癌症是世界上最危险的疾病之一，每年导致数百万人死亡。近年来，由抗癌肽组成的药物已被用于治疗癌症，具有低副作用。因此，鉴定抗癌肽已成为研究的重点。本研究提出了一种改进的抗癌肽预测器ACP-GBDT，基于梯度提升决策树（GBDT）和序列信息。为了编码抗癌肽数据集中包含的肽序列，ACP-GBDT使用由AAIndex和SVMProt-188D组成的合并特征。在ACP-GBDT中采用GBDT来训练预测模型。独立测试和十折交叉验证表明，ACP-GBDT可以有效区分抗癌肽和非抗癌肽。基准数据集的比较结果表明，ACP-GBDT比其他现有的抗癌肽预测方法更简单、更有效。版权所有 © 2023 Li、Ma、Chen和Chen。

Cancer is one of the most dangerous diseases in the world, killing millions of people every year. Drugs composed of anticancer peptides have been used to treat cancer with low side effects in recent years. Therefore, identifying anticancer peptides has become a focus of research. In this study, an improved anticancer peptide predictor named ACP-GBDT, based on gradient boosting decision tree (GBDT) and sequence information, is proposed. To encode the peptide sequences included in the anticancer peptide dataset, ACP-GBDT uses a merged-feature composed of AAIndex and SVMProt-188D. A GBDT is adopted to train the prediction model in ACP-GBDT. Independent testing and ten-fold cross-validation show that ACP-GBDT can effectively distinguish anticancer peptides from non-anticancer ones. The comparison results of the benchmark dataset show that ACP-GBDT is simpler and more effective than other existing anticancer peptide prediction methods.Copyright © 2023 Li, Ma, Chen and Chen.