arXiv 2005.025212020-05-05q-bio.GNcs.LGq-bio.QM

基因组和临床数据的集成理论和数据驱动建模管道

A Pipeline for Integrated Theory and Data-Driven Modeling of Genomic and Clinical Data

University of Pittsburgh（匹兹堡大学）

Vineet K Raghu, Xiaoyu Ge, Arun Balajee, Daniel J. Shirer, Isha Das, Panayiotis V. Benos, Panos K. Chrysanthis

备注：: 16 pages, 8 figures, Presented at the ACM SIGKDD Workshop on Data Mining in Bioinformatics (BioKDD, 2019)

摘要：

高通量基因组测序技术，如RNA-Seq和微阵列，通过在颗粒水平上实现基因组的高通量测量，有可能改变临床决策和生物医学研究。然而，为了真正了解疾病的原因和医疗干预的效果，这些数据必须与来自个体的表型，环境和行为数据相结合。此外，有效的知识发现方法，可以推断这些数据类型之间的关系是必需的。在这项工作中，我们提出了一个从综合基因组和临床数据中发现知识的管道。该管道从一种新颖的变量选择方法开始，并使用概率图模型来理解数据中特征之间的关系。我们展示了这个管道如何改善乳腺癌结果预测模型，并可以提供一个生物学上可解释的测序数据。

英文摘要：

High throughput genome sequencing technologies such as RNA-Seq and Microarray have the potential to transform clinical decision making and biomedical research by enabling high-throughput measurements of the genome at a granular level. However, to truly understand causes of disease and the effects of medical interventions, this data must be integrated with phenotypic, environmental, and behavioral data from individuals. Further, effective knowledge discovery methods that can infer relationships between these data types are required. In this work, we propose a pipeline for knowledge discovery from integrated genomic and clinical data. The pipeline begins with a novel variable selection method, and uses a probabilistic graphical model to understand the relationships between features in the data. We demonstrate how this pipeline can improve breast cancer outcome prediction models, and can provide a biologically interpretable view of sequencing data.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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