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Zhu A, Chiba S, Shimizu Y, Kunitake K, Okuno Y, Aoki Y, Yokota T. Ensemble-Learning and Feature Selection Techniques for Enhanced Antisense Oligonucleotide Efficacy Prediction in Exon Skipping. Pharmaceutics 2023; 15:1808. [PMID: 37513994 PMCID: PMC10384346 DOI: 10.3390/pharmaceutics15071808] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 06/13/2023] [Accepted: 06/15/2023] [Indexed: 07/30/2023] Open
Abstract
Antisense oligonucleotide (ASO)-mediated exon skipping has become a valuable tool for investigating gene function and developing gene therapy. Machine-learning-based computational methods, such as eSkip-Finder, have been developed to predict the efficacy of ASOs via exon skipping. However, these methods are computationally demanding, and the accuracy of predictions remains suboptimal. In this study, we propose a new approach to reduce the computational burden and improve the prediction performance by using feature selection within machine-learning algorithms and ensemble-learning techniques. We evaluated our approach using a dataset of experimentally validated exon-skipping events, dividing it into training and testing sets. Our results demonstrate that using a three-way-voting approach with random forest, gradient boosting, and XGBoost can significantly reduce the computation time to under ten seconds while improving prediction performance, as measured by R2 for both 2'-O-methyl nucleotides (2OMe) and phosphorodiamidate morpholino oligomers (PMOs). Additionally, the feature importance ranking derived from our approach is in good agreement with previously published results. Our findings suggest that our approach has the potential to enhance the accuracy and efficiency of predicting ASO efficacy via exon skipping. It could also facilitate the development of novel therapeutic strategies. This study could contribute to the ongoing efforts to improve ASO design and optimize gene therapy approaches.
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Affiliation(s)
- Alex Zhu
- Phillips Academy, Andover, MA 01810, USA
- Department of Medical Generics, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB T6G 2H7, Canada
| | - Shuntaro Chiba
- HPC- and AI-Driven Drug Development Platform Division, RIKEN Center for Computational Science, Yokohama 230-0045, Japan
| | - Yuki Shimizu
- Department of Biomedical Data Intelligence, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan
| | - Katsuhiko Kunitake
- Department of Molecular Therapy, National Institute of Neuroscience, National Center of Neurology and Psychiatry (NCNP), Kodaira, Tokyo 187-8551, Japan
| | - Yasushi Okuno
- HPC- and AI-Driven Drug Development Platform Division, RIKEN Center for Computational Science, Yokohama 230-0045, Japan
- Department of Biomedical Data Intelligence, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan
| | - Yoshitsugu Aoki
- Department of Molecular Therapy, National Institute of Neuroscience, National Center of Neurology and Psychiatry (NCNP), Kodaira, Tokyo 187-8551, Japan
| | - Toshifumi Yokota
- Department of Medical Generics, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB T6G 2H7, Canada
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