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Biological data studies, scale-up the potential with machine learning

European Journal of Human Genetics volume 31, pages 619–620 (2023)Cite this article

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Machine learning has emerged as one of the impactful artificial intelligence (AI)-based tools of computational sciences applications. In the recent past, this approach was consistently advancing and broadened the horizon into many interdisciplinary fields, including the biological sciences and medicine. To exhibit the research impact, scientific collaborations among various disciplines have increased with computer sciences, and this trend has also been noticed dramatically in sub-branches of biological sciences including genomics and genetics. Even though, associations with machine learning in biology field are exploring through various dimensions, however, the potential and demand of machine learning have yet to be extensively explored. Surprisingly, this AI-based approach was often overlooked in the field of biological sciences, assuming that, first, most researchers need to be made aware or relatively uninterested in applying rather different approach than traditional in their primary field to address the research questions, perhaps for various reasons. Second, it might be possible that, previously the direct use of machine learning was limited in their research of interest areas compared to others. Whatever the possible reasons, it is interesting to see how biological researchers consider using this method in the near future. As both field, biology and computer science have advanced, the need and/or desire to use machine learning models in unraveling complex biological data interpretations has recently increased, however, it is still limited, and therefore more researchers from both sides are getting interested and involved lately [1].

From the biological perspective, this system is rather complex and dynamic, and has various dimensions to understand, and is somewhat more challenging than it appears. Obliviously, there is none of single approach that can fully discover the complexity of biological phenomena. Therefore, it is requisite that, in combination with other techniques, machine learning methods may be adopted to extract significant relevant information from the biological system. It is somehow misunderstood and often comes into attention that, machine learning methodology seems to overlap with statistics, however, they both have their domains to process the data. Machine learning can be a significantly helpful tool for understanding the behavior of complex data studies genetics and genomic sciences, and interestingly results can be improved over time once the application algorithms have gained experience with sufficient data inputs. The hardcore technical background of machine learning and statistical methods can be reviewed from other sources available [2, 3]. In this opinion-based piece, I discuss about the latest observations, behavior and impact of machine learning in the field of biological sciences, after that, further narrowing down my extended thoughts to the genetics field. I was emphasized to give umbrella overview of recent updates and potential of machine learning applications to scale-up the potential in biological data studies.

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Acknowledgements

Author acknowledges Prof. Hiroaki Kawashima, University of Hyogo, Kobe, Japan for his valuable and constructive feedback on the manuscript.

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  1. Independent Investigator, Kobe, Hyogo, Japan

    Raj Rajeshwar Malinda

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RRM- conceptualized, drafted, edited and finalized this article.

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Correspondence to Raj Rajeshwar Malinda.

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Malinda, R.R. Biological data studies, scale-up the potential with machine learning. Eur J Hum Genet 31, 619–620 (2023). https://doi.org/10.1038/s41431-023-01361-5

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