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Biology-aware machine learning methods for characterizing microbiome genotype and phenotype

用于表征微生物组基因型和表型的生物学感知机器学习方法

基本信息

批准号：
10810437
负责人：
Siavash Mir arabbaygi
金额：
$ 1.48万
依托单位：
UNIVERSITY OF CALIFORNIA, SAN DIEGO
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-15 至 2026-08-31
项目状态：
未结题

项目摘要

PROJECT SUMMARY The Mirarab laboratory designs computational methods for answering biological and biomedical questions, fo- cusing on scalability and accuracy. These methods span several areas (e.g., microbiome proﬁling, multiple sequence alignment, and phylogenomics), and a common thread among them is evolutionary modeling. More recently, many of the developed methods are based on machine learning. The lab has developed scalable and accurate methods for reconstructing evolutionary histories (i.e., phylogenies) and using these histories in down- stream biomedical applications. Methods developed by this lab (e.g., ASTRAL, SEPP, DEPP) are at the fore- fronts of modern genome-wide phylogenetics. While the lab has previously focused more on inferring species histories, through an MIRA grant, it has shifted its focus to developing methods for microbiome analyses, which pose their a unique set of challenges. As part of the MIRA application, the Mirarab lab will focus on designing, testing, and applying improved methods for statistical analyses of microbiome data. These methods will target two questions. (i) Proﬁling: What organisms constitute a given sample? (ii) Association: How are samples different in their organismal composition, and how do these differences connect to measurable characteristics of their environment? While both questions have been subject to considerable research, many computational challenges remain, providing an opportunity for better methods to make a signiﬁcant impact. Instead of focusing solely on new algorithms, the lab will also work on building better reference datasets and combining data from multiple sources. Thus, the project aims to harness the unprecedented computational power, large available datasets, and recent advances in machine learning to improve state-of-the-art dramatically. The project will not use off-the-shelf machine learning methods in a black-box fashion. Instead, it develops methods that incorporate biological knowledge (e.g., of the evolutionary relationships) into machine learning methods in a principled biologically-motivated fashion. Within the context of the MIRA award, this supplementary request is to request support for an undergradu- ate student who is considering pursuing biomedical research career by providing research experiences in the intersection of mathematics/algorithmics and biology.

项目总结 Mirarab实验室设计了用于回答生物和生物医学问题的计算方法。注重可伸缩性和准确性。这些方法跨越几个领域(例如，微生物群PROﬁLing、多个序列比对和系统基因组学)，其中一个共同的线索是进化建模。更多近年来，发展起来的许多方法都是基于机器学习的。该实验室已经开发出可扩展和重建进化历史(即，系统发育)并使用这些历史的准确方法-- 流生物医学应用。本实验室开发的方法(如Astral、Sepp、Depp)处于领先地位- 现代全基因组系统发育学的前沿。虽然该实验室之前更多地专注于推断物种历史上，通过米拉赠款，它将重点转移到开发微生物组分析方法上，这带来了一系列独特的挑战。作为Mira应用程序的一部分，Mirarab实验室将专注于设计、测试和应用改进的微生物组数据的统计分析方法。这些方法将针对两个问题。(I)ﬁLing代表：给定的样本由哪些生物体组成？(2)协会：样本的生物体有何不同构成，以及这些差异如何与其环境的可测量特征相联系？而当这两个问题都经过了大量的研究，许多计算挑战仍然存在，提供了用更好的方法来产生重大影响的机会是不可能的。与其只专注于新的算法，该实验室还将致力于建立更好的参考数据集，并将来自多个来源的数据组合在一起。因此，该项目旨在利用前所未有的计算能力、大量可用的数据集和最新进展在机器学习方面大大提高了最先进的水平。该项目将不使用现成的机器以黑匣子方式学习方法。相反，它开发了结合生物学知识的方法 (例如，进化关系)转化为机器学习方法中的原则性生物动机时尚。在米拉奖的范围内，这项补充请求是为了请求对以下方面的支助：正在考虑通过提供研究经验从事生物医学研究的ATE学生数学/算法和生物学的交叉学科。