Machine learning applications for therapeutic tasks with genomics data.
Machine learning applications for therapeutic tasks with genomics data.
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DOI：
10.1016/j.patter.2021.100328
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发表时间：
2021-10-08
期刊：
影响因子：
--
通讯作者：
Sun J
中科院分区：
文献类型：
--
作者：
Huang K;Xiao C;Glass LM;Critchlow CW;Gibson G;Sun J
由于基因组学和其他生物医学数据的日益可获得性，许多机器学习算法已经被提出用于广泛的治疗发现和开发任务。在这次调查中，我们通过治疗发展的视角回顾了机器学习在基因组学中的应用的文献。我们调查基因组学、化合物、蛋白质、电子健康记录、细胞图像和临床文本之间的相互作用。我们在基因组学应用中确定了22种机器学习，这些学习跨越了整个治疗流水线，从发现新靶点、个性化药物、开发基因编辑工具，一直到促进临床试验和上市后研究。我们还指出了这一领域的七个具有扩展和影响潜力的关键挑战。这项调查考察了最近在机器学习、基因组学和治疗开发的交叉路口的研究。基因组包含构建生物体功能和结构的指令。最近的高通量技术使产生大量基因组数据成为可能。然而，在将基因组数据转化为有形疗法的道路上有许多障碍。我们观察到，仅有基因组学数据不足以进行治疗开发。我们需要研究基因组数据如何与其他类型的数据相互作用，如化合物、蛋白质、电子健康记录、图像和文本。机器学习技术可以用来识别模式，并从这些复杂的数据中提取见解。在这篇综述中，我们综述了机器学习在基因组学中的广泛应用，这些应用可以使治疗开发更快、更有效。挑战依然存在，包括技术问题，如在资源有限的情况下在不同环境下学习，以及实际问题，如对模型的不信任、隐私和公平。最近的高通量技术使产生大量基因组数据成为可能。然而，在将基因组数据转化为有形疗法的道路上有许多障碍。我们需要研究基因组数据如何与其他类型的数据相互作用，如化合物、蛋白质、电子健康记录、图像和文本。在这篇综述中，我们综述了机器学习在基因组学中的广泛应用，这些应用可以使治疗开发更快、更有效。
Thanks to the increasing availability of genomics and other biomedical data, many machine learning algorithms have been proposed for a wide range of therapeutic discovery and development tasks. In this survey, we review the literature on machine learning applications for genomics through the lens of therapeutic development. We investigate the interplay among genomics, compounds, proteins, electronic health records, cellular images, and clinical texts. We identify 22 machine learning in genomics applications that span the whole therapeutics pipeline, from discovering novel targets, personalizing medicine, developing gene-editing tools, all the way to facilitating clinical trials and post-market studies. We also pinpoint seven key challenges in this field with potentials for expansion and impact. This survey examines recent research at the intersection of machine learning, genomics, and therapeutic development. The genome contains instructions for building the function and structure of organisms. Recent high-throughput techniques have made it possible to generate massive amounts of genomics data. However, there are numerous roadblocks on the way to turning genomic data into tangible therapeutics. We observe that genomics data alone are insufficient for therapeutic development. We need to investigate how genomics data interact with other types of data such as compounds, proteins, electronic health records, images, and texts. Machine learning techniques can be used to identify patterns and extract insights from these complex data. In this review, we survey a wide range of genomics applications of machine learning that can enable faster and more efficacious therapeutic development. Challenges remain, including technical problems such as learning under different contexts given low-resource constraints, and practical issues such as mistrust of models, privacy, and fairness. Recent high-throughput techniques have made it possible to generate massive amounts of genomics data. However, there are numerous roadblocks on the way to turning genomic data into tangible therapeutics. We need to investigate how genomics data interact with other types of data such as compounds, proteins, electronic health records, images, and texts. In this review, we survey a wide range of genomics applications of machine learning that can enable faster and more efficacious therapeutic development.
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影响因子：
5.9
作者：
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