建立疾病的分子分类学

Jisoo Park, Benjamin J. Hescott, D. Slonim
{"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}
引用次数: 0

摘要

高通量技术的出现促进了人类疾病分子水平知识的快速增长。然而,现有的疾病分类法倾向于关注疾病的生理特征或医院的组织和计费需求。大多数都没有充分考虑到我们对疾病分子成因的快速增长的知识。更现代的疾病分类大概会建立在临床、生理和分子数据的基础上。在这项研究中,我们分析了仅从分子数据推断疾病关系的能力。这种方法可能为如何最终建立更现代的疾病分类提供见解
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Building a Molecular Taxonomy of Disease
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
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Mapping Free Text into MedDRA by Natural Language Processing: A Modular Approach in Designing and Evaluating Software Extensions Evolving Conformation Paths to Model Protein Structural Transitions Supervised Machine Learning Approaches Predict and Characterize Nanomaterial Exposures: MWCNT Markers in Lung Lavage Fluid. Geometry Analysis for Protein Secondary Structures Matching Problem Geometric Sampling Framework for Exploring Molecular Walker Energetics and Dynamics
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1