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Towards an accurate cancer diagnosis modelization: Comparison of Random Forest strategies
Debit, Ahmed; Poulet, Christophe; JOSSE, Claire et al.
20185th conference Bioinformatics for young international researchers expo byteMAL 2018
 

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Keywords :
Random Forest Classification methods; Cancer; Diagnosis; Prognosis; Tumor vs Normal; TCGA data
Abstract :
[en] Machine learning approaches are heavily used to produce models that will one day support clinical decisions. To be reliably used as a medical decision, such diagnosis and prognosis tools have to harbor a high-level of precision. Random Forests have been already used in cancer diagnosis, prognosis, and screening. Numerous Random Forests methods have been derived from the original random forest algorithm from Breiman et al. in 2001. Nevertheless, the precision of their generated models remains unknown when facing biological data. The precision of such models can be therefore too variable to produce models with the same accuracy of classification, making them useless in daily clinics. Here, we perform an empirical comparison of Random Forest based strategies, looking for their precision in model accuracy and overall computational time. An assessment of 15 methods is carried out for the classification of paired normal - tumor patients, from 3 TCGA RNA-Seq datasets: BRCA (Breast Invasive Carcinoma), LUSC (Lung Squamous Cell Carcinoma), and THCA (Thyroid Carcinoma). Results demonstrate noteworthy differences in the precisions of the model accuracy and the overall time processing, between the strategies for one dataset, as well as between datasets for one strategy. Therefore, we highly recommend to test each random forest strategy prior to modelization. This will certainly improve the precision in model accuracy while revealing the method of choice for the candidate data.
Research center :
GIGA‐R - Giga‐Research - ULiège
Disciplines :
Computer science
Author, co-author :
Debit, Ahmed ;  Université de Liège - ULiège > Cancer-Human Genetics
Poulet, Christophe  ;  Université de Liège - ULiège > Cancer-Human Genetics
JOSSE, Claire  ;  Centre Hospitalier Universitaire de Liège - CHU > Département de médecine interne > Service d'oncologie médicale
Van Steen, Kristel  ;  Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Bioinformatique
JERUSALEM, Guy  ;  Centre Hospitalier Universitaire de Liège - CHU > Département de médecine interne > Service d'oncologie médicale
Bours, Vincent ;  Université de Liège - ULiège > Département des sciences biomédicales et précliniques > Génétique humaine
Azencott, Chloé-Agathe;  Mines ParisTech > Centre for Computational Biology
Language :
English
Title :
Towards an accurate cancer diagnosis modelization: Comparison of Random Forest strategies
Publication date :
05 October 2018
Event name :
5th conference Bioinformatics for young international researchers expo byteMAL 2018
Event place :
Liege, Belgium
Event date :
05-10-2018
Audience :
International
Name of the research project :
wallInov NACATS
Funders :
Région wallonne: R.RWAL.1218-J-P-A
Available on ORBi :
since 21 February 2019

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