IntOMICS

IntOMICS integrates multi-omics data to infer genome-level regulatory networks using Bayesian networks and both existing and empirical biological prior knowledge.

Key Features:

• Multi-Omics Integration: Integrates gene expression, DNA methylation, and copy number variation to provide a multi-layer view of molecular interactions.
• Incorporation of Prior Knowledge: Incorporates existing biological prior knowledge alongside empirical biological knowledge derived from experimental data.
• Bayesian Network-Based Inference: Uses Bayesian networks as the core probabilistic framework to model dependencies and uncertainties in regulatory interactions.
• Empirical Knowledge Estimation: Estimates empirical biological knowledge from available experimental datasets to complement gaps in existing priors.
• Modeling Crosstalk and Enhanced Accuracy: Models crosstalk between omics modalities and demonstrates improved accuracy compared with methods using gene expression alone.
• Comparative Benchmarking: Has been benchmarked against other multi-omics regulatory network inference algorithms that incorporate prior knowledge and shown superior accuracy and insight.

Scientific Applications:

• Regulatory Network Inference: Infers comprehensive regulatory networks that elucidate interactions governing gene regulation and expression.
• Personalized Medicine: Provides molecular-mechanism insights applicable to developing personalized therapeutic strategies for complex diseases.
• Biomarker Discovery: Supports identification of potential predictive biomarkers, including applications to microsatellite stable stage III colon cancer samples, with utility in precision oncology.
• Exploratory Systems Biology: Enables exploratory analyses of intricate biological mechanisms across multiple omics layers.

Methodology:

Integrates gene expression, DNA methylation, and copy number variation data; performs Bayesian network inference; incorporates existing biological prior knowledge and empirical knowledge estimated from experimental datasets; validated using known associations in microsatellite stable/instable colon cancer samples and benchmarked against other prior-knowledge multi-omics inference algorithms.