Robust and efficient statistical inference methods for genomics

项目摘要 测序和实验分析的技术进步大大增加了各种瓦里的可用性， 各种各样的基因组数据，使我们能够在不同的人群中对遗传和表观遗传变异进行编目， 为了探测基本的生物过程（例如，翻译和翻译，以前所未有的细节。这个德- 生物医学为基础和生物医学研究提供了许多新的机会，但数据往往是 嘈杂和多方面的，而基础生物学是非常复杂的，从而提出了理论和COM， 分析和解释的假设挑战。新的高效和强大的统计推断工具，以及 作为理论分析的数学模型，是非常需要发展带来的大的希望， 让生物学的数据时代开花结果。母项目（R35-GM 134922）的中心目标是开发一个套件 有用的统计和计算工具，这将有助于应对这一挑战，使推理下 复杂的模型，并帮助研究人员整合来自不同类型数据的信息，以揭示基本的 生物过程。具体而言，母项目旨在实现以下目标：（1）完善和拓宽 深度学习/神经网络在基因组学中的应用。(2)利用自然景观中的尖端技术- 语言处理（NLP）和大量蛋白质数据库，以改善生物序列表示， 可以促进下游预测任务。(3)开发新的计算方法，用于综合分析 基因组数据拟议的多元化补充将培训和指导代表性不足的少数民族学生 透过有助达致上述母补助金特定目的的研究计划，资助学生。

PROJECT SUMMARY Technological advances in sequencing and experimental assays have greatly increased the availability of vari- ous kinds of genomic data, enabling us to catalog genetic and epigenetic variation in diverse populations, and to probe fundamental biological processes (e.g., transcription and translation) in unprecedented detail. This de- velopment is providing a number of new opportunities for basic and biomedical research, but often the data are noisy and multifaceted, while the underlying biology is very complex, thus presenting both theoretical and com- putational challenges for analysis and interpretation. New efficient and robust statistical inference tools, as well as theoretical analysis of mathematical models, are much in need of development to bring the promise of the big data era in biology to full fruition. The central goal of the parent project (R35-GM134922) is to develop a suite of useful statistical and computational tools that will help to tackle this challenge, by enabling inference under complex models and helping researchers integrate information from different types of data to reveal fundamental biological processes. In particular, the parent project aims to achieve the following goals: (1) Improve and widen deep learning/neural network applications in genomics. (2) Leverage cutting-edge techniques in natural lan- guage processing (NLP) and massive protein databases to improve biological sequence representations, which can facilitate downstream prediction tasks. (3) Develop novel computational methods for integrative analysis of genomic data. The proposed diversity supplement will train and mentor an underrepresented minority student through research projects that will help to achieve the above specific objectives of the parent grant.