Learn how to predict transcription factor direct targets with BETA, a powerful tool for integrating ChIP-seq and RNA-seq data. Now modernized for Python 3 with Claude Code.

I can't understand this problem. Even when I use the data and code offered at https://learn.gencore.bio.nyu.edu/rna-seq-analysis/gene-set-enrichment-analysis/ , the problem also existed. I did the analysis follow the official illustrate. As bellows: library(clusterProfiler) library(enrichplot) library(ggplot2) library(org.Dm.eg.db) library(DOSE) library(biomaRt) df = read.csv("~/lineagepaper/drosphila_example_de.csv", header=TRUE) original_gene_list

Background: Myalgic encephalomyelitis (ME)/chronic fatigue syndrome (CFS) is a disabling multisystem complex disorder with no known etiology or approved treatment. Our studies aimed to assess the link...

AbstractSummary. GSEA is a standard approach for pathway interpretation, yet Python ecosystems lack a high-performance implementation aligned with the fgse

Hi Alex, A (reproducible) issue ("GSEA hangs") was posted on the clusterProfiler GitHub. See: YuLab-SMU/clusterProfiler#659 (comment), and posts below that one. Since clusterProfiler uses under the...

R package for enrichment analysis of chemical–gene interactions from the Comparative Toxicogenomics Database (CTD). Four methods through a unified interface: ORA, GSEA, CAMERA, GSVA.

afropages.fr - Réforme des pensions : La GSEA réclame des consultations [Lexpress mu]

afropages.fr - GSEA : « Quel avenir pour les ex-Prevocational Teachers ? » [Le Mauricien]

À quelques jours de la présentation du Budget 2026-2027, la Government Services Employees Association (GSEA) multiplie les démarches auprès des autorités pour faire entendre les préoccupations des fonctionnaires et des retraités du secteur public. Dans trois correspondances adressées au Premier ministre et ministre des Finances, Navin Ramgoolam, le syndicat réclame des mesures concrètes afin de remédier à plusieurs

Background Prioritizing therapeutics from transcriptomic data remains a key challenge in precision medicine. Signature reversal approaches, most commonly implemented through Gene Set Enrichment Analysis (GSEA), have been widely used to match disease signatures to candidate drugs. However, enrichment-based methods can be sensitive to noise and are restricted to previously profiled compounds Methods We developed RANKOR, a machine-learning framework designed to rank candidate drugs directly from transcriptomic signatures. Rather than predicting full expression profiles, RANKOR learns structured latent representations of transcriptional responses alongside chemical structure, enabling prioritization from standardized signatures derived from disease states or treatment perturbations. The framework is applicable to both bulk and single-cell transcriptomic data. Results Across large-scale perturbational datasets, RANKOR achieved consistently lower median ranks than similarity- and distance-ba

Background Prioritizing therapeutics from transcriptomic data remains a key challenge in precision medicine. Signature reversal approaches, most commonly implemented through Gene Set Enrichment Analysis (GSEA), have been widely used to match disease signatures to candidate drugs. However, enrichment-based methods can be sensitive to noise and are restricted to previously profiled compounds Methods We developed RANKOR, a machine-learning framework designed to rank candidate drugs directly from transcriptomic signatures. Rather than predicting full expression profiles, RANKOR learns structured latent representations of transcriptional responses alongside chemical structure, enabling prioritization from standardized signatures derived from disease states or treatment perturbations. The framework is applicable to both bulk and single-cell transcriptomic data. Results Across large-scale perturbational datasets, RANKOR achieved consistently lower median ranks than similarity- and distance-ba