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See detailTowards an accurate cancer diagnosis modelization: Comparison of Random Forest strategies
Debit, Ahmed ULiege; Poulet, Christophe ULiege; JOSSE, Claire ULiege et al

Poster (2018, October 05)

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 ... [more ▼]

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. [less ▲]

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See detailFrom quality control to normalization of RNA-seq data
Debit, Ahmed ULiege

Speech/Talk (2017)

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See detailNormalization and correction for batch effects via RUV for RNA-seq data: practical implications for Breast Cancer Research
Debit, Ahmed ULiege; Wenric, Stéphane; JOSSE, Claire ULiege et al

Poster (2017, May)

The whole transcriptome contains information about nonsense, missense, silent, in-frame and frameshift mutations, as observed at whole-exome level, as well as splicing and (allelic) gene-expression ... [more ▼]

The whole transcriptome contains information about nonsense, missense, silent, in-frame and frameshift mutations, as observed at whole-exome level, as well as splicing and (allelic) gene-expression changes which are missed by DNA analysis. One important step in the analysis of gene expression data arising from RNA-seq is the detection of differential expression (DE) levels. Several methods are available and the choice is sometimes controversial. For a reliable DE analysis that reduces False Positive DE genes, and accurate estimation of gene expression levels, a good and suitable normalization approach (including correction for confounders) is mandatory. Several normalization methods have been proposed to correct for both within-sample and between-sample biases. RUV (Removing Unwanted Variation) is one of them and has the advantage to correct for batch effects including potentially unknown unwanted variation in gene expression. In this study, we present a comparison on real-life Illumina paired-end sequencing data for Estrogen-Receptor-Positive (ER+) Breast Cancer tissues versus matched controls between RUV (RUVg using in silico negative control genes) and more commonly used methods for RNA-seq data normalization, such as DESeq2, edgeR, and UQ. The set of in silico empirical negative control genes for RUVg was defined as the set of least significant DE genes obtained after a first DE analysis performed prior to RUVg correction. Box plots of relative log expression (RLE) among the samples and PCA plots show that RUVg performs well and leads to a stabilization of read count across samples with a clear clustering of biological replicates. [less ▲]

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See detailDetection d'objets rares au sein de coupes histologiques par apprentissage automatique
Debit, Ahmed ULiege

Master's dissertation (2014)

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