Computational Molecular Biology 2024, Vol.14, No.3, 106-114 http://bioscipublisher.com/index.php/cmb 106 Research Insight Open Access AI in Biology: Transforming Genomic Research with Machine Learning Qiang Zhang, Yu Wang Biotechnology Research Center, Cuixi Academy of Biotechnology, Zhuji, 311800, Zhejiang, China Corresponding author: yu.wang@cuixi.org Computational Molecular Biology, 2024, Vol.14, No.3 doi: 10.5376/cmb.2024.14.0013 Received: 08 Apr., 2024 Accepted: 23 May, 2024 Published: 10 Jun., 2024 Copyright © 2024 Zhang and Wang, This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Preferred citation for this article: Zhang Q., and Wang Y., 2024, AI in biology: transforming genomic research with machine learning, Computational Molecular Biology, 14(3): 106-114 (doi: 10.5376/cmb.2024.14.0013) Abstract With the rapid development of artificial intelligence (AI) and machine learning (ML) technologies, the field of biology, particularly genomic research, is undergoing profound transformations. This study explores how AI and ML are redefining genomic data analysis and functional genomics research, while emphasizing the critical role these technologies play in enhancing research efficiency, improving accuracy, and advancing personalized medicine. The application of AI in biology has expanded from basic data processing to complex tasks such as gene function prediction, identification of regulatory elements, and understanding epigenetic modifications. Through an in-depth analysis of key machine learning techniques, including supervised learning, unsupervised learning, and deep learning, this study demonstrates how these methods are revolutionizing traditional genomic data analysis workflows, significantly improving the efficiency of sequence alignment, variant calling, and gene expression profiling. Additionally, it discusses the future prospects of AI-driven genomic tools, cloud computing, big data integration, and open-source platform collaboration, aiming to provide valuable insights for future research and technological development. Keywords Artificial intelligence (AI); Machine learning (ML); Genomic research; Functional genomics; Personalized medicine 1 Introduction Artificial Intelligence (AI) and Machine Learning (ML) have revolutionized numerous fields, and biology is no exception. The advent of high-throughput technologies has led to an explosion of biological data, necessitating advanced computational methods to analyze and interpret these vast datasets. Machine learning, which involves developing algorithms that improve through experience, has shown immense potential in handling complex biological data. Techniques such as supervised, semi-supervised, and unsupervised learning, as well as deep learning, are being increasingly applied to genomic data to uncover hidden patterns and make accurate predictions (Angermueller et al., 2016). These methods have been particularly effective in tasks such as annotating sequence elements, predicting gene expression levels, and identifying genomic elements like promoters and enhancers (Wu and Zhao, 2019; Liu et al., 2020). Genomic research is pivotal in understanding the fundamental mechanisms of life and disease. By studying the genome, researchers can identify genetic variations that contribute to diseases, understand gene function, and develop targeted therapies. The ability to analyze large-scale genomic data has opened new avenues in precision medicine, where treatments can be tailored to an individual's genetic makeup (Koumakis et al., 2020). The integration of machine learning in genomic research has further accelerated discoveries, enabling the modeling of complex biological networks and the prediction of disease risks based on genetic information (Leung et al., 2016; Camacho et al., 2018). This study will provide a comprehensive overview of the current applications of artificial intelligence and machine learning in genomics research, explore various machine learning techniques and their practical applications in genomics, discuss the challenges and limitations of these methods, and emphasize the future development directions in this field. I hope to clarify the transformative impact of machine learning on genomic research and its potential to further advance biology and medicine. 2 Overview of Machine Learning Techniques in Genomics 2.1 Supervised learning Supervised learning involves training a model on a labeled dataset, where the input data is paired with the correct
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