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首页> 外文期刊>Proceedings of the IEEE >Machine Learning in Genomic Medicine: A Review of Computational Problems and Data Sets
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Machine Learning in Genomic Medicine: A Review of Computational Problems and Data Sets

机译：基因组医学中的机器学习：计算问题和数据集的回顾

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摘要

In this paper, we provide an introduction to machine learning tasks that address important problems in genomic medicine. One of the goals of genomic medicine is to determine how variations in the DNA of individuals can affect the risk of different diseases, and to find causal explanations so that targeted therapies can be designed. Here we focus on how machine learning can help to model the relationship between DNA and the quantities of key molecules in the cell, with the premise that these quantities, which we refer to as cell variables, may be associated with disease risks. Modern biology allows high-throughput measurement of many such cell variables, including gene expression, splicing, and proteins binding to nucleic acids, which can all be treated as training targets for predictive models. With the growing availability of large-scale data sets and advanced computational techniques such as deep learning, researchers can help to usher in a new era of effective genomic medicine.
机译：在本文中，我们将介绍机器学习任务，以解决基因组医学中的重要问题。基因组医学的目标之一是确定个体DNA的变异如何影响不同疾病的风险，并找到因果关系的解释，以便设计靶向治疗。在这里，我们着重于机器学习如何帮助建立DNA与细胞中关键分子数量之间关系的模型，前提是这些数量（我们称为细胞变量）可能与疾病风险相关。现代生物学可以对许多此类细胞变量进行高通量测量，包括基因表达，剪接和与核酸结合的蛋白质，这些均可被视为预测模型的训练目标。随着大规模数据集和深度学习等先进计算技术的日益普及，研究人员可以帮助开启有效基因组医学的新时代。

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