Collaborative Research: IIBR Informatics: Keeping up with the genomes - Continual Learning of Metagenomic Data

微生物群落是地球上随处可见的微生物群落，对帮助消化肠道中的食物非常重要。在肠道中，它们可以产生维生素(好的)或毒素(坏的)，所以我们需要了解这些微生物群落中存在哪些有机体和基因。该项目使用人工智能(AI)来识别生活在微生物群中的生物及其基因。这项工作的现有工作由于数据量的快速增长而受到阻碍，随着新数据的出现，这些数据量往往需要反复重新分析。这样的过程不仅效率低下，而且越来越不可持续，即使对于我们日益增长的计算资源来说也是如此。这种方法是独一无二的，因为它使用的计算能力更少。与不断重新输入大量数据不同，拟议的最先进系统具有召回和重复使用先前信息的能力，而不需要重新输入或重新分析先前的数据，从而节省大量计算时间，最终节省资金。我们的目标是找到在不牺牲准确性的情况下实现最佳成本节约的人工智能方法。在微生物组实验中也发现了许多未识别的生物，它们被丢弃，从未在其他实验中用于识别相同的生物。一种基于人工智能的方法将保留、记忆和重复使用他们的信息，以防这些新生物稍后在其他实验中再次出现，并最终帮助识别它们。如果生物体在未来被识别，该方法可以有效和高效地自动更新旧数据和知识库。该项目将开发一个动态、可扩展和半监督的学习框架，使用大量未标记的实验数据不断更新分类模型。除了创建可以利用参考和实验数据的更丰富的模型外，主要的创新是该模型将识别未知的生物和蛋白质，并将它们集成到参考数据库中，用于未来的模型更新。这一框架将在每年提交给微生物组计算网站MG-RAST的数百项元基因组研究(可能由数千个样本组成)上得到验证。MG-RAST被科学家用来上传他们的微生物群，用于研究和改进农业、诊断、医药、制造生物燃料以及微生物对其有深刻影响的各种其他应用。这项工作将有助于大学生在人工智能方面的培训及其在微生物组中的应用。结果将通过暑期工作坊与其他教育工作者和研究人员广泛分享。这一奖项反映了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。

Microbiomes are communities of microscopic organisms that are found everywhere on earth and are important in help to digest food in the gut. In the intestines, they can produce vitamins (good) or toxins (bad), so we need to understand what organisms and genes are present in these microscopic communities. This project uses artificial intelligence (AI) to identify organisms and their genes that live in microbiomes. Existing works for this effort have been hampered due to very rapidly growing amount of data, which often need to be repeatedly re-analyzed as new data become available. Such a process is not only inefficient, but is increasingly unsustainable, even for our growing computational resources. This approach is unique because it uses less computing power. Instead of continuously reentering massive amounts of data, the proposed state-of-the-art system has the ability to recall and reuse prior information without requiring reentering or re-analyzing prior data,saving substantial computing time and ultimately money. The goal is to find AI methods that achieve the best cost savings while not sacrificing accuracy. Many unidentified organisms are also found in microbiome experiments and are discarded and never used to identify the same organisms in other experiments. An AI based approach will keep, remember, and reuse their information in case those new organisms show up in again later in other experiments and eventually help in their identification. If the organism is identified in the future, the method can automatically update old data and the knowledgebase effectively and efficiently.This project will develop a dynamic, scalable, and semi-supervised learning framework that continually updates a classification model, with large unlabeled, experimental data. In addition to creating richer models that can leverage both reference and experimental data, the primary innovation is that the model will identify unknown organisms and proteins and integrate them into reference database for future model updates. This framework will be validated on the hundreds of metagenomic studies (composed of potentially thousands of samples) annually submitted to the microbiome computing website MG-RAST. MG-RAST is used by scientists to upload their microbiomes to study and improve agriculture, diagnoses, medicine, making biofuels, and a variety of other applications on which microorganisms have a deep effect. This work will contribute to college student training on artificial intelligence and its application to the microbiome. Results will be shared broadly with other educators and researchers through summer workshops.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.