Predicting liquid chromatographic retention times of peptides from the Drosophila melanogaster proteome by machine learning approaches.

[1]  Svante Wold,et al.  D-Optimal Designs in QSAR , 1993 .

[2]  Carl E. Rasmussen,et al.  In Advances in Neural Information Processing Systems , 2011 .

[3]  M. Karelson,et al.  QSPR: the correlation and quantitative prediction of chemical and physical properties from structure , 1995 .

[4]  Yi-Zeng Liang,et al.  Monte Carlo cross validation , 2001 .

[5]  S. Wold,et al.  PLS-regression: a basic tool of chemometrics , 2001 .

[6]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machines , 2002 .

[7]  Gordon A Anderson,et al.  Use of artificial neural networks for the accurate prediction of peptide liquid chromatography elution times in proteome analyses. , 2003, Analytical chemistry.

[8]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[9]  Yi-Zeng Liang,et al.  Monte Carlo cross‐validation for selecting a model and estimating the prediction error in multivariate calibration , 2004 .

[10]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machine Classifiers , 1999, Neural Processing Letters.

[11]  Károly Héberger,et al.  Quantitative structure–retention relationships XIV: Prediction of gas chromatographic retention indices for saturated O-, N-, and S-heterocyclic compounds , 2004 .

[12]  Xueguang Shao,et al.  Variable selection by modified IPW (iterative predictor weighting)-PLS (partial least squares) in continuous wavelet regression models. , 2004, The Analyst.

[13]  Ruisheng Zhang,et al.  Quantitative Prediction of logk of Peptides in High-Performance Liquid Chromatography Based on Molecular Descriptors by Using the Heuristic Method and Support Vector Machine , 2004, J. Chem. Inf. Model..

[14]  Zbigniew Grzonka,et al.  Prediction of high‐performance liquid chromatography retention of peptides with the use of quantitative structure‐retention relationships , 2005, Proteomics.

[15]  Yvan Vander Heyden,et al.  Prediction of peptide retention at different HPLC conditions from multiple linear regression models. , 2005, Journal of proteome research.

[16]  Károly Héberger,et al.  Comparison of Ridge Regression, Partial Least-Squares, Pairwise Correlation, Forward- and Best Subset Selection Methods for Prediction of Retention Indices for Aliphatic Alcohols , 2005, J. Chem. Inf. Model..

[18]  T. Kaufman,et al.  Mapping the proteome of Drosophila melanogaster: analysis of embryos and adult heads by LC-IMS-MS methods. , 2005, Journal of proteome research.

[19]  Ying Xu,et al.  Improved peptide elution time prediction for reversed-phase liquid chromatography-MS by incorporating peptide sequence information. , 2006, Analytical chemistry.

[20]  M Daszykowski,et al.  Retention prediction of peptides based on uninformative variable elimination by partial least squares. , 2006, Journal of proteome research.

[21]  T. Bączek.  Chemometric evaluation of relationships between retention and physicochemical parameters in terms of multidimensional liquid chromatography of peptides. , 2006, Journal of separation science.

[22]  Masaru Tomita,et al.  Prediction of liquid chromatographic retention times of peptides generated by protease digestion of the Escherichia coli proteome using artificial neural networks. , 2006, Journal of proteome research.

[23]  Gábor Csányi,et al.  Gaussian Processes: A Method for Automatic QSAR Modeling of ADME Properties , 2007, J. Chem. Inf. Model..

[24]  K. Héberger.  Quantitative structure-(chromatographic) retention relationships. , 2007, Journal of chromatography. A.

[25]  M. Tomita,et al.  Informatics for peptide retention properties in proteomic LC‐MS , 2008, Proteomics.

[26]  F. Tian,et al.  Modeling and prediction of binding affinities between the human amphiphysin SH3 domain and its peptide ligands using genetic algorithm‐Gaussian processes , 2008, Biopolymers.

[27]  Zhide Hu,et al.  Novel approaches to predict the retention of histidine‐containing peptides in immobilized metal‐affinity chromatography , 2008, Proteomics.

[28]  Zhide Hu,et al.  Prediction of retention times of peptides in RPLC by using radial basis function neural networks and projection pursuit regression , 2008 .

[29]  John Manchester,et al.  SAMFA: Simplifying Molecular Description for 3D-QSAR , 2008, J. Chem. Inf. Model..

[30]  F. Tian,et al.  Comprehensive comparison of eight statistical modelling methods used in quantitative structure-retention relationship studies for liquid chromatographic retention times of peptides generated by protease digestion of the Escherichia coli proteome. , 2009, Journal of chromatography. A.