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Sequence-Based Prediction of microRNA-Binding Residues in Proteins Using Cost-Sensitive Laplacian Support Vector Machines

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Zhi-Hua ZhouAuthors Info & Claims

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), Volume 10, Issue 3

Pages 752 - 759

https://doi.org/10.1109/TCBB.2013.75

Published: 01 May 2013 Publication History

Abstract

The recognition of microRNA (miRNA)-binding residues in proteins is helpful to understand how miRNAs silence their target genes. It is difficult to use existing computational method to predict miRNA-binding residues in proteins due to the lack of training examples. To address this issue, unlabeled data may be exploited to help construct a computational model. Semisupervised learning deals with methods for exploiting unlabeled data in addition to labeled data automatically to improve learning performance, where no human intervention is assumed. In addition, miRNA-binding proteins almost always contain a much smaller number of binding than nonbinding residues, and cost-sensitive learning has been deemed as a good solution to the class imbalance problem. In this work, a novel model is proposed for recognizing miRNA-binding residues in proteins from sequences using a cost-sensitive extension of Laplacian support vector machines (CS-LapSVM) with a hybrid feature. The hybrid feature consists of evolutionary information of the amino acid sequence (position-specific scoring matrices), the conservation information about three biochemical properties (HKM) and mutual interaction propensities in protein-miRNA complex structures. The CS-LapSVM receives good performance with an F1 score of $(26.23 \pm 2.55\%)$ and an AUC value of $(0.805 \pm 0.020)$ superior to existing approaches for the recognition of RNA-binding residues. A web server called SARS is built and freely available for academic usage.

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Cited By

Zhu YTan MWei J(2022)Robust Multi-view Classification with Sample ConstraintsNeural Processing Letters10.1007/s11063-021-10483-054:4(2589-2612)Online publication date: 1-Aug-2022
https://dl.acm.org/doi/10.1007/s11063-021-10483-0
Gan JLi JXie Y(2022)Robust SVM for Cost-Sensitive LearningNeural Processing Letters10.1007/s11063-021-10480-354:4(2737-2758)Online publication date: 1-Aug-2022
https://dl.acm.org/doi/10.1007/s11063-021-10480-3
Hu RZhu XZhu YGan J(2019)Robust SVM with adaptive graph learningWorld Wide Web10.1007/s11280-019-00766-x23:3(1945-1968)Online publication date: 27-Dec-2019
https://dl.acm.org/doi/10.1007/s11280-019-00766-x
Show More Cited By

Sequence-Based Prediction of microRNA-Binding Residues in Proteins Using Cost-Sensitive Laplacian Support Vector Machines
1. Applied computing
  1. Life and medical sciences
2. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning

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cover image IEEE/ACM Transactions on Computational Biology and Bioinformatics

IEEE/ACM Transactions on Computational Biology and Bioinformatics Volume 10, Issue 3

May 2013

272 pages

ISSN:1545-5963

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IEEE Computer Society Press

Washington, DC, United States

Publication History

Published: 01 May 2013

Published in TCBB Volume 10, Issue 3

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Cited By

Zhu YTan MWei J(2022)Robust Multi-view Classification with Sample ConstraintsNeural Processing Letters10.1007/s11063-021-10483-054:4(2589-2612)Online publication date: 1-Aug-2022
https://dl.acm.org/doi/10.1007/s11063-021-10483-0
Gan JLi JXie Y(2022)Robust SVM for Cost-Sensitive LearningNeural Processing Letters10.1007/s11063-021-10480-354:4(2737-2758)Online publication date: 1-Aug-2022
https://dl.acm.org/doi/10.1007/s11063-021-10480-3
Hu RZhu XZhu YGan J(2019)Robust SVM with adaptive graph learningWorld Wide Web10.1007/s11280-019-00766-x23:3(1945-1968)Online publication date: 27-Dec-2019
https://dl.acm.org/doi/10.1007/s11280-019-00766-x
Wu HTang YLu WChen CHuang HFu Q(2018)RNA Secondary Structure Prediction Based on Long Short-Term Memory ModelIntelligent Computing Theories and Application10.1007/978-3-319-95930-6_59(595-599)Online publication date: 15-Aug-2018
https://dl.acm.org/doi/10.1007/978-3-319-95930-6_59
Shahbeig SHelfroush MRahideh A(2017)A fuzzy multi-objective hybrid TLBO-PSO approach to select the associated genes with breast cancerSignal Processing10.1016/j.sigpro.2016.07.035131:C(58-65)Online publication date: 1-Feb-2017
https://dl.acm.org/doi/10.1016/j.sigpro.2016.07.035
Gong ZChen HMukhopadhyay SZhai CBertino ECrestani FMostafa JTang JSi LZhou XChang YLi YSondhi P(2016)Model-Based Oversampling for Imbalanced Sequence ClassificationProceedings of the 25th ACM International on Conference on Information and Knowledge Management10.1145/2983323.2983784(1009-1018)Online publication date: 24-Oct-2016
https://dl.acm.org/doi/10.1145/2983323.2983784
Wan JYang MChen Y(2015)Discriminative cost sensitive Laplacian score for face recognitionNeurocomputing10.1016/j.neucom.2014.10.059152:C(333-344)Online publication date: 25-Mar-2015
https://dl.acm.org/doi/10.1016/j.neucom.2014.10.059

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