Collaborative Research: Biology-guided neural networks for discovering phenotypic traits

合作研究：生物学引导的神经网络发现表型特征

• 批准号: 2022042
• 负责人: Paula Mabee
• 金额: $ 42.44万
• 依托单位: Battelle Memorial Institute
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-01-15 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2022042&HistoricalAwards=false
• 关键词: Collaborative; Research; Biology; guided; neural

Unlike genetic data, the traits of organisms such as their visible features, are not available in databases for analysis. The lack of machine-readable trait data has slowed progress on four grand challenge problems in biology: predicting the genes that generate traits, understanding the patterns of evolution, predicting the effects of ecological change, and species identification. This project will use advances in machine learning and machine-readable biological knowledge to create a new method to automatically identify traits from images of organisms. Images of organisms are widely available, and this new method could be used to rapidly harvest traits that could be used to solve the grand challenges in biology. Large image collections and corresponding digital data from fishes will be used in this study because of the extensive resources available for these organisms. The new machine learning model can be generalized to other disciplines that have similar machine-readable knowledge, and it will help in explaining the results of artificial intelligence, thus advancing the field of computer science. The new method stands to benefit society in application to areas such as agriculture or medicine, where trait discovery from images is critical in disease diagnosis. The project will support the education of students and postdocs in biology, computer science, and information science. It will disseminate its findings through workshops, presentations, publications, and open access to data and code that it produces. This project will leverage advances in state-of-the-art machine learning to develop a novel class of artificial neural networks that can exploit the machine readable and predictive knowledge about biology that is available in the form of phylogenies and anatomy ontologies. These biology-guided neural networks are expected to automatically detect and predict traits from specimen images, with little training data. Image-based trait data derived from this work will enable progress in gene-phenotype mapping to novel traits and understanding patterns of evolution. The resulting machine learning model can be generalized to other disciplines that have formally structured knowledge, and will contribute to advances in computer science by going beyond black-box learning and making important advances toward Explainable Artificial Intelligence. It may be extended to applied areas, such as agriculture or the biomedical domain. The research will be piloted using teleost fishes because of many high-quality data resources (digital images, evolutionary trees, anatomy ontology). Methods for automated metadata quality assessment and provenance tracking will be developed in the course of this project to ensure the results and processes are verifiable, replicable and reusable. These will broadly impact the many domains that will adopt machine learning as a way to make discoveries from images. This convergent research will accelerate scientific discovery across the biological sciences and computer science by harnessing the data revolution in conjunction with biological knowledge.This project is part of the National Science Foundation's Harnessing the Data Revolution (HDR) Big Idea activity, and is jointly supported by the HDR and the Division of Biological Infrastructure within the NSF Directorate of Directorate for Biological Sciences.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.

与遗传数据不同，生物体的特征，如其可见特征，无法在数据库中进行分析。 缺乏机器可读的性状数据已经减缓了生物学四大挑战问题的进展：预测产生性状的基因，理解进化模式，预测生态变化的影响，以及物种识别。该项目将利用机器学习和机器可读生物学知识的进步，创建一种新方法，从生物体图像中自动识别特征。 生物体的图像可以广泛获得，这种新方法可以用来快速获取可用于解决生物学重大挑战的特征。 大量的图像收集和相应的数字数据，从鱼类将用于这项研究，因为这些生物的广泛资源。新的机器学习模型可以推广到具有类似机器可读知识的其他学科，它将有助于解释人工智能的结果，从而推动计算机科学领域的发展。 这种新方法在应用于农业或医学等领域时将造福社会，在这些领域，从图像中发现特征对疾病诊断至关重要。 该项目将支持生物学、计算机科学和信息科学的学生和博士后的教育。 它将通过研讨会、演讲、出版物和开放获取其产生的数据和代码来传播其研究结果。该项目将利用最先进的机器学习的进步来开发一类新型的人工神经网络，该网络可以利用关于生物学的机器可读和预测性知识，这些知识以遗传学和解剖学本体的形式提供。 这些生物学引导的神经网络预计将自动检测和预测样本图像的特征，几乎没有训练数据。从这项工作中获得的基于图像的性状数据将使基因-表型映射到新性状和理解进化模式方面取得进展。由此产生的机器学习模型可以推广到其他具有正式结构化知识的学科，并将通过超越黑盒学习并在可解释人工智能方面取得重要进展来促进计算机科学的进步。 它可以扩展到应用领域，如农业或生物医学领域。该研究将使用硬骨鱼进行试点，因为有许多高质量的数据资源（数字图像，进化树，解剖本体）。在本项目过程中，将制定自动化元数据质量评估和来源跟踪方法，以确保结果和流程可核查、可复制和可重复使用。 这些将广泛影响将采用机器学习作为从图像中发现的方法的许多领域。这一融合研究将通过利用数据革命与生物知识的结合，加速生物科学和计算机科学的科学发现。该项目是美国国家科学基金会利用数据革命（HDR）大创意活动的一部分，该奖项由人类发展报告和NSF生物科学理事会生物基础设施司共同支持。该奖项反映了NSF的法定使命，并已被认为是值得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的支持。

项目成果

Biodiversity Image Quality Metadata Augments Convolutional Neural Network Classification of Fish Species

生物多样性图像质量元数据增强了鱼类物种的卷积神经网络分类

• 发表时间: 2021
• 期刊: Metadata and Semantic Research. MTSR 2020. Communications in Computer and Information Science
• 作者: Leipzig, J.;Bakis, Y;Wang, X;Elhamod, M.;Diamond, K.;Dahdul, W;Karpante, A;Maga, M;Mabee, P;Bart, H
• 通讯作者: Bart, H

Discovering Novel Biological Traits From Images Using Phylogeny-Guided Neural Networks

• DOI: 10.1145/3580305.3599808
• 发表时间: 2023-06
• 期刊: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
• 作者: Mohannad Elhamod;Mridul Khurana;Harish Babu Manogaran;J. Uyeda;M. Balk;W. Dahdul;Yasin Bakics;H. Bart;Paula M. Mabee;H. Lapp;J. Balhoff;Caleb Charpentier;David Carlyn;Wei-Lun Chao;Chuck Stewart;D. Rubenstein;T. Berger-Wolf;A. Karpatne