A Drug-Target Network-Based Supervised Machine Learning Repurposing Method Allowing the Use of Multiple Heterogeneous Information Sources

• Published in: Methods in molecular biology (Clifton, N.J.) Vol. 1903; p. 281
• Main Authors: Nascimento, André C A, Prudêncio, Ricardo B C, Costa, Ivan G
• Format: Journal Article
• Language: English
• Published: United States 01.01.2019
• Subjects: Kernel methods; Multiple kernel learning; Supervised machine learning; Drug discovery
• ISSN: 1940-6029
• DOI: 10.1007/978-1-4939-8955-3_17

Drug-target networks have an important role in pharmaceutical innovation, drug lead discovery, and recent drug repositioning tasks. Many different in silico approaches for the identification of new drug-target interactions have been proposed, many of them based on a particular class of machine learning algorithms called kernel methods. These pattern classification algorithms are able to incorporate previous knowledge in the form of similarity functions, i.e., a kernel, and they have been successful in a wide range of supervised learning problems. The selection of the right kernel function and its respective parameters can have a large influence on the performance of the classifier. Recently, multiple kernel learning algorithms have been introduced to address this problem, enabling one to combine multiple kernels into large drug-target interaction spaces in order to integrate multiple sources of biological information simultaneously. The Kronecker regularized least squares with multiple kernel learning (KronRLS-MKL) is a machine learning algorithm that aims at integrating heterogeneous information sources into a single chemogenomic space to predict new drug-target interactions. This chapter describes how to obtain data from heterogeneous sources and how to implement and use KronRLS-MKL to predict new interactions.