Neural Network Models for Modelling, Design and Optimization of Structurally Complex Entities in Biomedical Data Science

用于生物医学数据科学中结构复杂实体建模、设计和优化的神经网络模型

• 批准号: RGPIN-2021-03879
• 负责人: Li, Yifeng
• 金额: $ 2.48万
• 依托单位: Brock University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=749851
• 关键词: Neural; Network; Models; Modelling; Design

The rise of deep learning approaches has reshaped the research and development landscape in machine learning and data science. Deep learning modelling has been rapidly evolving with superior performances in many challenging tasks ranging from feature selection, classification, data understanding, decision making, to creative designs. The success of deep learning is largely due to (1) the acceptance of distributed representation and symbolic embedding theories, (2) the wide use of convolutional operations for automatic feature learning, (3) the modelling of memory and attention mechanisms, (4) the marriage of deep neural networks with reinforcement learning, and (5) the born of neural or deep generative models (DGMs). As a fast-growing family of deep learning techniques, DGMs use deterministic neural networks to model dependencies among visible and latent variables. Majorly applied in computer vision and natural language processing, DGMs enable versatile functionalities such as sampling, seasoning, simulation, design, optimization, and domain transformation. The adoption of deep learning in biomedical data science exhibits huge potentials but meanwhile faces grand challenges to represent and model structurally complex objects, learn on small data, and design from exponential number of possibilities. As a continuation of our research excellence in bioinformatics, the long-term goal of this research program is to investigate and implement novel deep-learning-based approaches (particularly DGMs) for the effective representation and modelling of extremely long sequences (ELSs) and structurally complex entities (SCEs), and apply them to solving challenging real-world biomedical data science tasks that are currently largely limited due to structural complexity and data availability. It is anticipated that our research and development will be disruptive by providing fresh ideas, inspirations, and findings. Three short-term objectives will be pursued within a five-year period. First, novel multi-module, multi-modal, and few-shot deep learning methods will be devised to model extremely long sequences and structurally complex entities which are often encountered in biomedical data analytics. Second, data-model co-evolutionary learning processes will be fully investigated to better solve black-box global multi-objective optimization tasks. We seek novel learning paradigms that can integratively improve both model learning and data quality through the data representation space. Third, our devised and newly emerged deep learning methods will be applied to address three challenging biomedical data science problems: genome annotation, aptamer drug design, and interactome predictions. This research will deliver a collection of next-generation machine learning approaches and data science tools that are beneficial to the research communities and the public good.

深度学习方法的兴起重塑了机器学习和数据科学的研发格局。深度学习建模一直在快速发展，在许多具有挑战性的任务中表现出上级性能，从特征选择，分类，数据理解，决策制定到创意设计。深度学习的成功主要归功于（1）分布式表示和符号嵌入理论的接受，（2）卷积运算在自动特征学习中的广泛使用，（3）记忆和注意力机制的建模，（4）深度神经网络与强化学习的结合，以及（5）神经或深度生成模型（DGMs）的诞生。作为一个快速发展的深度学习技术家族，DGMs使用确定性神经网络来建模可见变量和潜在变量之间的依赖关系。DGMs主要应用于计算机视觉和自然语言处理，可实现多功能，如采样，调味，模拟，设计，优化和域转换。深度学习在生物医学数据科学中的应用显示出巨大的潜力，但同时也面临着巨大的挑战，即表示和建模结构复杂的对象，在小数据上学习，以及从指数数量的可能性中进行设计。作为我们在生物信息学方面卓越研究的延续，该研究计划的长期目标是研究和实施基于深度学习的新方法（特别是DGMs），用于有效表示和建模极长序列（ELS）和结构复杂实体（SCE），并将其应用于解决具有挑战性的现实生物医学数据科学任务，这些任务目前由于结构复杂性和数据可用性而在很大程度上受到限制。预计我们的研究和开发将通过提供新的想法，灵感和发现而具有颠覆性。将在五年内实现三个短期目标。首先，将设计新颖的多模块、多模态和少镜头深度学习方法，以建模生物医学数据分析中经常遇到的超长序列和结构复杂的实体。其次，将充分研究数据模型协同进化学习过程，以更好地解决黑箱全局多目标优化任务。我们寻求新的学习范式，可以通过数据表示空间综合提高模型学习和数据质量。第三，我们设计和新出现的深度学习方法将被应用于解决三个具有挑战性的生物医学数据科学问题：基因组注释，适体药物设计和相互作用组预测。这项研究将提供一系列有利于研究社区和公共利益的下一代机器学习方法和数据科学工具。