Journal
ONCOTARGET
Volume 8, Issue 17, Pages 28169-28175Publisher
IMPACT JOURNALS LLC
DOI: 10.18632/oncotarget.15963
Keywords
cancerlectins; binomial distribution; optimal tripeptides; SVM
Categories
Funding
- Applied Basic Research Program of Sichuan Province [2015JY0100, 14JC0121]
- Scientific Research Foundation of the Education Department of Sichuan Province [11ZB122]
- Fundamental Research Funds for the Central Universities of China [ZYGX2015J144, ZYGX2015Z006]
- Program for the Top Young Innovative Talents of Higher Learning Institutions of Hebei Province [BJ2014028]
- Outstanding Youth Foundation of North China University of Science and Technology [JP201502]
- China Postdoctoral Science Foundation China [2015M582533]
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Lectins are a diverse type of glycoproteins or carbohydrate-binding proteins that have a wide distribution to various species. They can specially identify and exclusively bind to a certain kind of saccharide groups. Cancerlectins are a group of lectins that are closely related to cancer and play a major role in the initiation, survival, growth, metastasis and spread of tumor. Several computational methods have emerged to discriminate cancerlectins from non-cancerlectins, which promote the study on pathogenic mechanisms and clinical treatment of cancer. However, the predictive accuracies of most of these techniques are very limited. In this work, by constructing a benchmark dataset based on the CancerLectinDB database, a new amino acid sequence-based strategy for feature description was developed, and then the binomial distribution was applied to screen the optimal feature set. Ultimately, an SVM-based predictor was performed to distinguish cancerlectins from non-cancerlectins, and achieved an accuracy of 77.48% with AUC of 85.52% in jackknife cross-validation. The results revealed that our prediction model could perform better comparing with published predictive tools.
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