Additional file 3: of A computational framework for complex disease stratification from multiple large-scale datasets

Alexander Manta (5314745); Alexander Mazein (100093); Alun Bedding (5314736); Amphun Chaiboonchoe (4341562); Anthony Postle (525334); Anthony Rowe (5244689); Aruna Bansal (77614); Bertrand De Meulder (518342); Charles Auffray (782); Christopher Larminie (3716980); Diane Lefaudeux (5314739); Frédéric Baribaud (647912); Hassan Ahmed (604968); Ian Adcock (486486); Ioannis Pandis (551976); Irina Balaur (2745514); Johann Pellet (2745520); Jonathan van Eyll (5314733); Julie Corfield (5244677); Kai Sun (140584); Kian Chung (3582395); Kosmas Kretsos (5314751); Manohara Batuwitage (5314748); Mansoor Saqi (64402); Nathanaël Lemonnier (5314757); Paul Dodson (5314730); Peter Sterk (2274196); Ratko Djukanovic (3167016); Stéphane Ballereau (4100119); Timothy Davison (5314742); Xian Yang (584213); Yi-Ke Guo (5314754)

Additional file 3: of A computational framework for complex disease stratification from multiple large-scale datasets

Abstract

Table S7. Estimated accuracy and standard deviation of the RFE procedure. Table S8. Accuracy and Kappa values of the Random Forest models in the training set. Table S9. Performances values for the Random Forest model in the testing set. Figure S11. Relative importance of the top 20 predictors building the final model of the RF. The importance axis is scaled, with the mRNA expression of CD3D scaled to 100% and the methylation state of POLA2 to 0% (not shown). (DOCX 18 kb

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Last time updated on 13/08/2018