期刊
BRIEFINGS IN BIOINFORMATICS
卷 22, 期 3, 页码 -出版社
OXFORD UNIV PRESS
DOI: 10.1093/bib/bbaa118
关键词
pharmacogenomics; network fusion; drug discovery; multi-modal study
资金
- Research Grants Council of the Hong Kong Special Administrative Region [CityU 11203217, CityU 11200218]
- Health andMedical Research Fund, the Food andHealth Bureau, the Government of the Hong Kong Special Administrative Region [07181426]
- Hong Kong Institute for Data Science (HKIDS) at City University of Hong Kong
- City University of Hong Kong [CityU 11202219]
A novel network integration approach RNCE and a tailor-made clustering algorithm were developed for drug community detection, outperforming state-of-the-art approaches in tests on drug networks. The observed improvement of RNCE has potential contributions to drug discovery and integrative studies.
Mining drug targets and mechanisms of action (MoA) for novel anticancer drugs from pharmacogenomic data is a path to enhance the drug discovery efficiency. Recent approaches have successfully attempted to discover targets/MoA by characterizing drug similarities and communities with integrative methods on multi-modal or multi-omics drug information. However, the sparse and imbalanced community size structure of the drug network is seldom considered in recent approaches. Consequently, we developed a novel network integration approach accounting for network structure by a reciprocal nearest neighbor and contextual information encoding (RNCE) approach. In addition, we proposed a tailor-made clustering algorithm to perform drug community detection on drug networks. RNCE and spectral clustering are proved to outperform state-of-the-art approaches in a series of tests, including network similarity tests and community detection tests on two drug databases. The observed improvement of RNCE can contribute to the field of drug discovery and the related multi-modal/multi-omics integrative studies.
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