Nascimento André C A, Prudêncio Ricardo B C, Costa Ivan G
Department of Computing, UFRPE, Recife, Brazil.
Center of Informatics, UFPE, Recife, Brazil.
Methods Mol Biol. 2019;1903:281-289. 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.
药物-靶点网络在药物创新、药物先导发现以及近期的药物重新定位任务中发挥着重要作用。人们提出了许多不同的计算机模拟方法来识别新的药物-靶点相互作用，其中许多方法基于一类特定的机器学习算法，即核方法。这些模式分类算法能够以相似性函数（即核）的形式纳入先前的知识，并且它们在广泛的监督学习问题中取得了成功。正确的核函数及其相应参数的选择会对分类器的性能产生很大影响。最近，为了解决这个问题，引入了多种核学习算法，使人们能够将多个核组合到大型药物-靶点相互作用空间中，以便同时整合多种生物信息源。带多核学习的克罗内克正则化最小二乘法（KronRLS-MKL）是一种机器学习算法，旨在将异构信息源整合到单个化学基因组空间中，以预测新的药物-靶点相互作用。本章描述了如何从异构源获取数据，以及如何实现和使用KronRLS-MKL来预测新的相互作用。
