Accurate Identification of Cancerlectins through Hybrid Machine Learning Technology
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 identificati...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2016-01-01
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| Series: | International Journal of Genomics |
| Online Access: | http://dx.doi.org/10.1155/2016/7604641 |
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| _version_ | 1832551587906584576 |
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| author | Jieru Zhang Ying Ju Huijuan Lu Ping Xuan Quan Zou |
| author_facet | Jieru Zhang Ying Ju Huijuan Lu Ping Xuan Quan Zou |
| author_sort | Jieru Zhang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-15460e1c271a4dd7b3bfa38caa36dbf0 |
| institution | Kabale University |
| issn | 2314-436X 2314-4378 |
| language | English |
| publishDate | 2016-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Genomics |
| spelling | doaj-art-15460e1c271a4dd7b3bfa38caa36dbf02025-02-03T06:01:03ZengWileyInternational Journal of Genomics2314-436X2314-43782016-01-01201610.1155/2016/76046417604641Accurate Identification of Cancerlectins through Hybrid Machine Learning TechnologyJieru Zhang0Ying Ju1Huijuan Lu2Ping Xuan3Quan Zou4School of Software, Tianjin University, Tianjin, ChinaSchool of Information Science and Technology, Xiamen University, Xiamen, ChinaCollege of Information Engineering, China Jiliang University, Hangzhou, Zhejiang, ChinaSchool of Computer Science and Technology, Heilongjiang University, Harbin, ChinaSchool of Computer Science and Technology, Tianjin University, Tianjin, ChinaCancerlectins 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.http://dx.doi.org/10.1155/2016/7604641 |
| spellingShingle | Jieru Zhang Ying Ju Huijuan Lu Ping Xuan Quan Zou Accurate Identification of Cancerlectins through Hybrid Machine Learning Technology International Journal of Genomics |
| title | Accurate Identification of Cancerlectins through Hybrid Machine Learning Technology |
| title_full | Accurate Identification of Cancerlectins through Hybrid Machine Learning Technology |
| title_fullStr | Accurate Identification of Cancerlectins through Hybrid Machine Learning Technology |
| title_full_unstemmed | Accurate Identification of Cancerlectins through Hybrid Machine Learning Technology |
| title_short | Accurate Identification of Cancerlectins through Hybrid Machine Learning Technology |
| title_sort | accurate identification of cancerlectins through hybrid machine learning technology |
| url | http://dx.doi.org/10.1155/2016/7604641 |
| work_keys_str_mv | AT jieruzhang accurateidentificationofcancerlectinsthroughhybridmachinelearningtechnology AT yingju accurateidentificationofcancerlectinsthroughhybridmachinelearningtechnology AT huijuanlu accurateidentificationofcancerlectinsthroughhybridmachinelearningtechnology AT pingxuan accurateidentificationofcancerlectinsthroughhybridmachinelearningtechnology AT quanzou accurateidentificationofcancerlectinsthroughhybridmachinelearningtechnology |