Project description:Here we introduce a computer-guided design tool that combines a computational framework for prioritizing more efficient combinations of instructive factors (IFs) of cellular conversions, called IRENE, with a transposon-based genomic integration system for efficient delivery. Particularly, IRENE relies on a stochastic gene regulatory network model that systematically prioritizes more efficient IFs by maximizing the agreement of the transcriptional and epigenetic landscapes between the converted and target cells. Our predictions substantially increased the efficiency of two established iPSC-differentiation protocols (natural killer cells and melanocytes) and established the first protocol for iPSC-derived mammary epithelial cells with high efficiency.

2021-02-02 | GSE165961 | GEO

Dynamic estimation in the extended marginal Rasch model with an application to mathematical computer-adaptive practice.

Project description: Not available

| S-EPMC7003866 | biostudies-literature

Development of Random Forest Algorithm Based Prediction Model of Alzheimer's Disease Using Neurodegeneration Pattern.

Project description: Not available

| S-EPMC7897872 | biostudies-literature

A Random-Forest Based Algorithm for Prediction of Enhancers From Histone Modifications

Project description:Transcriptional enhancers play critical roles in regulation of gene expression, but their identification has remained a challenge. Recently, it was shown that enhancers in the mammalian genome are associated with characteristic histone modification patterns, which have been increasingly exploited for enhancer identification. However, only a limited number of histone modifications have previously been investigated for this purpose, leaving the questions answered whether there exist an optimal set of histone modifications that could improve the enhancer prediction. Here, we address this issue by exploring a rich dataset produced by the human Epigenome Roadmap Project. Specifically, we examined genome-wide profiles of 24 histone modifications in human embryonic stem cells and fibroblasts, and developed a Random-Forest based algorithm to integrate histone modification profiles for identification of enhancers.As a training set, we used histone modification profiles at genome-wide binding sites of p300 in the two cell types identified using ChIP-seq. We show that this algorithm not only leads to more accurate and precise prediction of enhancers than previous methods, but also helps identify an optimal set of three chromatin marks for enhancer prediction.

2012-05-10 | GSE37858 | GEO

Optimization of a real municipal sewage treatment plant using CRFSMA algorithm and a mathematical model.

Project description: Not available

| S-EPMC11336287 | biostudies-literature