Discriminant analysis and feature selection in mass spectrometry imaging using constrained repeated random sampling - Cross validation (CORRS-CV).
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Guillermo Quintás | Julia Kuligowski | David Pérez-Guaita | D. Pérez-Guaita | J. Kuligowski | G. Quintás
[1] B. Heijs,et al. Mass spectrometry imaging: How will it affect clinical research in the future? , 2018, Expert review of proteomics.
[2] Olga Vitek,et al. Cardinal: an R package for statistical analysis of mass spectrometry-based imaging experiments , 2015, Bioinform..
[3] Richard G. Brereton,et al. Chemometrics for Pattern Recognition , 2009 .
[4] Vincenzo Lagani,et al. Performance-Estimation Properties of Cross-Validation-Based Protocols with Simultaneous Hyper-Parameter Optimization , 2015, Int. J. Artif. Intell. Tools.
[5] Rainer Breitling,et al. Rank products: a simple, yet powerful, new method to detect differentially regulated genes in replicated microarray experiments , 2004, FEBS letters.
[6] B. Rocha,et al. Mass spectrometry imaging: a novel technology in rheumatology , 2017, Nature Reviews Rheumatology.
[7] Vincenzo Lagani,et al. Performance-Estimation Properties of Cross-Validation-Based Protocols with Simultaneous Hyper-Parameter Optimization , 2014, Int. J. Artif. Intell. Tools.
[8] Eric R. Ziegel,et al. The Elements of Statistical Learning , 2003, Technometrics.
[9] Tsuyoshi Murata,et al. {m , 1934, ACML.
[10] S. Tsakovski,et al. Comparison of the variable importance in projection (VIP) and of the selectivity ratio (SR) methods for variable selection and interpretation , 2015 .
[11] Run-tao Tian,et al. MassImager: A software for interactive and in-depth analysis of mass spectrometry imaging data. , 2018, Analytica chimica acta.
[12] Robert Tibshirani,et al. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.
[13] Liam A. McDonnell,et al. Imaging mass spectrometry statistical analysis. , 2012, Journal of proteomics.
[14] R Core Team,et al. R: A language and environment for statistical computing. , 2014 .
[15] P. Ubezio,et al. Past-in-the-Future. Peak detection improves targeted mass spectrometry imaging. , 2018, Analytica chimica acta.
[16] M. Clench,et al. Mass spectrometry imaging and its application in pharmaceutical research and development: A concise review , 2019, International Journal of Mass Spectrometry.
[17] Age K. Smilde,et al. Assessing the performance of statistical validation tools for megavariate metabolomics data , 2006, Metabolomics.
[18] Anthony B. Costa,et al. Multivariate statistical differentiation of renal cell carcinomas based on lipidomic analysis by ambient ionization imaging mass spectrometry , 2010, Analytical and bioanalytical chemistry.
[19] Lingjun Li,et al. Mass Spectrometry Imaging: A Review of Emerging Advancements and Future Insights. , 2018, Analytical chemistry.
[20] Age K. Smilde,et al. UvA-DARE ( Digital Academic Repository ) Assessment of PLSDA cross validation , 2008 .
[21] D. Ruppert. The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2004 .
[22] B. Wood,et al. Assessment of discriminant models in infrared imaging using constrained repeated random sampling - Cross validation. , 2018, Analytica chimica acta.
[23] Theodore Alexandrov,et al. Spatial segmentation of imaging mass spectrometry data with edge-preserving image denoising and clustering. , 2010, Journal of proteome research.
[24] Cyril Ruckebusch,et al. On the implementation of spatial constraints in multivariate curve resolution alternating least squares for hyperspectral image analysis , 2015 .