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

微生物组研究正在经历一个革命性的转变，从表征与不同环境/宿主相关的参考微生物组到转化应用，包括利用微生物组进行疾病诊断、提高癌症治疗效果和预防疾病。这些转化应用的成功依赖于识别能够区分不同微生物组数据组（例如，健康个体与患者）的不同微生物组标记（例如，物种、基因和途径）。了解影响肠道微生物组的因素和操纵微生物组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，并且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。研究人员将在本科和研究生教学中使用这个项目的发现，并通过夏令营为高中生提供拓展教育，为他们提供体验从样本收集到构建微生物组分类器的整个过程的机会。本项目旨在开发一个可解释的AI系统，用于微生物组表征和基于微生物组数据的宿主表型预测。提出的微生物组人工智能系统依赖于人类相关细菌网络，通过整合基因组尺度的代谢建模（细菌物种之间的代谢竞争和互补性）和大量微生物组数据集中微生物有机体的共发生分析来推断。人工智能系统使用由推断的细菌网络引导的条件变分自编码器来模拟不同宿主条件下的微生物丰度。使用自编码器以无监督的方式实现对一组数据的高效表示学习，使用多任务学习利用与不同疾病相关的微生物组数据集来缓解训练样本有限带来的问题。此外，人工智能模型将结合辅助表型的预测来规范表征学习。人工智能系统一旦经过训练，就可以用于基于微生物组的宿主表型预测，并通过模型的潜在变量为预测提供解释。它还可以用于预测表型交替的影响，表型交替是微生物组调节和微生物组工程中需要解决的一个重要问题。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.