Thodoris Koutsandreas, Kalliopi Tsafou, Heiko Horn, Ian Barrett, Evangelia Petsalaki
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引用次数: 0
Abstract
Drug target identification is the first step in drug development, and its importance is underscored by the fact that, even when using genetic evidence to improve success rates, only a small fraction of lead targets end up approved for use in the clinic. One of the reasons for this is the lack of in-depth understanding of the complexity of human diseases.In this review we argue that network-based approaches, which are able to capture relationships between relevant genes and proteins, and diverse data modalities have high potential for improving drug target identification and drug repurposing. We present the evolution of network-based methods that have been developed for this purpose and discuss the limitations of these approaches that are holding them back from making an impact in the clinic. We finish by presenting our recommendations for overcoming these limitations, for example, by leveraging emerging technologies such as artificial intelligence and knowledge graphs.
期刊介绍：
The Annual Review of Biomedical Data Science provides comprehensive expert reviews in biomedical data science, focusing on advanced methods to store, retrieve, analyze, and organize biomedical data and knowledge. The scope of the journal encompasses informatics, computational, artificial intelligence (AI), and statistical approaches to biomedical data, including the sub-fields of bioinformatics, computational biology, biomedical informatics, clinical and clinical research informatics, biostatistics, and imaging informatics. The mission of the journal is to identify both emerging and established areas of biomedical data science, and the leaders in these fields.
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