA-Seq technology enables us to allow for gathering information about many genes simultaneously; hence, RNA-Seq data can be used for cancer diagnosis and classification. In this study, RNA-Seq dataset for renal cell cancer is analysed using three different developed classification methods: random forest (RF), artificial neural network (ANN) and deep learning (DL). The genes in our dataset are related to the following cancer types: kidney renal papillary cell, kidney renal clear cell and kidney chromophore carcinomas. It suggests that the DL method gives the highest accuracy rate compared to RF and ANN for 95.15%, 91.83% and 89.22%, respectively. We believe that the results acquired in this study will make a contribution to the classification of cancer types and support doctors in their processes of decision making. \\n \\nKeywords: Classification, gene-expression, RNA-Seq, DL.\",\"PeriodicalId\":210768,\"journal\":{\"name\":\"New Trends and Issues Proceedings on Advances in Pure and Applied Sciences\",\"volume\":\"10 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-04-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"New Trends and Issues Proceedings on Advances in Pure and Applied Sciences\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.18844/gjpaas.v0i12.4983\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"New Trends and Issues Proceedings on Advances in Pure and Applied Sciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.18844/gjpaas.v0i12.4983","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A comparative study of different classification algorithms on RNA-Seq cancer data
Gene mutations are the most important reason of cancer diseases, and there are different kind of causing genes across these diseases. RNA-Seq technology enables us to allow for gathering information about many genes simultaneously; hence, RNA-Seq data can be used for cancer diagnosis and classification. In this study, RNA-Seq dataset for renal cell cancer is analysed using three different developed classification methods: random forest (RF), artificial neural network (ANN) and deep learning (DL). The genes in our dataset are related to the following cancer types: kidney renal papillary cell, kidney renal clear cell and kidney chromophore carcinomas. It suggests that the DL method gives the highest accuracy rate compared to RF and ANN for 95.15%, 91.83% and 89.22%, respectively. We believe that the results acquired in this study will make a contribution to the classification of cancer types and support doctors in their processes of decision making.
Keywords: Classification, gene-expression, RNA-Seq, DL.