Assistant Professor at Département d'Informatique, team DATA, Ecole Normale Supérieure (Paris, France).
Email : gilles.wainrib [at] ens.fr
Romain Couillet, Gilles Wainrib, Hafiz Tiomoko Ali, Harry Sevi
Recurrent neural networks, especially in their linear version, have provided many qualitative insights on their performance under different configurations. This article provides, through a novel random matrix framework, the quantitative counterpart of these performance results, specifically in the case of echo-state networks. Beyond mere insights, our approach conveys a deeper understanding on the core mechanism under play for both training and testing.
Colonic microRNA-based composite algorithm predicting drug responses in acute severe ulcerative colitis
Ian Morilla et. alAcute severe ulcerative colitis (ASUC) is a severe condition that should be managed sequentially with intravenous steroids and, infliximab or cyclosporine in case of refractoriness. If medical treatment fails, salvage colectomy would be delayed and associated with excess mortality. Currently, there is no biomarker of drug response; the aim was to identify predictors of response to first and second-line treatments in ASUC. This study identified new biomarkers of response to treatment in patients with ASUC, enabling more accurate prediction of the benefit of therapy.
Context-dependent representation in recurrent neural networks
Ulcerative Colitis and smoking: an integrative network-based analysis of detoxification gene expression data
Y-P. Ding et al., submitted.
Inflammatory bowel diseases (IBD) including Crohn’s disease (CD) and ulcerative colitis (UC) are severe chronic intestinal disorders common in developed countries. Besides the well-characterized genetic contribution to IBD predisposition, environmental factors affect the incidence and medical history of IBD, among which active smoking has been shown as the most robust risk factor. The effect of smoking seems to be ambivalent since active smoking improves UC while it worsens CD. Although this clinical relationship between IBD and tobacco is well established, only a few experimental works have investigated the effect of smoking on the colonic barrier homeostasis focusing on xenobiotic detoxification genes. We performed a comprehensive and integrated comparative analysis of the global xenobiotic detoxification capacity of the normal colonic mucosa of healthy smokers and non-smokers versus the non-affected colonic mucosa of UC patients to improve our understanding of the colon susceptibility to environmental aggression. Among the 244 detoxification genes investigated, 65 were significantly dysregulated in UC patients, which corresponds to a specific disease signature. We then developed a network-based data analysis approach for differentially assessing changes in genetic interactions allowing identifying unexpected regulatory detoxification genes which could play a major role in the pathogenesis of UC or in the beneficial effect of smoking on the colonic mucosa of UC patients. These observations could help clinicians to better understand the protective effect of cigarette smoking in UC and will be useful to develop new therapeutic avenues and automated diagnostic strategies.
Branching random walks on binary strings and application to adaptive immunity