文献类型：期刊文献

英文题名：Drug-target interactions prediction based on network topology feature representation embedded deep forest

作者：Lian, Majun[1];Wang, Xinjie[1,2];Du, Wenli[1,2]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China

年份：2023

卷号：551

外文期刊名：NEUROCOMPUTING

收录：;EI(收录号：20232914400552);WOS:【SCI-EXPANDED(收录号:WOS:001047898100001)】；

基金：Acknowledgments This work was supported by the National Natural Science Foun-dation of China (Basic Science Center Program: 61988101) , National Natural Science Fund for Distinguished Young Scholars (61725301) , Fundamental Research Funds for the Central Universi-ties (222202117006) and Shanghai Sailing Program (21YF1409900) .

语种：英文

外文关键词：Drug-target interactions; Low-dimensional feature representation; Composite similarities; Topological difference; Deep forest

摘要：Identifying drug-target interactions (DTIs) is instructive in drug design and disease treatment. Existing studies typically used the properties of nodes (drug chemical structure and protein sequence) to con-struct drug and target features while ignoring the influence of network topology information on the pre-diction of DTIs. In this study, a hybrid computation model is proposed to predict DTIs based on the network topological feature representation embedded the deep forest model (NTFRDF). The main idea is to capture the topological differences by learning the low-dimensional feature representation of drugs and targets from the heterogeneous network. In addition, the multi-similarity fusion strategy is proposed to mine hidden useful information in the known DTIs from multi-view to enrich network features of the heterogeneous network. Based on the deep forest framework, the performance of the proposed method is examined on four benchmark datasets. Our experimental results verify that the proposed method is com-petitive compared with some existing DTIs prediction models.& COPY; 2023 Elsevier B.V. All rights reserved.