Examining Machine Learning Models That Predict sgRNA Cleavage Efficiencies
DOI:
https://doi.org/10.47611/jsrhs.v11i3.3588Keywords:
CRISPR-cas9, sgRNA, RNA, DNA, AI, Computer Science, ML, Artificial Intelligence, Machine Learning, Neural Networks, CNN, Genes, Gene, Genome Engineering, Genetics, Engineering, Deep Learning, XGBoost, Boosting, Coding, Science, Data Science, Biomedical, Biomedical EngineeringAbstract
Ever since its discovery, CRISPR-Cas9 has taken over the world in gene editing. By providing a single guide RNA to the cas9 enzyme, CRISPR-Cas9 can immediately pinpoint the target gene location in the genome and slice it. Scientists discovered a revolutionary way to use this method for gene editing. Yet, the challenge is that the CRISPR-Cas9 system is lenient with the matching precision of the guide RNA to the target sequence As a result, the CRISPR-cas9 system may also cleave certain healthy sequences that are almost identical to the target sequence. This paper aims to find the best model that uses machine learning to predict an optimal sgRNA design.
Downloads
References or Bibliography
Abadi, S., Yan, W. X., Amar, D., & Mayrose, I. (2017). A machine learning approach for predicting CRISPR-Cas9 cleavage efficiencies and patterns underlying its mechanism of action. PLOS Computational Biology, 13(10), e1005807. https://doi.org/10.1371/journal.pcbi.1005807
Bentéjac, Candice & Csörgő, Anna & Martínez-Muñoz, Gonzalo. (2019). A Comparative
Analysis of XGBoost.
Chicco, D. (2017). Ten quick tips for machine learning in computational biology. BioData Mining, 10(1). https://doi.org/10.1186/s13040-017-0155-3
Choi RY, Coyner AS, Kalpathy-Cramer J, Chiang MF, Campbell JP. Introduction to
Machine Learning, Neural Networks, and Deep Learning. Transl Vis Sci Technol. 2020 Feb
;9(2):14. doi: 10.1167/tvst.9.2.14. PMID: 32704420; PMCID: PMC7347027.
Chuai, G., Ma, H., Yan, J., Chen, M., Hong, N., Xue, D., Zhou, C., Zhu, C., Chen, K., Duan, B., Gu, F., Qu, S., Huang, D., Wei, J., & Liu, Q. (2018). DeepCRISPR: optimized CRISPR guide RNA design by deep learning. Genome Biology, 19(1). https://doi.org/10.1186/s13059-018-1459-4
Deo, R. C. (2015). Machine Learning in Medicine. Circulation, 132(20), 1920–1930. https://doi.org/10.1161/circulationaha.115.001593
Emmert-Streib, F., Yang, Z., Feng, H., Tripathi, S., & Dehmer, M. (2020). An Introductory Review of Deep Learning for Prediction Models With Big Data. Frontiers in Artificial Intelligence, 3. https://doi.org/10.3389/frai.2020.00004
Fan, J., Ma, C., & Zhong, Y. (2021). A Selective Overview of Deep Learning. Statistical Science, 36(2). https://doi.org/10.1214/20-sts783
Li, B., Ai, D., & Liu, X. (2022). CNN-XG: A Hybrid Framework for sgRNA On-Target Prediction. Biomolecules, 12(3), 409. https://doi.org/10.3390/biom12030409
Mengstie, M. A., & Wondimu, B. Z. (2021). Mechanism and Applications of CRISPR/Cas-9-Mediated Genome Editing. Biologics: Targets and Therapy, Volume 15, 353–361. https://doi.org/10.2147/btt.s326422
Nayarisseri, Anuraj. (2019). Machine Learning, Deep Learning and Artificial Intelligence
approach for predicting CRISPR for the Cancer treatment. 10.3390/mol2net-05-06258.
Vargas, Rocio & Mosavi, Amir & Ruiz, Ramon. (2017). DEEP LEARNING: A REVIEW.
Advances in Intelligent Systems and Computing. 5.
Zhang, G., Zeng, T., Dai, Z., & Dai, X. (2021). Prediction of CRISPR/Cas9 single guide RNA cleavage efficiency and specificity by attention-based convolutional neural networks. Computational and Structural Biotechnology Journal, 19, 1445–1457. https://doi.org/10.1016/j.csbj.2021.03.001
Zhang, Y., Long, Y., Yin, R., & Kwoh, C. K. (2020). DL-CRISPR: A Deep Learning Method for Off-Target Activity Prediction in CRISPR/Cas9 With Data Augmentation. IEEE Access, 8, 76610–76617. https://doi.org/10.1109/access.2020.2989454
Published
How to Cite
Issue
Section
Copyright (c) 2022 Krish Kawle; Jothsna Kethar, Rajagopal Appavu
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Copyright holder(s) granted JSR a perpetual, non-exclusive license to distriute & display this article.
