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一个药物重定位的标准数据库。

A standard database for drug repositioning.

机构信息

Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck St, Boston, Massachusetts 02115, USA.

出版信息

Sci Data. 2017 Mar 14;4:170029. doi: 10.1038/sdata.2017.29.

Abstract

Drug repositioning, the process of discovering, validating, and marketing previously approved drugs for new indications, is of growing interest to academia and industry due to reduced time and costs associated with repositioned drugs. Computational methods for repositioning are appealing because they putatively nominate the most promising candidate drugs for a given indication. Comparing the wide array of computational repositioning methods, however, is a challenge due to inconsistencies in method validation in the field. Furthermore, a common simplifying assumption, that all novel predictions are false, is intellectually unsatisfying and hinders reproducibility. We address this assumption by providing a gold standard database, repoDB, that consists of both true positives (approved drugs), and true negatives (failed drugs). We have made the full database and all code used to prepare it publicly available, and have developed a web application that allows users to browse subsets of the data (http://apps.chiragjpgroup.org/repoDB/).

摘要

药物重定位,即将已批准药物重新用于新适应症的发现、验证和推广的过程,由于重定位药物的时间和成本降低,引起了学术界和工业界的极大兴趣。药物重定位的计算方法很有吸引力,因为它们可以为特定适应症提名最有前途的候选药物。然而,由于该领域方法验证的不一致,比较广泛的计算重定位方法是一项挑战。此外,一个常见的简化假设,即所有新的预测都是错误的,在智力上并不令人满意,并阻碍了可重复性。我们通过提供一个由真正的阳性(已批准的药物)和真正的阴性(失败的药物)组成的黄金标准数据库 repoDB 来解决这个假设。我们已经公开了完整的数据库和用于准备它的所有代码,并开发了一个网络应用程序,允许用户浏览数据的子集(http://apps.chiragjpgroup.org/repoDB/)。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/66d1/5349249/ceacdab658a8/sdata201729-f1.jpg

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