Advanced computational predictive models of miRNA-mRNA interaction efficiency

Sharon Bader, Tamir Tuller*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

The modeling of miRNA-mRNA interactions holds significant implications for synthetic biology and human health. However, this research area presents specific challenges due to the multifaceted nature of mRNA downregulation by miRNAs, influenced by numerous factors including competition or synergism among miRNAs and mRNAs. In this study, we present an improved computational model for predicting miRNA-mRNA interactions, addressing aspects not previously modeled. Firstly, we integrated a novel set of features that significantly enhanced the predictor's performance. Secondly, we demonstrated the cell-specific nature of certain aspects of miRNA-mRNA interactions, highlighting the importance of designing models tailored to specific cell types for improved accuracy. Moreover, we introduce a miRNA binding site interaction model (miBSIM) that, for the first time, accounts for both the distribution of miRNA binding sites along the mRNA and their respective strengths in regulating mRNA stability. Our analysis suggests that distant miRNA sites often compete with each other, revealing the intricate interplay of binding site interactions. Overall, our new predictive model shows a significant improvement of up to 6.43% over previous models in the field. The code of our model is available at https://www.cs.tau.ac.il/~tamirtul/miBSIM

Original languageEnglish
Pages (from-to)1740-1754
Number of pages15
JournalComputational and Structural Biotechnology Journal
Volume23
DOIs
StatePublished - Dec 2024

Funding

FundersFunder number
Koret-UC Berkeley-Tel Aviv University

    Keywords

    • Biophysics
    • Competition
    • MRNA
    • Machine Learning
    • MiRNAs

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