治疗目标预测中网络建模的一些观点。

IF 2.3 Q3 ENGINEERING, BIOMEDICAL
Biomedical Engineering and Computational Biology Pub Date : 2013-02-21 eCollection Date: 2013-01-01 DOI:10.4137/BECB.S10793
Reka Albert, Bhaskar DasGupta, Nasim Mobasheri
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引用次数: 0

摘要

药物靶点识别对制药公司来说具有重大的商业利益,与治疗靶点识别相关的研究成果也不胜枚举。这一领域的跨学科研究涉及生物网络社区和图算法社区。典型的治疗目标识别问题的关键步骤包括综合或推断与疾病相关的复杂相互作用网络,将该网络与疾病的特定行为联系起来,并预测哪些成分是该行为的关键媒介。所有这些步骤都涉及图论或图算法方面。在这一视角中,我们将从建模和算法的角度来探讨治疗目标的识别,并重点介绍迄今为止关注较少的一些算法进展,希望能加强这两个研究领域之间的联系。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Some perspectives on network modeling in therapeutic target prediction.

Some perspectives on network modeling in therapeutic target prediction.

Some perspectives on network modeling in therapeutic target prediction.

Some perspectives on network modeling in therapeutic target prediction.

Drug target identification is of significant commercial interest to pharmaceutical companies, and there is a vast amount of research done related to the topic of therapeutic target identification. Interdisciplinary research in this area involves both the biological network community and the graph algorithms community. Key steps of a typical therapeutic target identification problem include synthesizing or inferring the complex network of interactions relevant to the disease, connecting this network to the disease-specific behavior, and predicting which components are key mediators of the behavior. All of these steps involve graph theoretical or graph algorithmic aspects. In this perspective, we provide modelling and algorithmic perspectives for therapeutic target identification and highlight a number of algorithmic advances, which have gotten relatively little attention so far, with the hope of strengthening the ties between these two research communities.

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