Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Review
. 2022 May 2;21(1).
doi: 10.1515/sagmb-2021-0087.

Challenges for machine learning in RNA-protein interaction prediction

Viplove Arora  1 , Guido Sanguinetti  1
Affiliations
Review

Challenges for machine learning in RNA-protein interaction prediction

Viplove Arora et al. Stat Appl Genet Mol Biol. .

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: RNA-protein interactions; graph neural networks; graphs; higher-order interactions; noisy data.

PubMed Disclaimer

References

    1. Adadi, A. and Berrada, M. (2018). Peeking inside the black-box: a survey on explainable artificial intelligence (XAI). IEEE Access 6: 52138–52160. https://doi.org/10.1109/access.2018.2870052.
    1. Aittokallio, T. and Schwikowski, B. (2006). Graph-based methods for analysing networks in cell biology. Briefings Bioinf. 7: 243–255. https://doi.org/10.1093/bib/bbl022.
    1. Alipanahi, B., Delong, A., Weirauch, M.T., and Frey, B.J. (2015). Predicting the sequence specificities of DNA-and RNA-binding proteins by deep learning. Nat. Biotechnol. 33: 831–838. https://doi.org/10.1038/nbt.3300.
    1. Arora, V. and Sanguinetti, G. (2021). De novo prediction of RNA-protein interactions with graph neural networks. bioRxiv.
    1. Arrieta, A.B., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., García, S., Gil-López, S., Molina, D., Benjamins, R., et al.. (2020). Explainable artificial intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI. Inf. Fusion 58: 82–115. https://doi.org/10.1016/j.inffus.2019.12.012.