Biomedical Data Analysis

Integrative analysis of genomic and transcriptomic profiles to identify molecular subtypes, characterize subtype-associated pathways, and explore potential therapeutic vulnerabilities and biomarkers.

Integration of genomic, transcriptomic, proteomic, and clinical data to characterize disease-associated molecular changes, investigate biological pathways, and identify candidate biomarkers.

Enrichment analysis of gene or protein sets to identify overrepresented biological functions, pathways, and processes, supporting the interpretation of high-throughput omics data.

Comparative gene expression analysis across biological groups or conditions to identify differentially expressed genes and characterize associated molecular changes.

Integration of molecular and clinical features to identify candidate biomarkers and develop predictive or prognostic models for disease characterization and therapeutic response assessment.

Computational analysis of miRNA–target interactions to investigate gene regulatory networks, prioritize candidate targets, and explore their potential roles in disease-associated pathways