Semantic Disease Gene Embeddings (SmuDGE): phenotype-based disease gene prioritization without phenotypes.
Semantic Disease Gene Embeddings (SmuDGE): phenotype-based disease gene prioritization without phenotypes.
复制标题
DOI：
10.1093/bioinformatics/bty559
复制
发表时间：
2018-09-01
期刊：
影响因子：
--
通讯作者：
Hoehndorf R
中科院分区：
文献类型：
--
作者：
Alshahrani M;Hoehndorf R
在过去的几年中，已经开发了几种方法，将表型的信息纳入计算疾病基因优先级的方法。这些方法通常计算疾病（或患者）表型与基因-表型关联数据库之间的相似性，以找到表型最相似的匹配。这些方法的一个关键限制是它们依赖于关于与特定基因相关的表型的知识，这在人类以及许多模式生物如小鼠中是高度不完整的。我们开发了SmuDGE，这是一种使用特征学习来生成与实体相关的表型的基于向量的表示的方法。SmuDGE可以用作可训练的语义相似性度量，以比较两组表型（例如疾病和基因之间，或疾病和患者之间）。更重要的是，SmuDGE可以通过交互网络为仅与表型间接相关的实体生成表型表示;为此，SmuDGE利用由多种类型的交互组成的交互网络中的背景知识。我们证明了SmuDGE可以匹配或优于基于表型的疾病基因优先级的语义相似性，并进一步显着扩展了基于表型的方法的覆盖范围，以连接的相互作用网络中的所有基因。 https://github.com/bio-ontology-research-group/SmuDGE
In the past years, several methods have been developed to incorporate information about phenotypes into computational disease gene prioritization methods. These methods commonly compute the similarity between a disease’s (or patient’s) phenotypes and a database of gene-to-phenotype associations to find the phenotypically most similar match. A key limitation of these methods is their reliance on knowledge about phenotypes associated with particular genes which is highly incomplete in humans as well as in many model organisms such as the mouse. We developed SmuDGE, a method that uses feature learning to generate vector-based representations of phenotypes associated with an entity. SmuDGE can be used as a trainable semantic similarity measure to compare two sets of phenotypes (such as between a disease and gene, or a disease and patient). More importantly, SmuDGE can generate phenotype representations for entities that are only indirectly associated with phenotypes through an interaction network; for this purpose, SmuDGE exploits background knowledge in interaction networks comprised of multiple types of interactions. We demonstrate that SmuDGE can match or outperform semantic similarity in phenotype-based disease gene prioritization, and furthermore significantly extends the coverage of phenotype-based methods to all genes in a connected interaction network. https://github.com/bio-ontology-research-group/SmuDGE
登录
查看更多内容
DOI：
10.1093/bioinformatics/btx275
发表时间：
2017-09-01
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Alshahrani M;Khan MA;Maddouri O;Kinjo AR;Queralt-Rosinach N;Hoehndorf R
通讯作者：
Hoehndorf R
影响因子：
14.9
作者：
Köhler S;Vasilevsky NA;Engelstad M;Foster E;McMurry J;Aymé S;Baynam G;Bello SM;Boerkoel CF;Boycott KM;Brudno M;Buske OJ;Chinnery PF;Cipriani V;Connell LE;Dawkins HJ;DeMare LE;Devereau AD;de Vries BB;Firth HV;Freson K;Greene D;Hamosh A;Helbig I;Hum C;Jähn JA;James R;Krause R;F Laulederkind SJ;Lochmüller H;Lyon GJ;Ogishima S;Olry A;Ouwehand WH;Pontikos N;Rath A;Schaefer F;Scott RH;Segal M;Sergouniotis PI;Sever R;Smith CL;Straub V;Thompson R;Turner C;Turro E;Veltman MW;Vulliamy T;Yu J;von Ziegenweidt J;Zankl A;Züchner S;Zemojtel T;Jacobsen JO;Groza T;Smedley D;Mungall CJ;Haendel M;Robinson PN
通讯作者：
Robinson PN
影响因子：
3.9
作者：
Amberger, Joanna;Bocchini, Carol;Hamosh, Ada
通讯作者：
Hamosh, Ada
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：
4.3
作者：
Gillis J;Pavlidis P
通讯作者：
Pavlidis P