Accurate Identification of Cancerlectins through Hybrid Machine Learning Technology. (13th July 2016)

• Record Type: Journal Article
• Authors: Zhang, Jieru; Ju, Ying; Lu, Huijuan; Xuan, Ping; Zou, Quan
• Other Names: Ma Qin Academic Editor.
• Is Part Of: International journal of genomics. Volume 2016(2016)
• Publication Date: 2016-07-13
• Subjects: Genomes -- Periodicals; Genomics -- Periodicals; Cytogenetics -- Periodicals; Genomics; Genome; Molecular Biology; Cytogenetics; Genomes; Genomics; Periodicals; 572.86
• DOI: 10.1155/2016/7604641
• Languages: English
• ISSNs: 2314-436X

Abstract

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.