The advent of high-throughput multi-platform genomics technologies providing whole- genome molecular summaries of biological samples has revolutionalized biomedical research. These technologiees yield highly structured big data, whose analysis poses significant quantitative challenges. The field of bioinformatics has emerged to deal with these challenges, and is comprised of many quantitative and biological scientists working together to effectively process these data and extract the treasure trove of information they contain. Statisticians, with their deep understanding of variability and uncertainty quantification, play a key role in these efforts. In this article, we attempt to summarize some of the key contributions of statisticians to bioinformatics, focusing on four areas: (1) experimental design and reproducibility, (2) preprocessing and feature extraction, (3) unified modelling and (4) structure learning and integration. In each of these areas, we highlight some key contributions and try to elucidate the key statistical principles underlying these methods and approaches. Our goals are to demonstrate major ways in which statisticians have contributed to bioinformatics, encourage statisticians to get involved early in methods development as new technologies emerge, and to stimulate future methodological work based on the statistical principles elucidated in this article and utilizing all available information to uncover new biological insights.

中文翻译：

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统计对生物信息学的贡献：设计、建模、结构学习和集成

高通量多平台基因组学技术的出现，提供生物样本的全基因组分子摘要，彻底改变了生物医学研究。这些技术产生高度结构化的大数据，其分析提出了重大的定量挑战。生物信息学领域的出现是为了应对这些挑战，它由许多定量和生物科学家共同努力，有效地处理这些数据并提取其中包含的信息宝库。统计学家对变异性和不确定性量化有着深刻的理解，在这些努力中发挥着关键作用。在本文中，我们试图总结统计学家对生物信息学的一些关键贡献，重点关注四个领域：（1）实验设计和可重复性，（2）预处理和特征提取，（3）统一建模和（4）结构学习和整合。在每个领域，我们强调了一些关键贡献，并试图阐明这些方法和途径背后的关键统计原理。我们的目标是展示统计学家对生物信息学做出贡献的主要方式，鼓励统计学家随着新技术的出现尽早参与方法开发，并根据本文阐明的统计原理并利用所有可用信息来促进未来的方法学工作发现新的生物学见解。