{"title":"用支持向量机和文本处理表示细胞线条","authors":"I. Carrera, I. Dutra, E. Tejera","doi":"10.1145/3388440.3414912","DOIUrl":null,"url":null,"abstract":"A main problem for predicting cell line interactions with chemical compounds is the lack of a computational representation for cell lines. We describe a method for characterizing cell lines from scientific literature. We retrieve and process cell line-related scientific papers, perform a document classification algorithm, and then obtain a description of the information space of each cell line. We have successfully characterized a set of 300+ cell lines.","PeriodicalId":411338,"journal":{"name":"Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics","volume":"10 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Representing Cellular Lines with SVM and Text Processing\",\"authors\":\"I. Carrera, I. Dutra, E. Tejera\",\"doi\":\"10.1145/3388440.3414912\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A main problem for predicting cell line interactions with chemical compounds is the lack of a computational representation for cell lines. We describe a method for characterizing cell lines from scientific literature. We retrieve and process cell line-related scientific papers, perform a document classification algorithm, and then obtain a description of the information space of each cell line. We have successfully characterized a set of 300+ cell lines.\",\"PeriodicalId\":411338,\"journal\":{\"name\":\"Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics\",\"volume\":\"10 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-09-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3388440.3414912\",\"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 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3388440.3414912","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Representing Cellular Lines with SVM and Text Processing
A main problem for predicting cell line interactions with chemical compounds is the lack of a computational representation for cell lines. We describe a method for characterizing cell lines from scientific literature. We retrieve and process cell line-related scientific papers, perform a document classification algorithm, and then obtain a description of the information space of each cell line. We have successfully characterized a set of 300+ cell lines.
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