SPRED: A machine learning approach for the identification of classical and non-classical secretory proteins in mammalian genomes.

[1]  Pei-shan Wu,et al.  Biochemical and Biophysical Research Communications , 1960, Nature.

[2]  George Palade,et al.  Intracellular Aspects of the Process of Protein Synthesis , 1975, Science.

[3]  G. Blobel,et al.  Protein translocation across the endoplasmic reticulum. , 1994, Current opinion in cell biology.

[4]  T. Rapoport,et al.  A novel pathway for secretory proteins? , 1990, Trends in biochemical sciences.

[5]  G. Vonheijne The signal peptide. , 1990 .

[6]  D. Rifkin,et al.  Release of basic fibroblast growth factor, an angiogenic factor devoid of secretory signal sequence: A trivial phenomenon or a novel secretion mechanism? , 1991, Journal of cellular biochemistry.

[7]  R. Sitia,et al.  Secretion of thioredoxin by normal and neoplastic cells through a leaderless secretory pathway. , 1992, The Journal of biological chemistry.

[8]  Pat Langley,et al.  Estimating Continuous Distributions in Bayesian Classifiers , 1995, UAI.

[9]  Thomas G. Dietterich What is machine learning? , 2020, Archives of Disease in Childhood.

[10]  J. Rothman,et al.  Protein Sorting by Transport Vesicles , 1996, Science.

[11]  B. Dobberstein,et al.  Common Principles of Protein Translocation Across Membranes , 1996, Science.

[12]  A. Cleves Protein transport: The nonclassical ins and outs , 1997, Current Biology.

[13]  R. Hughes,et al.  Plasma membrane targetting, vesicular budding and release of galectin 3 from the cytoplasm of mammalian cells during secretion. , 1997, Journal of cell science.

[14]  Tin Kam Ho,et al.  The Random Subspace Method for Constructing Decision Forests , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  T. Hubbard,et al.  Using neural networks for prediction of the subcellular location of proteins. , 1998, Nucleic acids research.

[16]  R. Hughes Secretion of the galectin family of mammalian carbohydrate-binding proteins. , 1999, Biochimica et biophysica acta.

[17]  Minoru Kanehisa,et al.  AAindex: Amino Acid index database , 2000, Nucleic Acids Res..

[18]  Rolf Apweiler,et al.  The SWISS-PROT protein sequence database and its supplement TrEMBL in 2000 , 2000, Nucleic Acids Res..

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

[20]  Mark C. Field,et al.  Acylation-dependent Protein Export inLeishmania * , 2000, The Journal of Biological Chemistry.

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

[22]  R. Soldi,et al.  S100A13 Participates in the Release of Fibroblast Growth Factor 1 in Response to Heat Shock in Vitro * , 2001, The Journal of Biological Chemistry.

[23]  A. Krogh,et al.  Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. , 2001, Journal of molecular biology.

[24]  Adam Godzik,et al.  Clustering of highly homologous sequences to reduce the size of large protein databases , 2001, Bioinform..

[25]  S. Dudoit,et al.  Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data , 2002 .

[26]  Tin Kam Ho,et al.  A Data Complexity Analysis of Comparative Advantages of Decision Forest Constructors , 2002, Pattern Analysis & Applications.

[27]  David Ward,et al.  Comparison of statistical methods for classification of ovarian cancer using mass spectrometry data , 2003, Bioinform..

[28]  W. Nickel The mystery of nonclassical protein secretion. A current view on cargo proteins and potential export routes. , 2003, European journal of biochemistry.

[29]  L. Trotman,et al.  Non‐Classical Export of an Adenovirus Structural Protein , 2003, Traffic.

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

[31]  Gajendra P. S. Raghava,et al.  ESLpred: SVM-based method for subcellular localization of eukaryotic proteins using dipeptide composition and PSI-BLAST , 2004, Nucleic Acids Res..

[32]  Ian H. Witten,et al.  Data mining in bioinformatics using Weka , 2004, Bioinform..

[33]  N. Blom,et al.  Feature-based prediction of non-classical and leaderless protein secretion. , 2004, Protein engineering, design & selection : PEDS.

[34]  S. Brunak,et al.  Improved prediction of signal peptides: SignalP 3.0. , 2004, Journal of molecular biology.

[35]  E. Birney,et al.  The International Protein Index: An integrated database for proteomics experiments , 2004, Proteomics.

[36]  Yanjun Qi,et al.  Random Forest Similarity for Protein-Protein Interaction Prediction from Multiple Sources , 2004, Pacific Symposium on Biocomputing.

[37]  Ramón Díaz-Uriarte,et al.  Gene selection and classification of microarray data using random forest , 2006, BMC Bioinformatics.

[38]  G. Heijne The signal peptide , 2005, The Journal of Membrane Biology.

[39]  Jae Won Lee,et al.  An extensive comparison of recent classification tools applied to microarray data , 2004, Comput. Stat. Data Anal..

[40]  David W. Aha,et al.  Instance-Based Learning Algorithms , 1991, Machine Learning.

[41]  Chittibabu Guda,et al.  pTARGET: a web server for predicting protein subcellular localization , 2006, Nucleic Acids Res..

[42]  Ponnuthurai N. Suganthan,et al.  A machine learning approach for the identification of odorant binding proteins from sequence-derived properties , 2007, BMC Bioinformatics.

[43]  Andy Liaw,et al.  Classification and Regression by randomForest , 2007 .

[44]  John A. Bumpus,et al.  An in silico Analysis of Cytochrome c from Phanerochaete chrysosporium: Its Amino Acid Sequence and Characterization of Gene Structural Elements , 2008, Silico Biol..

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

[46]  P. Suganthan,et al.  Identification of catalytic residues from protein structure using support vector machine with sequence and structural features. , 2008, Biochemical and biophysical research communications.

[47]  Gajendra P. S. Raghava,et al.  A Machine Learning Based Method for the Prediction of Secretory Proteins Using Amino Acid Composition, Their Order and Similarity-Search , 2008, Silico Biol..

[48]  Constantin F. Aliferis,et al.  A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification , 2008, BMC Bioinformatics.