Accurate Identification of Cancerlectins through Hybrid Machine Learning Technology
Joint Authors
Zou, Quan
Lu, Huijuan
Xuan, Ping
Zhang, Jieru
Ju, Ying
Source
International Journal of Genomics
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-07-13
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Zhang, Jieru& Ju, Ying& Lu, Huijuan& Xuan, Ping& Zou, Quan. 2016. Accurate Identification of Cancerlectins through Hybrid Machine Learning Technology. International Journal of Genomics،Vol. 2016, no. 2016, pp.1-11.
https://search.emarefa.net/detail/BIM-1106182
Modern Language Association (MLA)
Zhang, Jieru…[et al.]. Accurate Identification of Cancerlectins through Hybrid Machine Learning Technology. International Journal of Genomics No. 2016 (2016), pp.1-11.
https://search.emarefa.net/detail/BIM-1106182
American Medical Association (AMA)
Zhang, Jieru& Ju, Ying& Lu, Huijuan& Xuan, Ping& Zou, Quan. Accurate Identification of Cancerlectins through Hybrid Machine Learning Technology. International Journal of Genomics. 2016. Vol. 2016, no. 2016, pp.1-11.
https://search.emarefa.net/detail/BIM-1106182
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1106182