RNASSIST

RNASSIST integrates differential expression analysis and gene co-expression networks using machine learning to identify disease-associated genes in transcriptomic datasets with small effect sizes.

Key Features:

• Differential Expression and Co-Expression Integration: Combines RNA-seq–based differential expression results with gene co-expression network analysis to detect candidate genes not identified by expression changes alone.
• Machine Learning–Based Signal Extraction: Applies machine learning algorithms to link differential expression and network features, enabling detection of subtle gene interactions in tissues with small effect sizes such as brain tissue.

Scientific Applications:

• Disease Transcriptomics: Identifies genes and pathways associated with complex conditions, including Alcohol Use Disorder (AUD), from post-mortem brain RNA-seq data.

Methodology:

RNASSIST synthesizes differential expression metrics with co-expression network topology and applies machine learning models to prioritize disease-relevant genes, incorporating validation strategies to confirm biological relevance.