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

与遗传数据不同，生物的特征，如它们的可见特征，不能在数据库中进行分析。缺乏机器可读的特征数据减缓了生物学中四个重大挑战问题的进展：预测产生特征的基因，理解进化模式，预测生态变化的影响，以及物种鉴定。这个项目将利用机器学习的进步和机器可读的生物学知识来创造一种新的方法，从生物体的图像中自动识别特征。生物体的图像随处可见，这种新方法可以用来快速获取特征，这些特征可以用来解决生物学中的重大挑战。这项研究将使用来自鱼类的大量图像收集和相应的数字数据，因为这些生物拥有广泛的资源。新的机器学习模型可以推广到其他具有类似机器可读知识的学科，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。这种新方法在农业或医学等领域的应用将使社会受益，在这些领域，从图像中发现特征在疾病诊断中至关重要。该项目将支持生物学、计算机科学和信息科学方面的学生和博士后的教育。它将通过研讨会、演示文稿、出版物以及开放获取其编制的数据和代码来传播其调查结果。这个项目将利用最先进的机器学习的进步来开发一种新型的人工神经网络，它可以利用以系统发育和解剖本体的形式提供的关于生物学的机器可读和预测性知识。这些生物引导的神经网络有望在几乎没有训练数据的情况下，从样本图像中自动检测和预测特征。从这项工作中获得的基于图像的性状数据将使基因-表型映射到新的性状和理解进化模式方面的进展成为可能。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，在可解释的人工智能方面取得重要进展，为计算机科学的进步做出贡献。它可以扩展到应用领域，如农业或生物医学领域。这项研究将使用硬骨鱼进行试点，因为它拥有许多高质量的数据资源(数字图像、进化树、解剖本体)。在该项目过程中，将制定自动元数据质量评估和来源追踪的方法，以确保结果和进程可核查、可复制和可重复使用。这些将广泛影响许多领域，这些领域将采用机器学习作为从图像中进行发现的一种方式。这一融合研究将通过结合生物知识利用数据革命来加速生物科学和计算机科学的科学发现。该项目是国家科学基金会利用数据革命(HDR)大创意活动的一部分，由HDR和NSF生物科学局内的生物基础设施部联合支持。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。

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.