Isoelectric point optimization using peptide descriptors and support vector machines.

[1]  Markus Müller,et al.  In silico analysis of accurate proteomics, complemented by selective isolation of peptides. , 2011, Journal of proteomics.

[2]  Alexandros Karatzoglou,et al.  Kernel-based machine learning for fast text mining in R , 2010, Comput. Stat. Data Anal..

[3]  F. Tian,et al.  Predicting liquid chromatographic retention times of peptides from the Drosophila melanogaster proteome by machine learning approaches. , 2009, Analytica chimica acta.

[4]  S. Lemeer,et al.  A versatile peptide pI calculator for phosphorylated and N‐terminal acetylated peptides experimentally tested using peptide isoelectric focusing , 2008, Proteomics.

[5]  Max Kuhn,et al.  Building Predictive Models in R Using the caret Package , 2008 .

[6]  Tim W. Nattkemper,et al.  Peak intensity prediction in MALDI-TOF mass spectrometry: A machine learning study to support quantitative proteomics , 2008, BMC Bioinformatics.

[7]  J. Eu,et al.  Calculation of the isoelectric point of tryptic peptides in the pH 3.5–4.5 range based on adjacent amino acid effects , 2008, Electrophoresis.

[8]  Minoru Kanehisa,et al.  AAindex: amino acid index database, progress report 2008 , 2007, Nucleic Acids Res..

[9]  Cesare Furlanello,et al.  Machine learning methods for predictive proteomics , 2007, Briefings Bioinform..

[10]  Tomislav Smuc,et al.  Enhanced analytical power of SDS‐PAGE using machine learning algorithms , 2008, Proteomics.

[11]  Morgan C. Giddings,et al.  High-accuracy peptide mass fingerprinting using peak intensity data with machine learning. , 2008, Journal of proteome research.

[12]  Christine A. Miller,et al.  Efficient Fractionation and Improved Protein Identification by Peptide OFFGEL Electrophoresis*S , 2006, Molecular & Cellular Proteomics.

[13]  Concha Bielza,et al.  Machine Learning in Bioinformatics , 2008, Encyclopedia of Database Systems.

[14]  Ruedi Aebersold,et al.  Added value for tandem mass spectrometry shotgun proteomics data validation through isoelectric focusing of peptides. , 2005, Journal of proteome research.

[15]  Yu Zong Chen,et al.  prediction of protein-protein interactions , 2004 .

[16]  Robertson Craig,et al.  TANDEM: matching proteins with tandem mass spectra. , 2004, Bioinformatics.

[17]  Pier Giorgio Righetti,et al.  Determination of the isoelectric point of proteins by capillary isoelectric focusing. , 2004, Journal of chromatography. A.

[18]  B. Cargile,et al.  Immobilized pH gradients as a first dimension in shotgun proteomics and analysis of the accuracy of pI predictability of peptides , 2004, Electrophoresis.

[19]  B. Cargile,et al.  An alternative to tandem mass spectrometry: isoelectric point and accurate mass for the identification of peptides. , 2004, Analytical chemistry.

[20]  Christopher J. C. Burges,et al.  A Tutorial on Support Vector Machines for Pattern Recognition , 1998, Data Mining and Knowledge Discovery.

[21]  Ruisheng Zhang,et al.  Prediction of the Isoelectric Point of an Amino Acid Based on GA-PLS and SVMs , 2004, J. Chem. Inf. Model..

[22]  Ron D. Appel,et al.  ExPASy: the proteomics server for in-depth protein knowledge and analysis , 2003, Nucleic Acids Res..

[23]  Alexey I Nesvizhskii,et al.  Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search. , 2002, Analytical chemistry.

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

[25]  J. C. BurgesChristopher A Tutorial on Support Vector Machines for Pattern Recognition , 1998 .

[26]  D. Hochstrasser,et al.  The focusing positions of polypeptides in immobilized pH gradients can be predicted from their amino acid sequences , 1993, Electrophoresis.

[27]  J. M. Zimmerman,et al.  The characterization of amino acid sequences in proteins by statistical methods. , 1968, Journal of theoretical biology.