Feature Selection Methods for Early Predictive Biomarker Discovery Using Untargeted Metabolomic Data
暂无分享,去创建一个
Amedeo Napoli | Mélanie Pétéra | Estelle Pujos-Guillot | Blandine Comte | Dhouha Grissa | Marion Brandolini | A. Napoli | B. Comte | E. Pujos-Guillot | Dhouha Grissa | M. Pétéra | M. Brandolini
[1] Yiyu Cheng,et al. Urinary nucleosides based potential biomarker selection by support vector machine for bladder cancer recognition. , 2007, Analytica chimica acta.
[2] Yoav Freund,et al. A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.
[3] Frank Hsu,et al. Knowledge Discovery , 2014, Encyclopedia of Social Network Analysis and Mining.
[4] D. Wishart,et al. Translational biomarker discovery in clinical metabolomics: an introductory tutorial , 2012, Metabolomics.
[5] L. Beran,et al. [Formal concept analysis]. , 1996, Casopis lekaru ceskych.
[6] Yoav Freund,et al. A decision-theoretic generalization of on-line learning and an application to boosting , 1997, EuroCOLT.
[7] Taghi M. Khoshgoftaar,et al. Measuring Stability of Feature Selection Techniques on Real-World Software Datasets , 2013 .
[8] Carolin Strobl,et al. A new variable importance measure for random forests with missing data , 2012, Statistics and Computing.
[9] Bernhard Ganter,et al. Formal Concept Analysis: Mathematical Foundations , 1998 .
[10] Thomas Brendan Murphy,et al. Applying random forests to identify biomarker panels in serum 2D-DIGE data for the detection and staging of prostate cancer. , 2011, Journal of proteome research.
[11] Gérard Biau,et al. Analysis of a Random Forests Model , 2010, J. Mach. Learn. Res..
[12] K. Schram. Urinary nucleosides. , 1998, Mass spectrometry reviews.
[13] Pedro Larrañaga,et al. A review of feature selection techniques in bioinformatics , 2007, Bioinform..
[14] Maria Liakata,et al. Merits of random forests emerge in evaluation of chemometric classifiers by external validation. , 2013, Analytica chimica acta.
[15] E F Sawyer,et al. A new variable. , 1885, Science.
[16] David I. Ellis,et al. A tutorial review: Metabolomics and partial least squares-discriminant analysis--a marriage of convenience or a shotgun wedding. , 2015, Analytica chimica acta.
[17] Joachim M. Buhmann,et al. Feature selection for support vector machines , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.
[18] Frans M van der Kloet,et al. Analytical error reduction using single point calibration for accurate and precise metabolomic phenotyping. , 2009, Journal of proteome research.
[19] Ron Kohavi,et al. A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection , 1995, IJCAI.
[20] Daniel Jacob,et al. Workflow4Metabolomics: a collaborative research infrastructure for computational metabolomics , 2014, Bioinform..
[21] Rawi Ramautar,et al. Human metabolomics: strategies to understand biology. , 2013, Current opinion in chemical biology.
[22] J. Lindon,et al. 'Metabonomics': understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. , 1999, Xenobiotica; the fate of foreign compounds in biological systems.
[23] Steffen Neumann,et al. Highly sensitive feature detection for high resolution LC/MS , 2008, BMC Bioinformatics.
[24] Carolin Strobl,et al. Random forest Gini importance favours SNPs with large minor allele frequency: impact, sources and recommendations , 2012, Briefings Bioinform..
[25] Huan Liu,et al. Toward integrating feature selection algorithms for classification and clustering , 2005, IEEE Transactions on Knowledge and Data Engineering.
[26] Ian H. Witten,et al. Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .
[27] Xavier Robin,et al. pROC: an open-source package for R and S+ to analyze and compare ROC curves , 2011, BMC Bioinformatics.
[28] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[29] Yu Cao,et al. Random Forest in Clinical Metabolomics for Phenotypic Discrimination and Biomarker Selection , 2013, Evidence-based complementary and alternative medicine : eCAM.
[30] A. Zell,et al. Metabonomics in cancer diagnosis: mass spectrometry-based profiling of urinary nucleosides from breast cancer patients. , 2008, Biomarkers : biochemical indicators of exposure, response, and susceptibility to chemicals.
[31] M. Zins,et al. Cohort Profile Update: The GAZEL Cohort Study. , 2015, International journal of epidemiology.
[32] O. Fiehn,et al. Metabolite profiling for plant functional genomics , 2000, Nature Biotechnology.
[33] Andy Liaw,et al. Classification and Regression by randomForest , 2007 .
[34] Estelle Pujos-Guillot,et al. Development and validation of a UPLC/MS method for a nutritional metabolomic study of human plasma , 2010, Metabolomics.
[35] Aixia Guo,et al. Gene Selection for Cancer Classification using Support Vector Machines , 2014 .
[36] T. Veenstra,et al. Analytical and statistical approaches to metabolomics research. , 2009, Journal of separation science.
[37] Serge Rudaz,et al. Mass spectrometry metabolomic data handling for biomarker discovery , 2020, Proteomic and Metabolomic Approaches to Biomarker Discovery.
[38] Tin Kam Ho,et al. The Random Subspace Method for Constructing Decision Forests , 1998, IEEE Trans. Pattern Anal. Mach. Intell..
[39] Age K. Smilde,et al. Reflections on univariate and multivariate analysis of metabolomics data , 2013, Metabolomics.
[40] Gavin C. Cawley,et al. On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation , 2010, J. Mach. Learn. Res..
[41] LarrañagaPedro,et al. A review of feature selection techniques in bioinformatics , 2007 .
[42] Ian H. Witten,et al. Data mining: practical machine learning tools and techniques with Java implementations , 2002, SGMD.
[43] David I. Ellis,et al. A comparative investigation of modern feature selection and classification approaches for the analysis of mass spectrometry data. , 2014, Analytica chimica acta.
[44] Vladimir Vapnik,et al. Statistical learning theory , 1998 .
[45] Paolo Giudici,et al. Applied Data Mining: Statistical Methods for Business and Industry , 2003 .
[46] Christian Baumgartner,et al. Bioinformatic-driven search for metabolic biomarkers in disease , 2011, Journal of Clinical Bioinformatics.
[47] Maria P. Pavlou,et al. Proteomic and Mass Spectrometry Technologies for Biomarker Discovery , 2013 .
[48] Kamlesh Khunti,et al. Risk assessment tools for detecting those with pre-diabetes: a systematic review. , 2014, Diabetes research and clinical practice.
[49] Sayan Mukherjee,et al. Feature Selection for SVMs , 2000, NIPS.
[50] Ramón Díaz-Uriarte,et al. Gene selection and classification of microarray data using random forest , 2006, BMC Bioinformatics.
[51] Corinna Cortes,et al. Support-Vector Networks , 1995, Machine Learning.
[52] Isabelle Guyon,et al. An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..
[53] Stephen T. C. Wong,et al. Gene Selection and Classification , 2008 .
[54] Ron Kohavi,et al. Feature Selection for Knowledge Discovery and Data Mining , 1998 .
[55] Seoung Bum Kim,et al. Discovery of metabolite features for the modelling and analysis of high-resolution NMR spectra , 2008, Int. J. Data Min. Bioinform..
[56] R. Balasubramanian,et al. Comparative Evaluation of Classifiers in the Presence of Statistical Interactions between Features in High Dimensional Data Settings , 2012, The international journal of biostatistics.
[57] R. Goodacre,et al. The role of metabolites and metabolomics in clinically applicable biomarkers of disease , 2010, Archives of Toxicology.
[58] Bjoern H. Menze,et al. A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data , 2009, BMC Bioinformatics.
[59] C. Watkinson. Risk assessment tools. , 1997, Professional nurse.
[60] Serge Rudaz,et al. Knowledge discovery in metabolomics: an overview of MS data handling. , 2010, Journal of separation science.
[61] Gabriel S. Eichler,et al. Metabolomics Reveals Attenuation of the SLC6A20 Kidney Transporter in Nonhuman Primate and Mouse Models of Type 2 Diabetes Mellitus* , 2011, The Journal of Biological Chemistry.
[62] A. Zell,et al. Metabonomics in cancer diagnosis: mass spectrometry-based profiling of urinary nucleosides from breast cancer patients , 2008 .
[63] Das Amrita,et al. Mining Association Rules between Sets of Items in Large Databases , 2013 .
[64] Jason Weston,et al. Embedded Methods , 2006, Feature Extraction.
[65] Bowei Xi,et al. Statistical analysis and modeling of mass spectrometry-based metabolomics data. , 2014, Methods in molecular biology.