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Challenges for machine learning in RNA-protein interaction prediction

Author

Listed:
  • Arora Viplove
  • Sanguinetti Guido

    (Data Science, Department of Physics, International School for Advanced Studies (SISSA), Trieste, 34136, Italy)

Abstract

RNA-protein interactions have long being recognised as crucial regulators of gene expression. Recently, the development of scalable experimental techniques to measure these interactions has revolutionised the field, leading to the production of large-scale datasets which offer both opportunities and challenges for machine learning techniques. In this brief note, we will discuss some of the major stumbling blocks towards the use of machine learning in computational RNA biology, focusing specifically on the problem of predicting RNA-protein interactions from next-generation sequencing data.

Suggested Citation

  • Arora Viplove & Sanguinetti Guido, 2022. "Challenges for machine learning in RNA-protein interaction prediction," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 21(1), pages 1-11, January.
    • Handle: RePEc:bpj:sagmbi:v:21:y:2022:i:1:p:11:n:2
    DOI: 10.1515/sagmb-2021-0087
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