Machine learning and biology

机器学习和生物学

• 批准号: RGPIN-2019-07308
• 负责人: Morris, Quaid
• 金额: $ 3.06万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=688498
• 关键词: Machine; learning; biology

A key challenge in biomedical research is integrating the massive amount of available omics data to better understand gene function. We developed GeneMANIA, a software tool to integrate multi-omic data and use machine learning to predict gene function based on similarity to genes of known function. A web server (http://genemania.org) enables users to make gene function predictions using 600 million gene-gene functional interactions from diverse omics data sets. GeneMANIA serves 10,000 users per month. We now propose to significantly expand the GeneMANIA algorithm and software to improve its utility for contemporary biomedical research.******Our main activity will be increasing GeneMANIA accuracy and scalability with novel machine learning methods. To enable GeneMANIA to scale to support the large number of functional interaction networks, we will update its algorithm using a novel combination of state-of-the-art machine learning techniques: deep learning and gene embedding. We will evaluate the scalability and accuracy of published and novel methods to identify the best one to improve the GeneMANIA framework.******Dissemination: We will leverage our large user base to facilitate dissemination, via collaboration, websites, publications, social media, conference presentations and training via the popular Canadian Bioinformatics Workshop training program.******Benefits: GeneMANIA benefits tens of thousands of biological researchers by helping them sift through large amounts of omics data to learn more about gene function. Our proposed work will vastly increase the value of the GeneMANIA technology for biomedical research. The ease of use of the GeneMANIA technology alleviates the need for specialized bioinformatics support for biologists and facilitates the use of very large genomics data already collected using massive government investment. This will lead to better access to analysis services, more efficient use of research funding, new scientific discoveries and new medical treatments.**

生物医学研究的一个关键挑战是整合大量可用的组学数据以更好地了解基因功能。我们开发了GeneMANIA，这是一个集成多组学数据的软件工具，并使用机器学习基于与已知功能基因的相似性来预测基因功能。一个web服务器（http://genemania.org）使用户能够使用来自不同组学数据集的6亿个基因-基因功能交互进行基因功能预测。GeneMANIA每月服务1万名用户。我们现在建议显著扩展GeneMANIA算法和软件，以提高其在当代生物医学研究中的效用。******我们的主要活动将是通过新颖的机器学习方法提高GeneMANIA的准确性和可扩展性。为了使GeneMANIA能够扩展以支持大量功能交互网络，我们将使用最先进的机器学习技术的新颖组合来更新其算法：深度学习和基因嵌入。我们将评估已发表的新方法的可扩展性和准确性，以确定改进GeneMANIA框架的最佳方法。******传播：我们将利用我们庞大的用户基础，通过合作、网站、出版物、社交媒体、会议演讲和通过流行的加拿大生物信息学研讨会培训计划的培训来促进传播。******好处：GeneMANIA帮助成千上万的生物研究人员筛选大量的组学数据，以了解更多的基因功能，从而使他们受益。我们提出的工作将大大增加GeneMANIA技术在生物医学研究中的价值。GeneMANIA技术的易用性减轻了生物学家对专业生物信息学支持的需求，并促进了使用已经通过大量政府投资收集的非常大的基因组学数据。这将导致更好地获得分析服务，更有效地利用研究经费，新的科学发现和新的医疗方法