Literature DB >> 17305606

Prediction of protein B-factors using multi-class bounded SVM.

Peng Chen1, Bing Wang, Hau-San Wong, De-Shuang Huang.   

Abstract

In this paper, we propose the adoption of the bounded support vector machine (BSVM) to predict the B-factors of residues based on a number of distinctive properties of residues. Due to the ability of multi-class classification of the BSVM, we can elaborately distinguish our targets and obtain relatively higher accuracy.

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Year:  2007        PMID： 17305606     DOI： 10.2174/092986607779816078

Source DB:  PubMed          Journal:  Protein Pept Lett        ISSN： 0929-8665            Impact factor:   1.890


  10 in total

1.  MoRFpred, a computational tool for sequence-based prediction and characterization of short disorder-to-order transitioning binding regions in proteins.

Authors:  Fatemeh Miri Disfani; Wei-Lun Hsu; Marcin J Mizianty; Christopher J Oldfield; Bin Xue; A Keith Dunker; Vladimir N Uversky; Lukasz Kurgan
Journal:  Bioinformatics       Date:  2012-06-15       Impact factor: 6.937

2.  Protein elastic network models and the ranges of cooperativity.

Authors:  Lei Yang; Guang Song; Robert L Jernigan
Journal:  Proc Natl Acad Sci U S A       Date:  2009-07-14       Impact factor: 11.205

3.  Structural features that predict real-value fluctuations of globular proteins.

Authors:  Michal Jamroz; Andrzej Kolinski; Daisuke Kihara
Journal:  Proteins       Date:  2012-02-13

4.  DomSVR: domain boundary prediction with support vector regression from sequence information alone.

Authors:  Peng Chen; Chunmei Liu; Legand Burge; Jinyan Li; Mahmood Mohammad; William Southerland; Clay Gloster; Bing Wang
Journal:  Amino Acids       Date:  2010-02-18       Impact factor: 3.520

5.  Sequence-based identification of interface residues by an integrative profile combining hydrophobic and evolutionary information.

Authors:  Peng Chen; Jinyan Li
Journal:  BMC Bioinformatics       Date:  2010-07-28       Impact factor: 3.169

6.  Distance matrix-based approach to protein structure prediction.

Authors:  Andrzej Kloczkowski; Robert L Jernigan; Zhijun Wu; Guang Song; Lei Yang; Andrzej Kolinski; Piotr Pokarowski
Journal:  J Struct Funct Genomics       Date:  2009-02-18

7.  LigandRFs: random forest ensemble to identify ligand-binding residues from sequence information alone.

Authors:  Peng Chen; Jianhua Z Huang; Xin Gao
Journal:  BMC Bioinformatics       Date:  2014-12-03       Impact factor: 3.169

8.  SIPGCN: A Novel Deep Learning Model for Predicting Self-Interacting Proteins from Sequence Information Using Graph Convolutional Networks.

Authors:  Ying Wang; Lin-Lin Wang; Leon Wong; Yang Li; Lei Wang; Zhu-Hong You
Journal:  Biomedicines       Date:  2022-06-29

9.  Predicting Protein-Protein Interactions Based on Ensemble Learning-Based Model from Protein Sequence.

Authors:  Xinke Zhan; Mang Xiao; Zhuhong You; Chenggang Yan; Jianxin Guo; Liping Wang; Yaoqi Sun; Bingwan Shang
Journal:  Biology (Basel)       Date:  2022-06-30

10.  Prediction of peptide drift time in ion mobility mass spectrometry from sequence-based features.

Authors:  Bing Wang; Jun Zhang; Peng Chen; Zhiwei Ji; Shuping Deng; Chi Li
Journal:  BMC Bioinformatics       Date:  2013-05-09       Impact factor: 3.169
  10 in total

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