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Machine learning and data mining in complex genomic data—a review on the lessons learned in Genetic Analysis Workshop 19

机译：复杂基因组数据中的机器学习与数据挖掘 - 遗传分析研讨会中学课程的综述19

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In the analysis of current genomic data, application of machine learning and data mining techniques has become more attractive given the rising complexity of the projects. As part of the Genetic Analysis Workshop 19, approaches from this domain were explored, mostly motivated from two starting points. First, assuming an underlying structure in the genomic data, data mining might identify this and thus improve downstream association analyses. Second, computational methods for machine learning need to be developed further to efficiently deal with the current wealth of data. In the course of discussing results and experiences from the machine learning and data mining approaches, six common messages were extracted. These depict the current state of these approaches in the application to complex genomic data. Although some challenges remain for future studies, important forward steps were taken in the integration of different data types and the evaluation of the evidence. Mining the data for underlying genetic or phenotypic structure and using this information in subsequent analyses proved to be extremely helpful and is likely to become of even greater use with more complex data sets.
机译：在对当前基因组数据的分析中，考虑到项目的复杂性上升，机器学习和数据挖掘技术的应用变得更加吸引人。作为遗传分析研讨会19的一部分，探索了该领域的方法，主要是两个起点。首先，假设基因组数据中的底层结构，数据挖掘可能会识别这一点，从而改善下游关联分析。其次，需要进一步开发机器学习的计算方法，以便有效地处理当前丰富的数据。在讨论机器学习和数据挖掘方法的结果和经验过程中，提取了六个常见消息。这些描绘了应用于复杂基因组数据的这些方法的当前状态。虽然未来的研究仍然存在一些挑战，但在不同数据类型的整合和证据的评估中纳入了重要的前瞻性步骤。在后续分析中挖掘潜在的遗传或表型结构的数据并在随后的分析中使用这些信息被证明是非常有帮助的，并且可能会与更复杂的数据集一起使用。

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