Additional file 3: of A computational framework for complex disease stratification from multiple large-scale datasets
- Bertrand De Meulder (518342)
- Diane Lefaudeux (5314739)
- Aruna Bansal (77614)
- Alexander Mazein (100093)
- Amphun Chaiboonchoe (4341562)
- Hassan Ahmed (604968)
- Irina Balaur (2745514)
- Mansoor Saqi (64402)
- Johann Pellet (2745520)
- Stéphane Ballereau (4100119)
- Nathanaël Lemonnier (5314757)
- Kai Sun (140584)
- Ioannis Pandis (551976)
- Xian Yang (584213)
- Manohara Batuwitage (5314748)
- Kosmas Kretsos (5314751)
- Jonathan van Eyll (5314733)
- Alun Bedding (5314736)
- Timothy Davison (5314742)
- Paul Dodson (5314730)
- Christopher Larminie (3716980)
- Anthony Postle (525334)
- Julie Corfield (5244677)
- Ratko Djukanovic (3167016)
- Kian Chung (3582395)
- Ian Adcock (486486)
- Yi-Ke Guo (5314754)
- Peter Sterk (2274196)
- Alexander Manta (5314745)
- Anthony Rowe (5244689)
- Frédéric Baribaud (647912)
- Charles Auffray (782)
Publication date
May 2018
DOIAbstract
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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