Application of network link prediction in drug discovery

Background Technological and research advances have produced large volumes of biomedical data. When represented as a network (graph), these data become useful for modeling entities and interactions in biological and similar complex systems. In the field of network biology and network medicine, there...

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Veröffentlicht in:	BMC bioinformatics Jg. 22; H. 1; S. 187 - 21
Hauptverfasser:	Abbas, Khushnood, Abbasi, Alireza, Dong, Shi, Niu, Ling, Yu, Laihang, Chen, Bolun, Cai, Shi-Min, Hasan, Qambar
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London BioMed Central 12.04.2021 Springer Nature B.V BMC
Schlagworte:	Algorithms Bioinformatics Biomedical and Life Sciences Biomedical data Clinical trials Complex systems Computational Biology/Bioinformatics Computer Appl. in Life Sciences Computer applications Data-driven drug discovery Datasets Deep learning Disease Drug Discovery Drug Interactions Drug-target prediction Drugs Gene expression Graph representations Graphical representations Learning algorithms Life Sciences Ligands Machine Learning Machine Learning and Artificial Intelligence in Bioinformatics Microarrays Model testing Network analysis Network link prediction Neural networks Performance evaluation Pharmaceuticals Poly-pharmacy Poly-pharmacy side effects prediction Predictions Protein interaction Proteins R&D Research & development Research Article Therapeutic targets Poly-pharmacy Drug-target prediction Network link prediction Poly-pharmacy side effects prediction Data-driven drug discovery
ISSN:	1471-2105, 1471-2105
Online-Zugang:	Volltext
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