Versatile knowledge guided network inference method for prioritizing key regulatory factors in multi-omics data.

Area of Science:

• Bioinformatics
• Computational Biology
• Systems Biology

Background:

• High-throughput molecular profiling generates vast multi-omics datasets.
• Integrated analysis of multi-omics data offers deeper insights into disease pathophysiology but is underutilized due to complexity and lack of tools.

Purpose of the Study:

• To introduce a versatile, fully integrated approach for multi-level omics data analysis.
• To leverage prior biological knowledge for robust network inference from multi-omics data.

Main Methods:

• Developed the Knowledge guided Multi-Omics Network inference (KiMONo) approach.
• Utilized statistical models to combine omics measurements.
• Integrated prior biological information through a knowledge-guided strategy.

Main Results:

• KiMONo generates multimodal networks where nodes represent diverse omics features (e.g., variants, genes) and edges signify statistically derived and knowledge-supported associations.
• The method demonstrates robustness to noise in multi-omics data.
• Successfully applied to the full spectrum of multi-omics data.

Conclusions:

• KiMONo is a powerful tool for fully integrated multi-omics data analysis.
• The approach effectively leverages prior biological knowledge for network inference.
• Facilitates the detection of biomarker candidates by unlocking the full potential of multi-omics datasets.