Due to recent technological advances, big data acquisition of molecular entities has been realized, and one of the biggest challenges for elucidating disease mechanisms is to understand the topology and dynamics of relationships between entities. Therefore, advanced analytic methods are desperately needed to systematically integrate these data and bridge data analysis to the mechanistic understanding of diseases.
We applied state-of-the-art integrative systems biology methods to combine multidimensional OMICs data to reveal underlying biological networks established in the principles of Mendelian Randomization.
Using machine learning algorithms, we explored the networks and evaluated the conjoint contributions of genetic variants across the genome, and variation in the circulating transcriptomics and metabolomics to interindividual variation in cardiovascular disease traits and endpoints.
Identification of biological networks facilitated understanding of OMICs system and identifying targets for “intervention” and “prediction”, elucidating gene networks, pathways, and disease modules impacting health and cardiovascular disease.
What we learn from the systematic analysis of OMICs is critical for the development and application of new drugs that target risk factors of diseases. It provides a complete context to interpret the findings and therefore, provides insights into complex processes and advances the understanding of the molecular etiology of diseases.