Comparison of metaheuristic strategies for peakbin selection in proteomic mass spectrometry data

[1]  Διονύσης Α. Κάβουρας,et al.  Proteomic mass spectra classification for biomarker discovery in prostate cancer ,employing pattern recognition techniques , 2015 .

[2]  Aixia Guo,et al.  Gene Selection for Cancer Classification using Support Vector Machines , 2014 .

[3]  Concha Bielza,et al.  Peakbin Selection in Mass Spectrometry Data Using a Consensus Approach with Estimation of Distribution Algorithms , 2011, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[4]  Qinghua Hu,et al.  Soft fuzzy rough sets for robust feature evaluation and selection , 2010, Inf. Sci..

[5]  Francisco Herrera,et al.  Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power , 2010, Inf. Sci..

[6]  Ching-Hsue Cheng,et al.  A hybrid model based on rough sets theory and genetic algorithms for stock price forecasting , 2010, Inf. Sci..

[7]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[8]  Qingzhong Liu,et al.  Comparison of feature selection and classification for MALDI-MS data , 2009, BMC Genomics.

[9]  Ihsan Kaya,et al.  A genetic algorithm approach to determine the sample size for attribute control charts , 2009, Inf. Sci..

[10]  Jana Novovicová,et al.  Evaluating the Stability of Feature Selectors That Optimize Feature Subset Cardinality , 2008, SSPR/SPR.

[11]  Yvan Saeys,et al.  Robust Feature Selection Using Ensemble Feature Selection Techniques , 2008, ECML/PKDD.

[12]  Carlos Gomes da Silva,et al.  Time series forecasting with a non-linear model and the scatter search meta-heuristic , 2008, Inf. Sci..

[13]  Qinghua Hu,et al.  Stability Analysis on Rough Set Based Feature Evaluation , 2008, RSKT.

[14]  Nicolas Molinari,et al.  A new genetic algorithm in proteomics: Feature selection for SELDI-TOF data , 2008, Comput. Stat. Data Anal..

[15]  Maria Joseph,et al.  Guilt-by-association feature selection: Identifying biomarkers from proteomic profiles , 2008, J. Biomed. Informatics.

[16]  Johannes Fürnkranz,et al.  A Re-evaluation of the Over-Searching Phenomenon in Inductive Rule Learning , 2008, LWA.

[17]  S. García,et al.  An Extension on "Statistical Comparisons of Classifiers over Multiple Data Sets" for all Pairwise Comparisons , 2008 .

[18]  Knut Reinert,et al.  OpenMS – An open-source software framework for mass spectrometry , 2008, BMC Bioinformatics.

[19]  Marcel J. T. Reinders,et al.  Comparison of normalisation methods for surface-enhanced laser desorption and ionisation (SELDI) time-of-flight (TOF) mass spectrometry data , 2008, BMC Bioinformatics.

[20]  Xiaoli Li,et al.  Profiling of High-Throughput Mass Spectrometry Data for Ovarian Cancer Detection , 2007, IDEAL.

[21]  Josef Kittler,et al.  Improving Stability of Feature Selection Methods , 2007, CAIP.

[22]  J. Pacheco,et al.  Use of VNS and TS in classification: variable selection and determination of the linear discrimination function coefficients , 2007 .

[23]  Ludmila I. Kuncheva,et al.  A stability index for feature selection , 2007, Artificial Intelligence and Applications.

[24]  Habtom W. Ressom,et al.  Peak selection from MALDI-TOF mass spectra using ant colony optimization , 2007, Bioinform..

[25]  A. Bezerianos,et al.  PROTEOMIC MASS SPECTRA CLASSIFICATION FOR BIOMARKER DISCOVERY IN PROSTATE CANCER , EMPLOYING PATTERN RECOGNITION TECHNIQUES , 2007 .

[26]  Jeffrey S. Morris,et al.  Pre-Processing Mass Spectrometry Data , 2007 .

[27]  Sven Rahmann,et al.  Discovering Biomarkers for Myocardial Infarction from SELDI-TOF Spectra , 2006, GfKl.

[28]  Melanie Hilario,et al.  Stability of feature selection algorithms: a study on high-dimensional spaces , 2007, Knowledge and Information Systems.

[29]  Janez Demsar,et al.  Statistical Comparisons of Classifiers over Multiple Data Sets , 2006, J. Mach. Learn. Res..

[30]  Melanie Hilario,et al.  On Preprocessing of SELDI-MS Data and its Evaluation , 2006, 19th IEEE Symposium on Computer-Based Medical Systems (CBMS'06).

[31]  Mia K. Markey,et al.  A machine learning perspective on the development of clinical decision support systems utilizing mass spectra of blood samples , 2006, J. Biomed. Informatics.

[32]  Belén Melián-Batista,et al.  Solving feature subset selection problem by a Parallel Scatter Search , 2006, Eur. J. Oper. Res..

[33]  Habtom W. Ressom,et al.  Ant Colony Optimization for Biomarker Identification from MALDI-TOF Mass Spectra , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[34]  S. Datta,et al.  Feature selection and machine learning with mass spectrometry data for distinguishing cancer and non-cancer samples , 2006 .

[35]  Melanie Hilario,et al.  Stability of feature selection algorithms , 2005, Fifth IEEE International Conference on Data Mining (ICDM'05).

[36]  Mario Cannataro,et al.  MS-Analyzer: Intelligent Preprocessing, Management, and Data Mining Analysis of Mass Spectrometry Data on the Grid , 2005, 2005 First International Conference on Semantics, Knowledge and Grid.

[37]  Jeffrey S. Morris,et al.  Improved peak detection and quantification of mass spectrometry data acquired from surface‐enhanced laser desorption and ionization by denoising spectra with the undecimated discrete wavelet transform , 2005, Proteomics.

[38]  André M Deelder,et al.  Reliability of human serum protein profiles generated with C8 magnetic beads assisted MALDI-TOF mass spectrometry. , 2005, Analytical chemistry.

[39]  Pedro Larrañaga,et al.  Feature selection in Bayesian classifiers for the prognosis of survival of cirrhotic patients treated with TIPS , 2005, J. Biomed. Informatics.

[40]  Pierre Geurts,et al.  Proteomic mass spectra classification using decision tree based ensemble methods , 2005, Bioinform..

[41]  Claudio Cobelli,et al.  Ovarian cancer identification based on dimensionality reduction for high-throughput mass spectrometry data , 2005, Bioinform..

[42]  Jeffrey S. Morris,et al.  Feature extraction and quantification for mass spectrometry in biomedical applications using the mean spectrum , 2005, Bioinform..

[43]  Jeffrey S. Morris,et al.  Signal in noise: evaluating reported reproducibility of serum proteomic tests for ovarian cancer. , 2005, Journal of the National Cancer Institute.

[44]  Jagath C. Rajapakse,et al.  SVM-RFE peak selection for cancer classification with mass spectrometry data , 2005, APBC.

[45]  Preprocessing , Management , and Analysis of Mass Spectrometry Proteomics Data , 2005 .

[46]  William H. Hsu,et al.  Genetic wrappers for feature selection in decision tree induction and variable ordering in Bayesian network structure learning , 2004, Inf. Sci..

[47]  Jeffrey S. Morris,et al.  Reproducibility of SELDI-TOF protein patterns in serum: comparing datasets from different experiments , 2004, Bioinform..

[48]  E. Holland,et al.  Serum peptide profiling by magnetic particle-assisted, automated sample processing and MALDI-TOF mass spectrometry. , 2004, Analytical chemistry.

[49]  E. Petricoin,et al.  Toxicoproteomics: Serum Proteomic Pattern Diagnostics for Early Detection of Drug Induced Cardiac Toxicities and Cardioprotection , 2004, Toxicologic pathology.

[50]  Jason Weston,et al.  Gene Selection for Cancer Classification using Support Vector Machines , 2002, Machine Learning.

[51]  E. Petricoin,et al.  High-resolution serum proteomic features for ovarian cancer detection. , 2004, Endocrine-related cancer.

[52]  Belén Melián-Batista,et al.  Solving Feature Subset Selection Problem by a Hybrid Metaheuristic , 2004, Hybrid Metaheuristics.

[53]  Jeffrey S. Morris,et al.  A comprehensive approach to the analysis of matrix‐assisted laser desorption/ionization‐time of flight proteomics spectra from serum samples , 2003, Proteomics.

[54]  Min Zhan,et al.  A data review and re-assessment of ovarian cancer serum proteomic profiling , 2003, BMC Bioinformatics.

[55]  Mark S. Boguski,et al.  Biomedical informatics for proteomics , 2003, Nature.

[56]  Isabelle Guyon,et al.  An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..

[57]  José A. Moreno-Pérez,et al.  Scatter Search for the Feature Selection Problem , 2003, CAEPIA.

[58]  Rafael Martí,et al.  Scatter Search: Diseño Básico y Estrategias avanzadas , 2002, Inteligencia Artif..

[59]  E. Fung,et al.  Proteomic approaches to tumor marker discovery. , 2002, Archives of pathology & laboratory medicine.

[60]  E. Petricoin,et al.  Serum proteomic patterns for detection of prostate cancer. , 2002, Journal of the National Cancer Institute.

[61]  P. Schellhammer,et al.  Serum protein fingerprinting coupled with a pattern-matching algorithm distinguishes prostate cancer from benign prostate hyperplasia and healthy men. , 2002, Cancer research.

[62]  P. Cunningham,et al.  Solutions to Instability Problems with Sequential Wrapper-based Approaches to Feature Selection , 2002 .

[63]  E. Petricoin,et al.  MECHANISMS OF DISEASE Mechanisms of disease Use of proteomic patterns in serum to identify ovarian cancer , 2022 .

[64]  L. Liotta,et al.  Proteomic Patterns of Nipple Aspirate Fluids Obtained by SELDI-TOF: Potential for New Biomarkers to Aid in the Diagnosis of Breast Cancer , 2002, Disease markers.

[65]  Pierre Hansen,et al.  Variable neighborhood search: Principles and applications , 1998, Eur. J. Oper. Res..

[66]  Kristel Michielsen,et al.  Morphological image analysis , 2000 .

[67]  Edoardo Amaldi,et al.  On the Approximability of Minimizing Nonzero Variables or Unsatisfied Relations in Linear Systems , 1998, Theor. Comput. Sci..

[68]  Jihoon Yang,et al.  Feature Subset Selection Using a Genetic Algorithm , 1998, IEEE Intell. Syst..

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

[70]  Ron Kohavi,et al.  Wrappers for Feature Subset Selection , 1997, Artif. Intell..

[71]  Pierre Hansen,et al.  Variable neighborhood search , 1997, Eur. J. Oper. Res..

[72]  Jerzy W. Bala,et al.  Using Learning to Facilitate the Evolution of Features for Recognizing Visual Concepts , 1996, Evolutionary Computation.

[73]  R. Mike Cameron-Jones,et al.  Oversearching and Layered Search in Empirical Learning , 1995, IJCAI.

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

[75]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[76]  Matthew L. Ginsberg,et al.  Essentials of Artificial Intelligence , 2012 .

[77]  T. Yip,et al.  New desorption strategies for the mass spectrometric analysis of macromolecules , 1993 .

[78]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[79]  Lei Xu,et al.  Best first strategy for feature selection , 1988, [1988 Proceedings] 9th International Conference on Pattern Recognition.

[80]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[81]  M. Karas,et al.  Matrix-assisted ultraviolet laser desorption of non-volatile compounds , 1987 .

[82]  Fred W. Glover,et al.  Future paths for integer programming and links to artificial intelligence , 1986, Comput. Oper. Res..

[83]  B. C. Brookes,et al.  Information Sciences , 2020, Cognitive Skills You Need for the 21st Century.

[84]  F. Glover HEURISTICS FOR INTEGER PROGRAMMING USING SURROGATE CONSTRAINTS , 1977 .

[85]  John Holland,et al.  Adaptation in Natural and Artificial Sys-tems: An Introductory Analysis with Applications to Biology , 1975 .