From sequences to therapeutics: Using machine learning to predict chemically modified siRNA activity.

Autor: Martinelli DD; Cornell University, Ithaca, NY 14850, United States of America. Electronic address: ddm94@cornell.edu.
Jazyk: angličtina
Zdroj: Genomics [Genomics] 2024 Mar; Vol. 116 (2), pp. 110815. Date of Electronic Publication: 2024 Mar 01.
DOI: 10.1016/j.ygeno.2024.110815
Abstrakt: Small interfering RNAs (siRNAs) exemplify the promise of genetic medicine in the discovery of novel therapeutic modalities. Their ability to selectively suppress gene expression makes them ideal candidates for the development of oligonucleotide pharmaceuticals. Recent advancements in machine learning (ML) have facilitated the design of unmodified siRNA and efficacy prediction. However, a model trained to predict the silencing activity of siRNAs with diverse chemical modification patterns is yet to be published despite the importance of such modifications in designing siRNAs with the potential to reach the level of clinical use. This study presents the first application of ML to efficiently classify chemically modified siRNAs on the basis of sequence and chemical modification patterns alone. Three algorithms were evaluated at three classification thresholds and compared according to sensitivity, specificity, consistency of feature weights with empirical knowledge, and performance using an external validation dataset. Finally, possible directions for future research were proposed.
Competing Interests: Declaration of competing interest The author declares no competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2023. Published by Elsevier Inc.)
Databáze: MEDLINE
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