IIBR Informatics: Advancing Bioinformatics Methods using Ensembles of Profile Hidden Markov Models

IIBR 信息学：使用轮廓隐马尔可夫模型集成推进生物信息学方法

基本信息

批准号：
2006069
负责人：
Tandy Warnow
金额：
$ 50万
依托单位：
University of Illinois at Urbana-Champaign
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-08-15 至 2024-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2006069&HistoricalAwards=false
关键词：
IIBR Informatics Advancing Bioinformatics Methods

项目摘要

Many steps in biological research pipelines involve the use of machine learning models, and these have become standard tools for many basic problems. Elaborations on basic machine learning models ("ensembles" of machine learning models) can provide improvements in accuracy compared to standard usage, for various biological questions. However, the design of these ensembles has been fairly ad hoc, and their use can be computationally intensive, which reduces their appeal in practice. This project will advance this technology by developing statistically rigorous techniques for building ensembles of machine learning models, with the goal of improving accuracy. The project will also develop methods that use these ensembles for new biological problems, including protein structure and function prediction. Broader impacts include software school, engagement with under-represented groups, and open-source software. Profile Hidden Markov Models (i.e., profile HMMs) are probabilistic graphical models that are in wide use in bioinformatics. Research over the last decade has shown that ensembles of profile HMMs (e-HMMs) can provide greater accuracy than a single profile HMM for many applications in bioinformatics, including phylogenetic placement, multiple sequence alignment, and taxonomic identification of metagenomic reads. This project will advance the use of e-HMMs by developing statistically rigorous techniques for building e-HMMs with the goal of improving accuracy and improving understanding of e-HMMs, and will also develop methods that use e-HMMs for protein structure and function prediction. Broader impacts include software schools, engagement with under-represented groups, and open-source software. Project software and papers are available at http://tandy.cs.illinois.edu/eHMMproject.html.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

生物研究管道的许多步骤都涉及机器学习模型的使用，并且这些模型已成为许多基本问题的标准工具。与标准用法相比，针对各种生物学问题，对基本机器学习模型（机器学习模型的“集合”）的详细说明可以提供准确性的改进。但是，这些合奏的设计是相当临时的，它们的使用可以在计算上进行密集，从而降低了他们在实践中的吸引力。该项目将通过开发统计上严格的技术来构建机器学习模型的合奏，以提高准确性来推进这项技术。该项目还将开发使用这些集合来解决新的生物学问题的方法，包括蛋白质结构和功能预测。更广泛的影响包括软件学校，与代表性不足的团体的互动以及开源软件。配置文件隐藏的马尔可夫模型（即配置文件HMM）是在生物信息学中广泛使用的概率图形模型。在过去的十年中，研究表明，对于许多在生物信息学中的应用，包括系统发育放置，多个序列比对和元基因组读数的分类学鉴定，概况HMM（E-HMM）的集合可以提供比单个轮廓HMM更高的准确性。该项目将通过开发用于构建E-HMM的统计严格技术来提高E-HMM的使用，以提高准确性并提高对E-HMM的了解，还将开发使用E-HMM的方法来用于蛋白质结构和功能预测。更广泛的影响包括软件学校，与代表性不足的团体的交往以及开源软件。项目软件和论文可在http://tandy.cs.illinois.edu/ehmmproject.html.html..html..html.ins奖中获得，反映了NSF的法定任务，并且认为值得通过基金会的知识分子优点和更广泛的影响审查标准通过评估来进行评估。