Comparison of metaheuristic strategies for peakbin selection in proteomic mass spectrometry data
暂无分享,去创建一个
Concha Bielza | Pedro Larrañaga | Miguel García-Torres | Rubén Armañanzas | C. Bielza | P. Larrañaga | M. García-Torres | Rubén Armañanzas | R. Armañanzas
[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 .