PhosphoSVM: prediction of phosphorylation sites by integrating various protein sequence attributes with a support vector machine

[1]  Monika Zulawski,et al.  PhosPhAt goes kinases—searchable protein kinase target information in the plant phosphorylation site database PhosPhAt , 2012, Nucleic Acids Res..

[2]  Yao Chi Chen,et al.  Hidden relationship between conserved residues and locally conserved phosphate-binding structures in NAD(P)-binding proteins. , 2012, The journal of physical chemistry. B.

[3]  Jun Wang,et al.  L1pred: A Sequence-Based Prediction Tool for Catalytic Residues in Enzymes with the L1-logreg Classifier , 2012, PloS one.

[4]  A. T. Özcerit,et al.  Prediction of MHC class I binding peptides with a new feature encoding technique. , 2012, Cellular immunology.

[5]  Anthony J. Kusalik,et al.  Computational prediction of eukaryotic phosphorylation sites , 2011, Bioinform..

[6]  Yu Xue,et al.  GPS 2.1: enhanced prediction of kinase-specific phosphorylation sites with an algorithm of motif length selection. , 2011, Protein engineering, design & selection : PEDS.

[7]  Yu Xue,et al.  A summary of computational resources for protein phosphorylation. , 2010, Current protein & peptide science.

[8]  Dong Xu,et al.  Musite, a Tool for Global Prediction of General and Kinase-specific Phosphorylation Sites* , 2010, Molecular & Cellular Proteomics.

[9]  Xiaoqi Zheng,et al.  Prediction of catalytic residues based on an overlapping amino acid classification , 2010, Amino Acids.

[10]  Weifeng Liu,et al.  Adaptive and Learning Systems for Signal Processing, Communication, and Control , 2010 .

[11]  Robert Schmidt,et al.  PhosPhAt: the Arabidopsis thaliana phosphorylation site database. An update , 2009, Nucleic Acids Res..

[12]  Aleksey A. Porollo,et al.  Enhanced prediction of conformational flexibility and phosphorylation in proteins. , 2010, Advances in experimental medicine and biology.

[13]  Fredrik Johansson,et al.  A comparative study of conservation and variation scores , 2010, BMC Bioinformatics.

[14]  Ashis Kumer Biswas,et al.  Machine learning approach to predict protein phosphorylation sites by incorporating evolutionary information , 2010, BMC Bioinformatics.

[15]  Subhadip Basu,et al.  AMS 3.0: prediction of post-translational modifications , 2010, BMC Bioinformatics.

[16]  Yu Shyr,et al.  Improved prediction of lysine acetylation by support vector machines. , 2009, Protein and peptide letters.

[17]  Dong Xu,et al.  Computational Identification of Protein Methylation Sites through Bi-Profile Bayes Feature Extraction , 2009, PloS one.

[18]  Jonathan D. Hirst,et al.  Prediction of glycosylation sites using random forests , 2008, BMC Bioinformatics.

[19]  Lukasz A. Kurgan,et al.  Accurate sequence-based prediction of catalytic residues , 2008, Bioinform..

[20]  B. Turk,et al.  A versatile strategy to define the phosphorylation preferences of plant protein kinases and screen for putative substrates. , 2008, The Plant journal : for cell and molecular biology.

[21]  Joachim Selbig,et al.  PhosPhAt: a database of phosphorylation sites in Arabidopsis thaliana and a plant-specific phosphorylation site predictor , 2007, Nucleic Acids Res..

[22]  Allegra Via,et al.  Phospho.ELM: a database of phosphorylation sites—update 2008 , 2007, Nucleic Acids Res..

[23]  Francisco Melo,et al.  StAR: a simple tool for the statistical comparison of ROC curves , 2008, BMC Bioinformatics.

[24]  Mona Singh,et al.  Predicting functionally important residues from sequence conservation , 2007, Bioinform..

[25]  Liangjiang Wang,et al.  BindN: a web-based tool for efficient prediction of DNA and RNA binding sites in amino acid sequences , 2006, Nucleic Acids Res..

[26]  Marios M. Polycarpou,et al.  Adaptive and Learning Systems for Signal Processing, Communications, and Control , 2006 .

[27]  Yu Xue,et al.  PPSP: prediction of PK-specific phosphorylation site with Bayesian decision theory , 2006, BMC Bioinformatics.

[28]  Chih-Jen Lin,et al.  Working Set Selection Using Second Order Information for Training Support Vector Machines , 2005, J. Mach. Learn. Res..

[29]  Lucila Ohno-Machado,et al.  The use of receiver operating characteristic curves in biomedical informatics , 2005, J. Biomed. Informatics.

[30]  S. Brunak,et al.  Prediction, conservation analysis, and structural characterization of mammalian mucin-type O-glycosylation sites. , 2005, Glycobiology.

[31]  Bermseok Oh,et al.  Prediction of phosphorylation sites using SVMs , 2004, Bioinform..

[32]  T. Hunter,et al.  The mouse kinome: discovery and comparative genomics of all mouse protein kinases. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[33]  G. Bologna,et al.  N‐Terminal myristoylation predictions by ensembles of neural networks , 2004, Proteomics.

[34]  J. S. Sodhi,et al.  Prediction and functional analysis of native disorder in proteins from the three kingdoms of life. , 2004, Journal of molecular biology.

[35]  O. Lichtarge,et al.  A family of evolution-entropy hybrid methods for ranking protein residues by importance. , 2004, Journal of molecular biology.

[36]  N. Blom,et al.  Identification of phosphorylation sites in protein kinase A substrates using artificial neural networks and mass spectrometry. , 2004, Journal of proteome research.

[37]  L. Iakoucheva,et al.  The importance of intrinsic disorder for protein phosphorylation. , 2004, Nucleic acids research.

[38]  Nikolaj Blom,et al.  Prediction of proprotein convertase cleavage sites. , 2004, Protein engineering, design & selection : PEDS.

[39]  Shandar Ahmad,et al.  RVP-net: online prediction of real valued accessible surface area of proteins from single sequences , 2003, Bioinform..

[40]  T. Hunter,et al.  The Protein Kinase Complement of the Human Genome , 2002, Science.

[41]  Søren Brunak,et al.  Prediction of Glycosylation Across the Human Proteome and the Correlation to Protein Function , 2001, Pacific Symposium on Biocomputing.

[42]  Liam J. McGuffin,et al.  The PSIPRED protein structure prediction server , 2000, Bioinform..

[43]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[44]  N. Blom,et al.  Sequence and structure-based prediction of eukaryotic protein phosphorylation sites. , 1999, Journal of molecular biology.

[45]  Nikolaj Blom,et al.  PhosphoBase, a database of phosphorylation sites: release 2.0 , 1999, Nucleic Acids Res..

[46]  N. Blom,et al.  Statistical analysis of protein kinase specificity determinants , 1998, FEBS letters.

[47]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[48]  Thomas L. Madden,et al.  Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. , 1997, Nucleic acids research.

[49]  N. Blom,et al.  Cleavage site analysis in picornaviral polyproteins: Discovering cellular targets by neural networks , 1996, Protein science : a publication of the Protein Society.

[50]  Vladimir Naumovich Vapni The Nature of Statistical Learning Theory , 1995 .

[51]  Douglas L. Brutlag,et al.  Identification of Protein Motifs Using Conserved Amino Acid Properties and Partitioning Techniques , 1995, ISMB.

[52]  D. Hardie,et al.  Evidence for a protein kinase cascade in higher plants. 3-Hydroxy-3-methylglutaryl-CoA reductase kinase. , 1992, European journal of biochemistry.

[53]  E. DeLong,et al.  Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. , 1988, Biometrics.

[54]  W. Taylor,et al.  The classification of amino acid conservation. , 1986, Journal of theoretical biology.

[55]  D. Eisenberg,et al.  Correlation of sequence hydrophobicities measures similarity in three-dimensional protein structure. , 1983, Journal of molecular biology.

[56]  P. Y. Chou,et al.  Conformational parameters for amino acids in helical, beta-sheet, and random coil regions calculated from proteins. , 1974, Biochemistry.