Affiliations: [a] School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China | [b] School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China | [c] Beijing Key Laboratory of Intelligent Processing for Building Big Data, Beijing 100044, China | [d] Institute of Materials, China Academy of Engineering Physics, Mianyang, Sichuan 621907, China
Correspondence:
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Corresponding author: Donghua Yu, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China. Tel.: +86 15124504025, E-mail: [email protected].
Abstract: Since drug-target data have neither class labels nor the cluster number information, they are not suitable for clustering algorithms that require predefined parameters determined by comparing clustering results with real class labels. Density peaks clustering (DPC) is a density-based clustering algorithm that can determine the number of clusters without requiring class labels. However, the predefined cutoff distance of local density limits its wide application. Therefore, this paper proposes an improved local density method based on a cutoff distance sequence that overcomes the limitations of DPC and can be successful applied to drug-target data. We also introduce multiple-dimensional scaling based on drug and target similarity and perform intuitive graph analysis of the two most significant differentiation features. Drugs of the Enzyme, GPCR, Ion Channel, and Nuclear Receptor 4 standard datasets are identified as 6, 6, 3, and 5 clusters by an improved algorithm, respectively, and similarly, their targets are identified be 5, 5, 8, and 4 clusters. Drug-target data clustering results of the improved algorithm are more reasonable than the results of the fast K-medoids and hierarchical clustering algorithms.
