Abstract
It has been well established that understanding the underlying heterogeneity of numerous complex disease process needs new
strategies that present in precision medicine for prediction, prevention, and personalized treatment strategies. This approach
must be tailored for each individual’s unique omics that lead to personalized management of disease. The correlation between
different omics data should be considered in the precision medicine approach. The interaction provides a hypothesis which is
called the domino effect in the present minireview. Here we review the various potentials of omics data including genomics,
transcriptomics, proteomics, metabolomics, and pharmacogenomics. We comprehensively summarize the impact of omics data
and its major role in precision medicine and provide a description of the domino effect on the pathophysiology of diseases.
Each constituent of the omics data typically provides different information associated with the disease. Current research,
although inadequate, clearly indicates that the information of omics data can be applicable to the concept of precision medicine.
Integration of different omics data type in the domino effect hypothesis can explain the causative changes of disease as it
is discussed in the system biology too. While most existing studies investigate omics data separately, data integration is
needed on the horizon of precision medicine by using machine learning.
Keywords: Omics; Precision medicine; Personalized medicine