{"title":"建立疾病的分子分类学","authors":"Jisoo Park, Benjamin J. Hescott, D. Slonim","doi":"10.1145/3107411.3108236","DOIUrl":null,"url":null,"abstract":"The advent of high throughput technologies contributes to the rapid growth of molecular-level knowledge about human disease. However, existing disease taxonomies tend to focus on either physiological characterizations of disease or the organizational and billing needs of hospitals. Most fail to fully incorporate our rapidly increasing knowledge about molecular causes of disease. More modern disease taxonomies would presumably be built based on the combination of clinical, physiological, and molecular data. In this study, we analyzed our ability to infer disease relationships from molecular data alone. This approach may provide insights into how to ultimately build more modern taxonomies of disease","PeriodicalId":246388,"journal":{"name":"Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics","volume":"6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Building a Molecular Taxonomy of Disease\",\"authors\":\"Jisoo Park, Benjamin J. Hescott, D. Slonim\",\"doi\":\"10.1145/3107411.3108236\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The advent of high throughput technologies contributes to the rapid growth of molecular-level knowledge about human disease. However, existing disease taxonomies tend to focus on either physiological characterizations of disease or the organizational and billing needs of hospitals. Most fail to fully incorporate our rapidly increasing knowledge about molecular causes of disease. More modern disease taxonomies would presumably be built based on the combination of clinical, physiological, and molecular data. In this study, we analyzed our ability to infer disease relationships from molecular data alone. This approach may provide insights into how to ultimately build more modern taxonomies of disease\",\"PeriodicalId\":246388,\"journal\":{\"name\":\"Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics\",\"volume\":\"6 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-08-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"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/10.1145/3107411.3108236\",\"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/10.1145/3107411.3108236","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The advent of high throughput technologies contributes to the rapid growth of molecular-level knowledge about human disease. However, existing disease taxonomies tend to focus on either physiological characterizations of disease or the organizational and billing needs of hospitals. Most fail to fully incorporate our rapidly increasing knowledge about molecular causes of disease. More modern disease taxonomies would presumably be built based on the combination of clinical, physiological, and molecular data. In this study, we analyzed our ability to infer disease relationships from molecular data alone. This approach may provide insights into how to ultimately build more modern taxonomies of disease