ALE

ALE extracts and predicts standardized labels for age, gender, and tissue from open-ended textual metadata in NCBI's Gene Expression Omnibus (GEO) to enable stratified gene expression analyses and meta-analyses.

Key Features:

• Automated Label Extraction: Extracts labels from open-ended, non-standardized textual descriptions within GEO metadata.
• Machine Learning Integration: Trains machine learning models on existing labeled data and gene expression patterns to predict missing labels such as age and gender.
• Dual Methodology: Combines heuristic direct text analysis with machine learning applied to gene expression profiles to assign and refine labels.
• Performance Evaluation: Benchmarks label assignments against manually curated gold standards to evaluate accuracy for age, gender, and tissue type.

Scientific Applications:

• Meta-Analysis Facilitation: Standardizes key covariates (age, gender, tissue) across GEO datasets to enable stratified and cross-study meta-analyses of gene expression.
• Demographic and Tissue-Specific Studies: Supports analyses of gene expression differences across demographic groups (e.g., gender, age) and tissue types by providing inferred metadata.
• Reduced Manual Curation: Lowers the need for manual metadata curation by providing automated and predicted labels for downstream analyses.

Methodology:

Applies heuristic direct text analysis on GEO metadata fields and trains machine learning models using gene expression profiles and text-derived labels to predict missing labels, with performance evaluated against manually curated gold standards.