Analysis of the human diseasome using phenotype similarity between common, genetic, and infectious diseases.
Analysis of the human diseasome using phenotype similarity between common, genetic, and infectious diseases.
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DOI：
10.1038/srep10888
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发表时间：
2015-06-08
影响因子：
4.6
通讯作者：
Gkoutos GV
中科院分区：
文献类型：
--
作者：
Hoehndorf R;Schofield PN;Gkoutos GV
表型是生物体对环境的反应所产生的可观察到的特征。与工程和天然遗传变异相关的表型使用表型本体在模式生物中被广泛记录，人类孟德尔疾病的体征和症状也在数据库如OMIM和Orphanet中被广泛记录。利用这些资源，已经开发了几种计算方法，用于整合和分析表型数据，以确定疾病的遗传病因或提出合理的干预措施。类似的资源不仅对罕见疾病和孟德尔疾病非常有用，而且对常见、复杂和传染性疾病也非常有用。我们应用语义文本挖掘方法来识别与6,000多种疾病相关的表型（体征和症状）。我们评估我们的文本挖掘表型，证明他们可以正确地识别已知的疾病相关基因在小鼠和人类具有高准确性。使用表型相似性度量，我们生成了一个人类疾病网络，其中具有相似体征和症状的疾病聚集在一起，我们使用这个网络来识别基于共同病因学，解剖学和生理学基础的密切相关的疾病。
Phenotypes are the observable characteristics of an organism arising from its response to the environment. Phenotypes associated with engineered and natural genetic variation are widely recorded using phenotype ontologies in model organisms, as are signs and symptoms of human Mendelian diseases in databases such as OMIM and Orphanet. Exploiting these resources, several computational methods have been developed for integration and analysis of phenotype data to identify the genetic etiology of diseases or suggest plausible interventions. A similar resource would be highly useful not only for rare and Mendelian diseases, but also for common, complex and infectious diseases. We apply a semantic text-mining approach to identify the phenotypes (signs and symptoms) associated with over 6,000 diseases. We evaluate our text-mined phenotypes by demonstrating that they can correctly identify known disease-associated genes in mice and humans with high accuracy. Using a phenotypic similarity measure, we generate a human disease network in which diseases that have similar signs and symptoms cluster together, and we use this network to identify closely related diseases based on common etiological, anatomical as well as physiological underpinnings.
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影响因子：
5.8
作者：
Hoehndorf, Robert;Dumontier, Michel;Gkoutos, Georgios V.
通讯作者：
Gkoutos, Georgios V.
影响因子：
3.9
作者：
Amberger, Joanna;Bocchini, Carol;Hamosh, Ada
通讯作者：
Hamosh, Ada
影响因子：
--
作者：
KOPF, AW;MORRILL, SD;SILBERBERG, I
通讯作者：
SILBERBERG, I
影响因子：
14.9
作者：
Köhler S;Doelken SC;Mungall CJ;Bauer S;Firth HV;Bailleul-Forestier I;Black GC;Brown DL;Brudno M;Campbell J;FitzPatrick DR;Eppig JT;Jackson AP;Freson K;Girdea M;Helbig I;Hurst JA;Jähn J;Jackson LG;Kelly AM;Ledbetter DH;Mansour S;Martin CL;Moss C;Mumford A;Ouwehand WH;Park SM;Riggs ER;Scott RH;Sisodiya S;Van Vooren S;Wapner RJ;Wilkie AO;Wright CF;Vulto-van Silfhout AT;de Leeuw N;de Vries BB;Washingthon NL;Smith CL;Westerfield M;Schofield P;Ruef BJ;Gkoutos GV;Haendel M;Smedley D;Lewis SE;Robinson PN
通讯作者：
Robinson PN
影响因子：
56.9
作者：
Campillos, Monica;Kuhn, Michael;Bork, Peer
通讯作者：
Bork, Peer