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

生物学研究管道中的许多步骤都涉及到机器学习模型的使用，这些模型已经成为解决许多基本问题的标准工具。对于各种生物学问题，对基本机器学习模型(机器学习模型的“集成”)的详细阐述可以提供与标准用法相比的准确性改进。然而，这些合奏的设计是相当特别的，而且它们的使用可能需要大量的计算，这降低了它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促进这项技术，以建立机器学习模型的集成，目标是提高精度。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、与代表不足的群体的互动以及开放源码软件。轮廓隐马尔可夫模型是生物信息学中广泛使用的概率图形模型。过去十年的研究表明，对于生物信息学中的许多应用，包括系统发育定位、多序列比对和元基因组读数的分类鉴定，轮廓HMM的集合(e-HMM)可以提供比单个轮廓HMM更高的准确性。该项目将通过开发建立e-HMM的严格统计技术来推动e-HMM的使用，目的是提高e-HMM的准确性和加深对e-HMM的理解，并将开发使用E-HMM进行蛋白质结构和功能预测的方法。更广泛的影响包括软件学校、与代表不足的群体的接触以及开放源码软件。项目软件和论文可在http://tandy.cs.illinois.edu/eHMMproject.html.This上获得，该奖项反映了国家科学基金会的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.