Cited by 94Machine Learning in BioinformaticsProtein Structure and Dynamicsvaccines and immunoinformatics approaches

Sequence-based predictive modeling to identify cancerlectins

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Lai, Hong-Yan, Chen, Xin-Xin, Chen, Wei, Tang, Hua, Lin, Hao·2017·Oncotarget

Abstract

// Hong-Yan Lai 1 , Xin-Xin Chen 1 , Wei Chen 1, 2 , Hua Tang 3 , Hao Lin 1 1 Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China 2 Department of Physics, School of Sciences, and Center for Genomics and Computational Biology, North China University of Science and Technology, Tangshan, Tangshan, China 3 Department of Pathophysiology, Southwest Medical University, Luzhou, China Correspondence to: Hua Tang, email: Tanghua771211@aliyun.com Hao Lin, email: hlin@uestc.edu.cn Keywords: cancerlectins, binomial distribution, optimal tripeptides, SVM Received: January 18, 2017 Accepted: February 24, 2017 Published: March 07, 2017 ABSTRACT 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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Authors
Lai, H.-Y., Chen, X.-X., Chen, W., Tang, H., & Lin, H.
Affiliations
  • University of Electronic Science and Technology of China (CN)
  • Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
  • North China University of Science and Technology (CN)
  • Southwest Medical University (CN)
Corresponding author
Lin, Hao
Venue
Oncotarget
Published
2017-03-07
Volume
8
Issue
17
Pages
28169-28175
ISSN
1949-2553 (ISSN-L)
OpenAlex
W2591975816
Cited by
94
References
57
Citations by year
2024: 1 · 2022: 2 · 2021: 3 · 2020: 11 · 2019: 23 · 2018: 50 · 2017: 4
FWCI
9.82
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Oncotarget (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Funders
  • Education Department of Sichuan Province
  • Applied Basic Research Program of Sichuan Province (2015JY0100, 14JC0121)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
Keywords
amino acid sequence features, support vector classifier, carbohydrate-binding proteins, tumor markers
MeSH terms
Algorithms, Amino Acid Sequence, Humans, Neoplasms, ROC Curve, Reproducibility of Results, Databases, Protein, Lectins, Support Vector Machine

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