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Machine learning in postgenomic biology and personalized medicine
WIREs Data Mining and Knowledge Discovery ( IF 7.8 ) Pub Date : 2022-01-24 , DOI: 10.1002/widm.1451
Animesh Ray 1, 2
Affiliation  

In recent years, machine learning (ML) has been revolutionizing biology, biomedical sciences, and gene-based agricultural technology capabilities. Massive data generated in biological sciences by rapid and deep gene sequencing and protein or other molecular structure determination, on the one hand, require data analysis capabilities using ML that are distinctly different from classical statistical methods; on the other, these large datasets are enabling the adoption of novel data-intensive ML algorithms for the solution of biological problems that until recently had relied on mechanistic model-based approaches that are computationally expensive. This review provides a bird's eye view of the applications of ML in postgenomic biology. Attempt is also made to indicate as far as possible the areas of research that are poised to make further impacts in these areas, including the importance of explainable artificial intelligence in human health. Further contributions of ML are expected to transform medicine, public health, agricultural technology, as well as to provide invaluable gene-based guidance for the management of complex environments in this age of global warming.

中文翻译:

后基因组生物学和个性化医学中的机器学习

近年来,机器学习 (ML) 一直在革新生物学、生物医学科学和基于基因的农业技术能力。一方面,通过快速深度基因测序和蛋白质或其他分子结构测定在生物科学中产生的海量数据需要使用 ML 的数据分析能力,这与经典统计方法截然不同;另一方面,这些大型数据集使得采用新的数据密集型 ML 算法来解决生物学问题成为可能,而直到最近,这些问题仍依赖于计算成本高昂的基于机械模型的方法。这篇综述提供了 ML 在后基因组生物学中的应用的鸟瞰图。还尝试尽可能指出准备在这些领域产生进一步影响的研究领域,包括可解释的人工智能在人类健康中的重要性。ML 的进一步贡献有望改变医学、公共卫生、农业技术,并为这个全球变暖时代的复杂环境管理提供宝贵的基于基因的指导。
更新日期:2022-01-24
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