MTM 1: An explainable AI system for microbiome characterization and microbiome-based host-phenotype prediction

MTM 1：一个可解释的人工智能系统，用于微生物组表征和基于微生物组的宿主表型预测

• 批准号: 2025451
• 负责人: Yuzhen Ye
• 金额: $ 50万
• 依托单位: Indiana University
• 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: 2020-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2025451&HistoricalAwards=false
• 关键词: MTM; explainable; AI; system; microbiome

Microbiome research is going through a revolutionary transition from characterizing reference microbiomes associated with different environments/hosts to translational applications, including using microbiome for disease diagnosis, improving the efficacy of cancer treatments, and prevention of diseases. The success of these translational applications relies on the identification of differential microbiome markers (e.g., species, genes and pathways) that can distinguish different groups of microbiome data (e.g., healthy individuals versus patients). It is also important to understand factors influencing the gut microbiome and strategies to manipulate the microbiome to augment therapeutic responses and disease prevention. Existing approaches for microbiome-based human host phenotype prediction typically lack explainability and they treat different diseases individually (even though it has been shown that some diseases share similar microbiome characteristics). This project aims to address these issues and develop an explainable AI system for microbiome-based phenotype predictions. The investigators will use the discoveries from this project in undergraduate and graduate teaching, and for outreach education through summer camps for high school students, providing them the opportunity to experience the entire process from sample collection to building microbiome classifiers. This project is to develop an explainable AI system for microbiome characterization and host phenotype prediction based on microbiome data. The proposed microbiome AI system relies on a network of human associated bacteria, to be inferred by integrating genome-scale metabolic modeling (of metabolic competition and complementarity between bacterial species) and co-occurrence profiling of microbial organisms across a large number of microbiome datasets. The AI system uses a conditional variational autoencoder guided by the inferred bacterial network to model the microbial abundances under various host conditions. The autoencoder is used to achieve efficient representation learning of a set of data in an unsupervised manner, and multitask learning is used to leverage the microbiome datasets associated with different diseases to alleviate the problem caused by limited training samples. Further the AI model will incorporate prediction of auxiliary phenotypes to regularize the representation learning. The AI system once trained can be used for microbiome-based host phenotype prediction, and provide explanations to the prediction through the model’s latent variables. It can also be used for predicting the impact of phenotypic alternation, an important problem to address in microbiome modulation and microbiome engineering.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.

微生物组研究正在经历一场革命性的转变，从表征与不同环境/宿主相关的参考微生物组到翻译应用，包括将微生物组用于疾病诊断、提高癌症治疗的疗效和预防疾病。这些翻译应用的成功依赖于识别能够区分不同组微生物组数据的差异微生物组标记(例如，物种、基因和途径)(例如，健康个体与患者)。了解影响肠道微生物群的因素以及操纵微生物群以增强治疗反应和疾病预防的策略也很重要。现有的基于微生物组的人类宿主表型预测方法通常缺乏可解释性，它们单独治疗不同的疾病(尽管已经表明一些疾病具有相似的微生物组特征)。该项目旨在解决这些问题，并开发一个可解释的人工智能系统，用于基于微生物组的表型预测。研究人员将把这个项目的发现用于本科生和研究生的教学，并通过高中生夏令营进行外展教育，为他们提供机会体验从样本收集到构建微生物组分类器的整个过程。该项目旨在开发一个基于微生物组数据的可解释的人工智能系统，用于微生物组表征和宿主表型预测。拟议的微生物组人工智能系统依赖于人类相关细菌的网络，通过整合基因组规模的代谢建模(细菌物种之间的代谢竞争和互补)和微生物在大量微生物组数据集中的共生概况来推断。人工智能系统使用由推断的细菌网络指导的条件变分自动编码器来模拟不同宿主条件下的微生物丰度。自动编码器用于在无监督的情况下实现一组数据的高效表示学习，多任务学习用于利用与不同疾病相关的微生物组数据集，以缓解训练样本有限的问题。此外，人工智能模型将包括辅助表型的预测，以正规化表征学习。一旦训练好，人工智能系统就可以用于基于微生物组的寄主表型预测，并通过模型的潜在变量为预测提供解释。它还可用于预测表型变化的影响，这是微生物组调制和微生物组工程中需要解决的重要问题。该奖项反映了NSF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。

项目成果

Model-based and phylogenetically adjusted quantification of metabolic interaction between microbial species.

• DOI: 10.1371/journal.pcbi.1007951
• 发表时间: 2020-10
• 期刊: PLoS computational biology
• 影响因子: 4.3
• 作者: Lam TJ;Stamboulian M;Han W;Ye Y
• 通讯作者: Ye Y

Metaproteomics as a tool for studying the protein landscape of human-gut bacterial species.

• DOI: 10.1371/journal.pcbi.1009397
• 发表时间: 2022-03
• 期刊: PLoS computational biology
• 影响因子: 4.3
• 作者: Stamboulian M;Canderan J;Ye Y
• 通讯作者: Ye Y

Using high-abundance proteins as guides for fast and effective peptide/protein identification from human gut metaproteomic data.

• DOI: 10.1186/s40168-021-01035-8
• 发表时间: 2021-04-01
• 期刊: Microbiome
• 影响因子: 15.5
• 作者: Stamboulian M;Li S;Ye Y
• 通讯作者: Ye Y

Locality-Sensitive Hashing-Based k-Mer Clustering for Identification of Differential Microbial Markers Related to Host Phenotype.

• DOI: 10.1089/cmb.2021.0640
• 发表时间: 2022-07
• 期刊: Journal of computational biology : a journal of computational molecular cell biology