Semi-Supervised Hybrid Predictive Bi-Clustering Trees for Drug-Target Interaction Prediction

André Alves; Pedro Ilídio; Ricardo Cerri

doi:10.1145/3555776.3578606

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Semi-Supervised Hybrid Predictive Bi-Clustering Trees for Drug-Target Interaction Prediction

André Alves Pedro Ilídio Ricardo Cerri

Year: 2023 Pages: 1163-1170

DOI: 10.1145/3555776.3578606

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Abstract

sponsorship: This study was financed in part by the Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior - Brasil (CAPES) - Finance Code 001, and the Sao Paulo Research Foundation (FAPESP), grant #2020/11611-4. (Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior - Brasil (CAPES)|001, Sao Paulo Research Foundation (FAPESP)|2020/11611-4)

Keywords:

Cluster analysis Computer science Decision tree Artificial intelligence Bipartite graph Machine learning Tree (set theory) Distance matrix Data mining Graph Pattern recognition (psychology) Mathematics Algorithm Theoretical computer science

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Computational Drug Discovery Methods

Physical Sciences → Computer Science → Computational Theory and Mathematics

Text and Document Classification Technologies

Physical Sciences → Computer Science → Artificial Intelligence

Machine Learning in Bioinformatics

Life Sciences → Biochemistry, Genetics and Molecular Biology → Molecular Biology

Semi-Supervised Hybrid Predictive Bi-Clustering Trees for Drug-Target Interaction Prediction

Abstract

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