Search results for "modeling"
showing 10 items of 4489 documents
DeepWAS: Multivariate genotype-phenotype associations by directly integrating regulatory information using deep learning
2020
Genome-wide association studies (GWAS) identify genetic variants associated with traits or diseases. GWAS never directly link variants to regulatory mechanisms. Instead, the functional annotation of variants is typically inferred by post hoc analyses. A specific class of deep learning-based methods allows for the prediction of regulatory effects per variant on several cell type-specific chromatin features. We here describe “DeepWAS”, a new approach that integrates these regulatory effect predictions of single variants into a multivariate GWAS setting. Thereby, single variants associated with a trait or disease are directly coupled to their impact on a chromatin feature in a cell type. Up to…
Identifying Prognostic SNPs in Clinical Cohorts: Complementing Univariate Analyses by Resampling and Multivariable Modeling
2016
Clinical cohorts with time-to-event endpoints are increasingly characterized by measurements of a number of single nucleotide polymorphisms that is by a magnitude larger than the number of measurements typically considered at the gene level. At the same time, the size of clinical cohorts often is still limited, calling for novel analysis strategies for identifying potentially prognostic SNPs that can help to better characterize disease processes. We propose such a strategy, drawing on univariate testing ideas from epidemiological case-controls studies on the one hand, and multivariable regression techniques as developed for gene expression data on the other hand. In particular, we focus on …
A clustering package for nucleotide sequences using Laplacian Eigenmaps and Gaussian Mixture Model.
2018
International audience; In this article, a new Python package for nucleotide sequences clustering is proposed. This package, freely available on-line, implements a Laplacian eigenmap embedding and a Gaussian Mixture Model for DNA clustering. It takes nucleotide sequences as input, and produces the optimal number of clusters along with a relevant visualization. Despite the fact that we did not optimise the computational speed, our method still performs reasonably well in practice. Our focus was mainly on data analytics and accuracy and as a result, our approach outperforms the state of the art, even in the case of divergent sequences. Furthermore, an a priori knowledge on the number of clust…
The Role of Mathematical Models in Immuno-Oncology: Challenges and Future Perspectives
2021
Immuno-oncology (IO) focuses on the ability of the immune system to detect and eliminate cancer cells. Since the approval of the first immune checkpoint inhibitor, immunotherapies have become a major player in oncology treatment and, in 2021, represented the highest number of approved drugs in the field. In spite of this, there is still a fraction of patients that do not respond to these therapies and develop resistance mechanisms. In this sense, mathematical models offer an opportunity to identify predictive biomarkers, optimal dosing schedules and rational combinations to maximize clinical response. This work aims to outline the main therapeutic targets in IO and to provide a description …
Challenges and advances for the treatment of renal cancer patients with brain metastases: From immunological background to upcoming clinical evidence…
2021
The introduction of checkpoint inhibitors (ICIs) in renal cell carcinoma (RCC) treatment landscape, resulted in improvements in overall survival (OS) in metastatic patients. Brain metastases (BMs) are a specific metastatic site of interest representing a predictive factor of poor prognosis. Patients with BMs were usually excluded from prospective clinical trials in the past. Despite recent evidence suggest the efficacy and safety of ICIs, the BMs treatment remains a challenge; the immunotherapy responsiveness seems to be multifactorial and dependent on several factors, such as the genetic intratumor heterogeneity and the immunosuppressive role of the brain tumor microenvironment. This revie…
Optimization of Lead Placement in the Right Ventricle During Cardiac Resynchronization Therapy. A Simulation Study
2019
[EN] Patients suffering from heart failure and left bundle branch block show electrical ventricular dyssynchrony causing an abnormal blood pumping. Cardiac resynchronization therapy (CRT) is recommended for these patients. Patients with positive therapy response normally present QRS shortening and an increased left ventricle (LV) ejection fraction. However, around one third do not respond favorably. Therefore, optimal location of pacing leads, timing delays between leads and/or choosing related biomarkers is crucial to achieve the best possible degree of ventricular synchrony during CRT application. In this study, computational modeling is used to predict the optimal location and delay of p…
PBRM1 loss is a late event during the development of cholangiocarcinoma
2017
Aims: Somatic mutations in genes encoding chromatin remodellers have been reported recently in several cancer types, including approximately half of cholangiocarcinomas. One of the most commonly mutated chromatin remodellers in cholangiocarcinoma is the Polybromo-1 (PBRM1) gene located on chromosome 3p21, which encodes a subunit of the SWI/SNF complex. The aim of this study was to determine the timing of PBRM1 mutations in biliary carcinogenesis. Methods and results: In order to accomplish this goal, we used immunohistochemistry to assess PBRM1 protein expression in a series of precursor lesions and invasive biliary carcinomas. Previous studies have correlated loss of protein expression on …
Detection of mast cells in ameloblastomas and odontogenic keratocysts
2020
Background MCs (MCs) have been ascribed to mediating several diseases, including malignant neoplasms. These cells can play a role in angiogenesis, tissue remodeling and immune modulation and favor neoplasm progression. Despite the studies analyzing the contribution of MCs in odontogenic lesions, its biological behavior in ameloblastomas (AMBs) and odontogenic keratocysts (OKCs) remains unclear. This study aims to detect MCs in OKCs and AMBs and clarify the role of MCs in these lesions. Material and methods A total of 40 odontogenic lesions were analyzed. This included 20 OKCs and 20 AMBs, 10 being the solid type and the other 10 being the unicystic type of AMB. All cases were histologically…
Mathematical model of T-cell lymphoblastic lymphoma: disease, treatment, cure or relapse of a virtual cohort of patients
2017
International audience; T lymphoblastic lymphoma (T-LBL) is a rare type of lymphoma with a good prognosis with a remission rate of 85%. Patients can be completely cured or can relapse during or after a 2-year treatment. Relapses usually occur early after the remission of the acute phase. The median time of relapse is equal to 1 year, after the occurrence of complete remission (range 0.2–5.9 years) (Uyttebroeck et al., 2008). It can be assumed that patients may be treated longer than necessary with undue toxicity. The aim of our model was to investigate whether the duration of the maintenance therapy could be reduced without increasing the risk of relapses and to determine the minimum treatm…
A Thermodynamic Model of Monovalent Cation Homeostasis in the Yeast Saccharomyces cerevisiae
2016
Cationic and heavy metal toxicity is involved in a substantial number of diseases in mammals and crop plants. Therefore, the understanding of tightly regulated transporter activities, as well as conceiving the interplay of regulatory mechanisms, is of substantial interest. A generalized thermodynamic description is developed for the complex interplay of the plasma membrane ion transporters, membrane potential and the consumption of energy for maintaining and restoring specific intracellular cation concentrations. This concept is applied to the homeostasis of cation concentrations in the yeast cells of S. cerevisiae. The thermodynamic approach allows to model passive ion fluxes driven by the…