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

基本信息

批准号：
1939505
负责人：
Ali Maga
金额：
$ 59.22万
依托单位：
Seattle Children's Hospital
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-10-01 至 2022-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1939505&HistoricalAwards=false
关键词：
Collaborative Research Biology guided neural

项目摘要

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，在数据库中没有可见的生物特征，例如它们的可见特征。缺乏机器可读性数据已经放缓了生物学四个挑战问题的进步：预测产生性状的基因，了解进化的模式，预测生态变化的影响以及物种鉴定。该项目将利用机器学习和机器可读生物学知识的进步来创建一种新方法，以自动从生物图像中识别特征。有机体的图像广泛可用，这种新方法可用于快速收获特征，这些特征可用于解决生物学的巨大挑战。由于这些生物可用的广泛资源，因此将使用大型图像收集和来自鱼类的相应数字数据。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，并且将有助于解释人工智能的结果，从而促进计算机科学领域。新方法将使社会在应用领域（例如农业或医学领域的应用中）受益，而从图像中发现特征对于疾病诊断至关重要。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，演示文稿，出版物以及对其产生的数据和代码的开放访问来传播其发现。该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，这些神经网络可以利用机器可读性和有关生物学的预测性知识，这种知识以系统发育和解剖学本体的形式获得。这些生物学指导的神经网络有望自动检测并预测标本图像的特征，而训练数据很少。基于图像的特征数据从这项工作中得出，将使基因 - 表型映射到新的特征和理解进化模式的进展。所得的机器学习模型可以推广到具有正式结构知识的其他学科，并通过超越黑盒学习并为可解释的人工智能做出重要进展，从而为计算机科学的进步做出贡献。它可以扩展到应用领域，例如农业或生物医学领域。由于许多高质量的数据资源（数字图像，进化树，解剖本体论），该研究将使用硬鱼类进行试验。自动元数据质量评估和出处跟踪的方法将在本项目的过程中开发，以确保结果和过程可验证，可复制和可重复使用。这些将广泛影响许多将采用机器学习作为从图像发现的方式的领域。这项收敛研究将通过与生物学知识结合利用数据革命来加速整个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）的大思想活动的一部分，并由HDR和HDR共同支持，由HDR和在NSF的统计局局局局长统计的统计局局局局长统治统计局局长。认为值得通过基金会的智力优点和更广泛影响的评论标准来评估值得支持。