文献类型：期刊文献

英文题名：ACP-MLC: A two-level prediction engine for identification of anticancer peptides and multi-label classification of their functional types

作者：Deng, Hua[1];Ding, Meng[1];Wang, Yimeng[1];Li, Weihua[1];Liu, Guixia[1];Tang, Yun[1]

机构：[1]East China Univ Sci & Technol, Shanghai Frontiers Sci Ctr Optogenet Tech Cell Met, Sch Pharm, Shanghai 200237, Peoples R China

年份：2023

卷号：158

外文期刊名：COMPUTERS IN BIOLOGY AND MEDICINE

收录：;EI(收录号：20231513877166);WOS:【SCI-EXPANDED(收录号:WOS:001007209100001)】；

基金：Acknowledgments This work was supported by the National Key Research and Devel-opment Program of China (Grant 2019YFA0904800) , the National Natural Science Foundation of China (Grants 81872800, 82173746, and 82104066) , and Shanghai Frontiers Science Center of OptogeneticTechniques for Cell Metabolism (Shanghai Municipal Education Com-mission, Grant 2021 Sci & Tech 03-28) .

语种：英文

外文关键词：Anticancer peptides; Machine learning; Multi -label learning; Sequence analysis; Feature selection

摘要：Anticancer peptides (ACPs), a series of short bioactive peptides, are promising candidates in fighting against cancer due to their high activity, low toxicity, and not likely cause drug resistance. The accurate identification of ACPs and classification of their functional types is of great importance for investigating their mechanisms of action and developing peptide-based anticancer therapies. Here, we provided a computational tool, called ACP-MLC, to address binary classification and multi-label classification of ACPs for a given peptide sequence. Briefly, ACP-MLC is a two-level prediction engine, in which the 1st-level model predicts whether a query sequence is an ACP or not by random forest algorithm, and the 2nd-level model predicts which tissue types the sequence might target by the binary relevance algorithm. Development and evaluation by high-quality datasets, our ACP-MLC yielded an area under the receiver operating characteristic curve (AUC) of 0.888 on the independent test set for the 1st-level prediction, and obtained 0.157 hamming loss, 0.577 subset accuracy, 0.802 F1-scoremacro, and 0.826 F1-scoremicro on the independent test set for the 2nd-level prediction. A systematic comparison demon-strated that ACP-MLC outperformed existing binary classifiers and other multi-label learning classifiers for ACP prediction. Finally, we interpreted the important features of ACP-MLC by the SHAP method. User-friendly software and the datasets are available at https://github.com/Nicole-DH/ACP-MLC. We believe that the ACP-MLC would be a powerful tool in ACP discovery.