Poster Presentation 44th Lorne Genome Conference 2023

PRIMITI: A machine learning model for accurate prediction of miRNA target identification (#149)

Korawich Uthayopas 1 2 3 , Alex G. C. de Sá 1 2 3 4 , Azadeh Alavi 5 , Douglas E. V. Pires 1 2 3 6 , David B. Ascher 1 2 3 4
  1. Systems and Computational Biology, Bio21 Institute, University of Melbourne, Parkville, Victoria, 3052, Australia
  2. School of Chemistry and Molecular Biosciences, University of Queensland, Brisbane , Queensland, 4072 , Australia
  3. Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, 3004, Australia
  4. Baker Department of Cardiometabolic Health, Melbourne Medical School, University of Melbourne, Parkville, Victoria, 3010, Australia
  5. School of Computational Technology, RMIT University, Melbourne, Victoria, 3000, Australia
  6. School of Computing and Information Systems, University of Melbourne, Parkville , Victoria, 3052, Australia

Increasing numbers of studies have highlighted the significance of miRNAs in human gene regulation, with their dysregulation linked to a variety of diseases, including cancers and neurological disorders1. miRNAs play a crucial role through post-transcriptional gene suppression, targeting the majority of human genes, with potential to act as highly selective therapeutics2-3. In order to further our understanding of their biological roles, including in disease and use as therapeutics, a comprehensive map of miRNA functions, including their targets and repression levels is required.

Due to experimental constraints, a number of computational models4-6 for prioritising potential miRNA-target mRNA repression activities have been proposed to assist functional characterisation, but with limited predictive performance and restricted usability. To address these limitations, we developed PRIMITI - a PRedictive model for the identification of new MIRNA-Target mRNA Interactions. PRIMITI utilises machine learning to model the molecular interactions between miRNAs and target sites in 3’-untranslated regions (3’-UTR) and to predict miRNA-mRNA suppression activities. To counteract natural publication bias, negative sample selection was used to produce reliable negative samples, resulting in high-quality data. PRIMITI then used information on single nucleotide polymorphisms (SNPs) and general physicochemical descriptors to improve the characterization of functional target sites.  

On cross-validation and independent blind test sets, PRIMITI achieved robust predictive performances, achieving Matthew’s correlation coefficients of up to 0.81 for identification of miRNA-target sites and up to 0.77 for prediction of the resulting mRNA repression activity. The model was further validated with an external blind microarray dataset7 and a list of experimentally validated miRNA-mRNA interactions collected from miRTarbase and Tarbase8,9. Across both validations, PRIMITI successfully predicted miRNA-mediated repression, outperforming current state-of-the-art methods4-6, confirming the model’s utility for preliminary screening. We have made PRIMITI publicly available through a user-friendly web server at https://biosig.lab.uq.edu.au/primiti.

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