CGPE

CGPE analyzes TCGA and GEO transcriptional datasets and PubMed literature to identify gene-level associations, perform GSEA-based pathway enrichment, and prioritize cell lines using dependency, expression, and pathway activity data.

Key Features:

• GenePub HotIndex: Uses natural language processing, statistical analysis, and association discovery on PubMed publications to reveal research trends, correlations with cancer types, top-related genes, and WordCloud publication profiles.
• OnlineGSEA: Performs GSEA-based gene set enrichment on public and proprietary transcriptional datasets using the GSEA algorithm and integrated preprocessed TCGA and GEO datasets to detect pathway alterations and gene signatures.
• CellLine Search: Combines cell line dependency data, gene expression profiles, and pathway activity maps to provide evidence for selecting appropriate cell lines for experiments.
• Preprocessed TCGA and GEO datasets: Integrates curated, preprocessed transcriptional data from TCGA and GEO for downstream enrichment, association, and expression analyses.

Scientific Applications:

• Gene expression and pathway analysis: Detects pathway alterations and gene signatures across TCGA and GEO transcriptional datasets using GSEA.
• Literature-driven gene prioritization: Identifies research trends and gene associations from PubMed to inform candidate gene selection.
• Cell line selection and experimental planning: Prioritizes cell lines based on dependency, expression, and pathway activity data to guide experimental design.
• Hypothesis generation and data interpretation: Supports preliminary hypothesis formation and interpretation of complex transcriptional datasets through combined literature, enrichment, and association analyses.

Methodology:

Uses natural language processing for PubMed analysis, statistical methods and association discovery for data associations, the GSEA algorithm for gene set enrichment, and preprocessed TCGA and GEO transcriptional datasets for analyses.