保险丝(电气)
计算机科学
判别式
概率逻辑
特征(语言学)
人工智能
鉴定(生物学)
机器学习
代表(政治)
标杆管理
班级(哲学)
模式识别(心理学)
水准点(测量)
数据挖掘
工程类
哲学
大地测量学
业务
电气工程
政治
营销
生物
法学
地理
植物
语言学
政治学
作者
Bing Rao,Chen Zhou,Guoying Zhang,Ran Su,Leyi Wei
摘要
Abstract Fast and accurate identification of the peptides with anticancer activity potential from large-scale proteins is currently a challenging task. In this study, we propose a new machine learning predictor, namely, ACPred-Fuse, that can automatically and accurately predict protein sequences with or without anticancer activity in peptide form. Specifically, we establish a feature representation learning model that can explore class and probabilistic information embedded in anticancer peptides (ACPs) by integrating a total of 29 different sequence-based feature descriptors. In order to make full use of various multiview information, we further fused the class and probabilistic features with handcrafted sequential features and then optimized the representation ability of the multiview features, which are ultimately used as input for training our prediction model. By comparing the multiview features and existing feature descriptors, we demonstrate that the fused multiview features have more discriminative ability to capture the characteristics of ACPs. In addition, the information from different views is complementary for the performance improvement. Finally, our benchmarking comparison results showed that the proposed ACPred-Fuse is more precise and promising in the identification of ACPs than existing predictors. To facilitate the use of the proposed predictor, we built a web server, which is now freely available via http://server.malab.cn/ACPred-Fuse.
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