Abstract
A genome is a complete DNA segment that contains the genetic information of how the organism cell lives and performs its functions, such as producing proteins. The field of biology that studies the complete information about the genome is known as genomics. It introduces concepts and techniques about the sequence, functions, evolution, and mapping of the genome. Many genomics applications in broad areas improve creatures’ lives via precise diagnosis and effective treatment. Analyzing the vast amount of genetic information generated from medical technologies and devices is a complicated process. Therefore, implementing deep learning and machine learning with other methods such as feature selection and clustering reduces cost and time spent and enhances prediction accuracy. Scientists can discover hidden patterns in genetic information by utilizing machine learning models, finding mutations in gene expression, and selecting precise locations in gene editing. This study reviewed recent studies that applied machine learning approaches in genomics and focused explicitly on gene editing. One of the hot topics for gene editing research is off-target mutation prediction using Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) and CRISPR-associated protein 9 (CRISPRCas9). Prediction techniques in gene editing have been developed to determine values based on mismatches in the CRISPR-Cas9 guide series. This paper summarizes recent studies that have employed machine learning and deep learning methods in different genomics applications and presents a comparative analysis of the discussed studies.
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Almutiri, T., Saeed, F., Alassaf, M. (2022). A Survey of Machine Learning and Deep Learning Applications in Genome Editing. In: Saeed, F., Al-Hadhrami, T., Mohammed, E., Al-Sarem, M. (eds) Advances on Smart and Soft Computing. Advances in Intelligent Systems and Computing, vol 1399. Springer, Singapore. https://doi.org/10.1007/978-981-16-5559-3_13
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DOI: https://doi.org/10.1007/978-981-16-5559-3_13
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