Linear and Kernel Model Construction Methods for Predicting Drug-Target Interactions in a Chemogenomic Framework.
Linear and Kernel Model Construction Methods for Predicting Drug-Target Interactions in a Chemogenomic Framework.
复制标题
在化学基因组框架中预测药物-靶点相互作用的线性和核模型构建方法。
DOI：
10.1007/978-1-4939-8639-2_12
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Yamanishi Y.
中科院分区：
文献类型：
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作者：
Sawada R;Iwata M;Umezaki M;Usui Y;Kobayashi T;Kubono T;Hayashi S;Kadowaki M;Yamanishi Y.;須藤 信行;Yamanishi Y.
药物-靶标相互作用的识别是药物发现的关键过程。在这一章中，我们提出了机器学习方法的最新进展，用于在化学基因组学框架中从异质生物数据预测药物-靶标相互作用，其中预测是基于候选药物化合物的化学结构数据和靶候选蛋白的翻译基因组序列数据。大多数现有的方法是基于线性建模或核建模。为了说明线性建模，我们介绍了稀疏诱导的二进制分类器和稀疏典型相关分析。为了说明内核建模，我们介绍了成对的基于内核的支持向量机和基于内核的远程学习。使用这些技术的工作流程。我们还讨论了每种方法的特点，并提出了一些未来的研究方向。
Identification of drug–target interactions is a crucial process in drug discovery. In this chapter, we present protocols for recent advancements in machine learning methods for predicting drug–target interactions from heterogeneous biological data in a chemogenomic framework, in which prediction is based on the chemical structure data of drug candidate compounds and translated genomic sequence data of target candidate proteins. Most existing methods are based on either linear modeling or kernel modeling. To illustrate linear modeling, we introduce sparsity-induced binary classifiers and sparse canonical correlation analysis. To illustrate kernel modeling, we introduce pairwise kernel-based support vector machines and kernel-based distance learning. Workflows for using these techniques are presented. We also discuss the characteristics of each method and suggest some directions for future research.