Some Perspectives on Network Modeling in Therapeutic Target Prediction:

Biomedical Engineering and Computational Biology

RekaAlbert¹ 

关键词: drug target identification;    complex network of interactions;    Boolean models;    signal transduction networks;    transitive reduction;    node essentiality;   

DOI  :  10.4137/BECB.S10793

学科分类：工程和技术（综合）

来源: Sage Journals

【 摘 要 】

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.

【 授权许可】

CC BY