DiseaseConnect: a comprehensive web server for mechanism-based disease-disease connections.
DiseaseConnect: a comprehensive web server for mechanism-based disease-disease connections.
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DOI：
10.1093/nar/gku412
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发表时间：
2014-07
影响因子：
14.9
通讯作者：
Zhou XJ
中科院分区：
文献类型：
--
作者：
Liu CC;Tseng YT;Li W;Wu CY;Mayzus I;Rzhetsky A;Sun F;Waterman M;Chen JJ;Chaudhary PM;Loscalzo J;Crandall E;Zhou XJ
DiseaseConnect（http：disease-connect.org）是一个网络服务器，用于分析和可视化基于机制的疾病连接的综合知识。传统的疾病分类系统将具有相似临床症状和表型特征的疾病分组。因此，具有完全不同病理的疾病可以被分组在一起，导致类似的治疗设计。如果根据疾病的分子机制对其进行分类，就可以避免这些问题。将疾病与相似的病理机制联系起来，可以激发对现有药物和疗法进行有效重新定位的新策略。虽然有几项研究试图生成疾病连接网络，但它们尚未利用庞大且快速增长的疾病相关组学数据和文献的公共库，这两种主要资源能够以前所未有的细节水平提供对疾病连接的见解。我们的DiseaseConnect是第一个公共网络服务器，整合了全面的组学和文献数据，包括大量的基因表达数据，全基因组关联研究目录和文本挖掘知识，通过共同的分子机制发现疾病之间的联系。此外，临床合并症数据和已知药物-疾病关系的综合汇编还用于促进对疾病状况的理解，并促进基于机制的新药治疗开发。
The DiseaseConnect (http://disease-connect.org) is a web server for analysis and visualization of a comprehensive knowledge on mechanism-based disease connectivity. The traditional disease classification system groups diseases with similar clinical symptoms and phenotypic traits. Thus, diseases with entirely different pathologies could be grouped together, leading to a similar treatment design. Such problems could be avoided if diseases were classified based on their molecular mechanisms. Connecting diseases with similar pathological mechanisms could inspire novel strategies on the effective repositioning of existing drugs and therapies. Although there have been several studies attempting to generate disease connectivity networks, they have not yet utilized the enormous and rapidly growing public repositories of disease-related omics data and literature, two primary resources capable of providing insights into disease connections at an unprecedented level of detail. Our DiseaseConnect, the first public web server, integrates comprehensive omics and literature data, including a large amount of gene expression data, Genome-Wide Association Studies catalog, and text-mined knowledge, to discover disease–disease connectivity via common molecular mechanisms. Moreover, the clinical comorbidity data and a comprehensive compilation of known drug–disease relationships are additionally utilized for advancing the understanding of the disease landscape and for facilitating the mechanism-based development of new drug treatments.
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影响因子：
9.9
作者：
通讯作者：
--
影响因子：
158.5
作者：
Rai, KR;Peterson, BL;Schiffer, CA
通讯作者：
Schiffer, CA
影响因子：
8
作者：
Peters, Charlotte P.;Eshuis, Emma J.;Ponsioen, Cyriel Y.
通讯作者：
Ponsioen, Cyriel Y.
从多关系基因和表型网络中探索和利用疾病相互作用。
影响因子：
3.7
作者：
Davis DA;Chawla NV
通讯作者：
Chawla NV
影响因子：
4.3
作者：
Hidalgo CA;Blumm N;Barabási AL;Christakis NA
通讯作者：
Christakis NA