Challenges for machine learning in RNA-protein interaction prediction

Arora Viplove () and Sanguinetti Guido ()
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Arora Viplove: Data Science, Department of Physics, International School for Advanced Studies (SISSA), Trieste 34136, Italy
Sanguinetti Guido: Data Science, Department of Physics, International School for Advanced Studies (SISSA), Trieste 34136, Italy

Statistical Applications in Genetics and Molecular Biology, 2022, vol. 21, issue 1, 11

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.

Keywords: graph neural networks; graphs; higher-order interactions; noisy data; RNA-protein interactions (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1515/sagmb-2021-0087

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