Improving the Diagnostic Potential of Extracellular miRNAs Coupled to Multiomics Data by Exploiting the Power of Artificial Intelligence

Abstract

In recent years, the clinical use of extracellular miRNAs as potential biomarkers of disease has increasingly emerged as a new and powerful tool. Serum, urine, saliva and stool contain miRNAs that can exert regulatory effects not only in surrounding epithelial cells but can also modulate bacterial gene expression, thus acting as a “master regulator” of many biological processes. We think that in order to have a holistic picture of the health status of an individual, we have to consider comprehensively many “omics” data, such as miRNAs profiling form different parts of the body and their interactions with cells and bacteria. Moreover, Artificial Intelligence (AI) and Machine Learning (ML) algorithms coupled to other multiomics data (i.e., big data) could help researchers to classify better the patient’s molecular characteristics and drive clinicians to identify personalized therapeutic strategies. Here, we highlight how the integration of “multiomic” data (i.e., miRNAs profiling and microbiota signature) with other omics (i.e., metabolomics, exposomics) analyzed by AI algorithms could improve the diagnostic and prognostic potential of specific biomarkers of disease.

Utilizing systems genetics to enhance understanding into molecular targets of skin cancer

“…Specifically, in data collection, AI models streamline the extraction of information from diverse gene databases by utilizing natural language processing (NLP) techniques. 47,48 NLP techniques effectively integrate multi-omics data, spanning genomics, transcriptomics, epigenomics and proteomics, providing a more comprehensive perspective on biological systems. Furthermore, NLP AI models can be trained to extract information related to external factors from literature and databases, enabling researchers to efficiently prioritize candidate genes while considering external influences.…”

“…Another field within systems genetics that is rapidly growing to overcome constraints of conventional systems genetics method is AI. Specifically, in data collection, AI models streamline the extraction of information from diverse gene databases by utilizing natural language processing (NLP) techniques 47,48 . NLP techniques effectively integrate multi‐omics data, spanning genomics, transcriptomics, epigenomics and proteomics, providing a more comprehensive perspective on biological systems.…”

Toll-like receptor-guided therapeutic intervention of human cancers: molecular and immunological perspectives

“…Being involved in regulation at a post-transcriptional level as either oncogenes or tumor suppressors, miRNAs can either inhibit the translation or facilitate the cleavage of their targeted mRNA. Moreover, miRNAs gained great interest for their potential use as biomarkers in several human diseases (153)(154)(155). It has been reported that induction of miRNAs by TLR ligands could affect TLR pathway activation and initiate the signaling cascade of immune response (e.g., through stimulating NF-kB signal downstream to TLRs) (156, 157).…”