Robust and efficient statistical inference methods for genomics

稳健且高效的基因组学统计推断方法

• 批准号: 10581075
• 负责人: Yun S Song
• 金额: $ 4.25万
• 依托单位: UNIVERSITY OF CALIFORNIA BERKELEY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-12-01 至 2024-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10581075
• 关键词: Award; Basic Science; Big Data; Biological; Biological Assay; Biological Models; Biological Process; Biology; Biomedical Research; Catalogs; Communities; Complex; Computing Methodologies; Data; Development; Fruit; Genetic; Genetic Models; Genetic Transcription; Genetic Variation; Genomics; Goals; Human Biology; Methods; Modeling; Modernization; Natural Language Processing; Neural Network Simulation; Population Genetics; Population Heterogeneity; Protein Databases; Research; Research Personnel; Statistical Methods; Techniques; Training; Translations; computerized tools; deep neural network; disorder risk; epigenetic variation; genomic data; human disease; improved; mathematical analysis; mathematical model; novel; parent project; tool

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 language processing (NLP) and massive protein databases to improve biological sequence representations, which can fa- cilitate downstream prediction tasks. (3) Develop novel computational methods for integrative analysis of genomic data. By utilizing modern Graphics Processing Units (GPUs) provided by this supplement award, we will train and publicly release improved NLP-inspired neural network models for biological sequences that will be useful to the genomics community.

项目总结 测序和实验分析方面的技术进步极大地提高了各种病毒的可用性。 各种基因组数据，使我们能够对不同种群中的遗传和表观遗传变异进行分类，以及 以前所未有的细节探索基本的生物过程(例如转录和翻译)。这件事- 发展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 噪声和多方面的，而潜在的生物学是非常复杂的，因此既有理论上的，也有复杂的。 对分析和解释的假设挑战。新的有效和健壮的统计推断工具，以及fi 作为理论分析的数学模型，是亟待发展的大有可为的前景 生物学中的数据时代全面开花结果。父项目(R35-GM134922)的中心目标是开发一个套件 一系列有用的统计和计算工具，将有助于应对这一挑战，方法是在 复杂的模型，并帮助研究人员集成来自不同类型数据的信息，以揭示基本信息 生物过程。特别是，父项目旨在实现以下目标：(1)改进和拓宽 深度学习/神经网络在基因组学中的应用。(2)利用自然语言中的尖端技术 处理(NLP)和海量蛋白质数据库，以改进生物序列表示，这可以促进 制定下游预测任务。(3)发展新的基因组综合分析计算方法 数据。通过利用本补充奖提供的现代图形处理器(GPU)，我们将培训和 公开发布生物序列的改进的NLP启发神经网络模型，该模型将对 基因组学社区。