Learning representations from heterogeneous data for digital health

从数字健康的异构数据中学习表示

• 批准号: RGPIN-2021-03297
• 负责人: WU, FANGXIANG
• 金额: $ 4.66万
• 依托单位: University of Saskatchewan
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=742483
• 关键词: Learning; representations; heterogeneous; data; digital

Molecular biomarkers, including genes, miRNAs, LncRNAs, circRNAs, etc., not only help understand the mechanisms of complex human diseases, but also help their diagnosis, prognosis, treatment and drug development. The emerging high throughput technologies generate copious heterogeneous data such as sequence data, expression data, semantic data, and some experimentally verified association(network) data. Although existing machine learning methods can be applied for various prediction tasks from such heterogeneous data, there are some challenges and limitations, as well as some obstacles yet to overcome, including lack of integration of heterogeneous data, lack of balanced training data, and lack of model explanation or intelligibility. In this proposed research program my long-term goal is to develop the novel artificial intelligence models and algorithms for effectively and efficiently learning strong comprehensive representations of biomarkers, drugs and diseases from heterogeneous data while applying them to various prediction tasks, which are key to digital health for precision medicine. To achieve my long-term goal, three specific objectives are designed in this proposal as follows. Objective 1: Developing improved machine learning models for learning representations of biomarkers, drugs and diseases by integrating heterogeneous data; Objective 2: Developing deep neural network models for learning representations of biomarkers, drugs and diseases from heterogeneous data. Objective 3: Developing intelligible machine learning models that can learn the representations of biomarkers, drugs and diseases for interpretable prediction results, so that humans can understand and interpret the outcomes. Our developed models and algorithms will be applied to several prediction tasks such as gene-disease associations, miRNA-disease associations, circRNA-disease associations, LncRNA-disease associations or dug repositioning. This proposed research program will make advances in both artificial intelligence and digital health that will ultimately aid in processing, analyzing, understanding, and exploiting heterogeneous health data. The methods to be developed could also be used by researchers in other areas (natural language processing, image analytics, just to name a few) in guiding their methods and discovering new knowledge through our proposed knowledge translation strategies. Through this proposed research program, three Ph.D., and several M.Sc. and undergraduate students will be trained to gain advanced knowledge in artificial intelligence, digital health, and software development, as well as the problem solving skills in digital health and other fields in which artificial intelligence is indispensable. In addition, high demand in artificial intelligence and digital health combined with my collaborative research approach provides my students with numerous opportunities for working in this exciting multidisciplinary training environment.

分子生物标志物，包括基因、miRNA、LncRNA、circRNA等，不仅有助于了解复杂人类疾病的机制，而且有助于其诊断、预后、治疗和药物开发。新兴的高通量技术产生了大量异构数据，例如序列数据、表达数据、语义数据和一些经过实验验证的关联（网络）数据。尽管现有的机器学习方法可以应用于此类异构数据的各种预测任务，但仍存在一些挑战和局限性，以及一些尚未克服的障碍，包括缺乏异构数据的集成、缺乏平衡的训练数据以及缺乏模型解释或可理解性。在这个拟议的研究项目中，我的长期目标是开发新颖的人工智能模型和算法，以便有效且高效地从异构数据中学习生物标志物、药物和疾病的强大综合表示，同时将其应用于各种预测任务，这是精准医学数字健康的关键。为了实现我的长期目标，本提案设计了以下三个具体目标。目标 1：开发改进的机器学习模型，通过整合异构数据来学习生物标志物、药物和疾病的表示；目标 2：开发深度神经网络模型，用于从异构数据中学习生物标志物、药物和疾病的表示。目标 3：开发可理解的机器学习模型，可以学习生物标志物、药物和疾病的表示，以获得可解释的预测结果，以便人类能够理解和解释结果。我们开发的模型和算法将应用于多种预测任务，例如基因-疾病关联、miRNA-疾病关联、circRNA-疾病关联、LncRNA-疾病关联或挖掘重新定位。这项拟议的研究计划将在人工智能和数字健康方面取得进展，最终将有助于处理、分析、理解和利用异构健康数据。其他领域（自然语言处理、图像分析等）的研究人员也可以使用待开发的方法来指导他们的方法并通过我们提出的知识翻译策略发现新知识。通过这项拟议的研究计划，三名博士和几名硕士获得了博士学位。本科生将接受人工智能、数字健康、软件开发等方面的高级知识，以及数字健康等人工智能不可或缺的领域解决问题的技能。此外，对人工智能和数字健康的高需求加上我的协作研究方法为我的学生提供了在这个令人兴奋的多学科培训环境中工作的大量机会。