Machine learning in the development of targeting microRNAs in human disease

Abstract

A microRNA is a small, single-stranded, non-coding ribonucleic acid that plays a crucial role in RNA silencing and can regulate gene expression. With the in-depth study of miRNA in development and disease, miRNA has become an attractive target for novel therapeutic strategies. Exploring miRNA targeting therapy only through experiments is expensive and laborious, so it is essential to develop novel and efficient computational methods to narrow down the search. Recent advances in machine learning applied in biomedical informatics provide opportunities to explore miRNA-targeting drugs, thus promoting miRNA therapeutics. This review provides an overview of recent advancements in miRNA targeting therapeutic using machine learning. First, we mainly describe the basics of predicting miRNA targeting drugs, including pharmacogenomic data resources and data preprocessing. Then we present primary machine learning algorithms and elaborate their application in discovering relationships among miRNAs, drugs, and diseases. Along with the progress of miRNA targeting therapeutics, we finally analyze and discuss the current challenges and opportunities that machine learning confronts.

The leading global health challenges in the artificial intelligence era

“…MiRNAs, a small, single-stranded, non-coding ribonucleic acids (RNAs), are essential for all biological functions including cancer development. ML provides an opportunity to explore miRNA’s ability to serve as a reliable biomarker targeting drugs and improve cancer clinical classification ( 58 , 59 ).…”

Analysis of Amblyomma americanum microRNAs in response to Ehrlichia chaffeensis infection and their potential role in vectorial capacity

“…Understanding the molecular interactions between tick vectors and E. chaffeensis , and the characterization of differentially regulated tick miRNAs are needed to develop new approaches to combat tick-borne infections. Identification of new and novel miRNAs by using new sequencing platforms opened up a new avenue of research ( Artigas-Jerónimo et al., 2019 ; Kumar et al., 2022 ; Luo et al, 2023 ). In this work, a small RNA sequencing approach was utilized to identify miRNAs induced by E. chaffeensis in A. americanum tissues, and differentially expressed miRNAs were functionally characterized using a miRNA inhibitor approach.…”