title: Accurate Identification of Cancerlectins through Hybrid Machine Learning Technology.
abstract: Cancerlectins are cancer-related proteins that function as lectins. They have been identified through computational identification techniques, but these techniques have sometimes failed to identify proteins because of sequence diversity among the cancerlectins. Advanced machine learning identification methods, such as support vector machine and basic sequence features (n-gram), have also been used to identify cancerlectins. In this study, various protein fingerprint features and advanced classifiers, including ensemble learning techniques, were utilized to identify this group of proteins. We improved the prediction accuracy of the original feature extraction methods and classification algorithms by more than 10% on average. Our work provides a basis for the computational identification of cancerlectins and reveals the power of hybrid machine learning techniques in computational proteomics.
authors.affiliation: School of Software, Tianjin University, Tianjin, China; School of Computer Science and Technology, Tianjin University, Tianjin, China.
authors.forename: Jieru
authors.identifier: 0000-0003-4926-4149
authors.initials: J
authors.lastname: Zhang
authors.affiliation: School of Information Science and Technology, Xiamen University, Xiamen, China.
authors.forename: Ying
authors.initials: Y
authors.lastname: Ju
authors.affiliation: College of Information Engineering, China Jiliang University, Hangzhou, Zhejiang, China.
authors.forename: Huijuan
authors.identifier: 0000-0002-3698-9653
authors.initials: H
authors.lastname: Lu
authors.affiliation: School of Computer Science and Technology, Heilongjiang University, Harbin, China.
authors.forename: Ping
authors.identifier: 0000-0001-5328-691X
authors.initials: P
authors.lastname: Xuan
authors.affiliation: School of Computer Science and Technology, Tianjin University, Tianjin, China; State Key Laboratory of Medicinal Chemical Biology, NanKai University, Tianjin, China.
authors.forename: Quan
authors.identifier: 0000-0001-6406-1142
authors.initials: Q
authors.lastname: Zou