CRII: III: A Scalable Probabilistic Model Selection Method for Deep Learning in Gene-Protein Network Inference and Integration

单个基因和蛋白质之间相互作用的详细描述一直是生物学研究的重点之一，因为这些相互作用控制着涉及复杂的生化反应和信号通路的级联反应的细胞过程。这些通路的任何组成部分的失调都可能导致广泛的人类病理，包括癌症、心血管疾病、神经退行性疾病和代谢性疾病。由于人工智能和机器学习的最新进展，深度学习技术已经出现，许多研究试图在基因组/蛋白质组范围内构建和分析基因/蛋白质相互作用，以描述它们的全球特征。然而，生物数据的异构性及其不断演化对深度神经网络的体系结构设计提出了独特的挑战。最先进的解决方案在很大程度上依赖于专业知识、启发式方法和实验，而且非常耗时且不可扩展。这项研究建议使深度神经网络能够自动经历质的增长，以适应来自新的异质数据的更丰富的信息，因为它们正在积累。该项目将提供强大的新型计算工具来发现和定位驱动调节性疾病如癌症、糖尿病和神经疾病的基因-蛋白质相互作用，同时深入了解细胞信息处理背后的基本原理。该项目将专注于开发可扩展的概率模型选择方法来推断用于基因-蛋白质相互作用网络推理和整合的深层神经网络结构。该项目将为建议的模型选择方法设计有效的推理算法，使其能够转换为可部署的工具，供生物学家使用。研究人员将开发新的原则性模型选择方法，以推断异质生物数据所保证的最合理的深层神经网络结构。通过将神经网络结构的假设空间建模为随机过程，该方法使神经网络结构能够根据生物数据进行进化，并设计和实现高效的技术来利用计算上易于处理的推理。该项目建议基于变分方法近似地评估优先于备选方案的边际可能性。研究人员将把蛋白质功能预测作为一个多标签分类问题，并用两种互补的方法：交叉验证和时间保持验证来衡量性能，将其结构用所提出的模型选择方法学习的深度神经网络的性能与真核生物酿酒酵母和人类细胞的网络和功能注释的最新方法进行比较。这一奖项反映了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。

Detailed characterization of interactions between individual genes and proteins has been one of the focuses of biological research, since these interactions control cellular processes involving complicated cascades of biochemical reactions and signaling pathways. Dysregulation of any component of these pathways can lead to a broad spectrum of human pathologies including cancer, cardiovascular disease, neurodegenerative conditions, and metabolic diseases. Deep learning techniques, which is attributed to the most recent advances in artificial intelligence and machine learning, have emerged and many studies have tried to construct and analyze gene/protein interactions at a genome/proteome-wide scale to describe their global characteristics. However, the heterogeneous nature of the biological data and their continuous evolution pose a unique challenge to the architecture design of deep neural networks. The state-of-the-art solutions heavily rely on expertise, heuristics, and experimentation, and are time-consuming and not scalable. This research proposes to enable deep neural networks to automatically go through a qualitative growth to accommodate richer information from new heterogenous data as they are accumulating. This project will provide powerful novel computational tools to discover and target gene-protein interactions driving regulatory diseases such as cancer, diabetes and neurological disorders, while gaining insight into the fundamental principles behind cellular information processing.This project will focus on developing scalable probabilistic model selection approaches to infer deep neural network architectures for gene-protein interaction network inference and integration. The project will design efficient inference algorithms for the proposed model selection approach to enable its translation into a deployable tool for use by biologists. The researchers will develop novel principled model selection methods to infer the most plausible architectures of deep neural networks warranted by the heterogenous biological data. By modeling the hypothesis space of neural network architectures as stochastic processes, the proposed method enables neural network architectures to evolve according to the biological data; to design and implement efficient techniques to make use of the inference computationally tractable. The project proposes to evaluate the marginal likelihoods for preference to the alternatives approximately based on variational methods. The investigator will compare the performance of the deep neural networks whose architectures are learned with the proposed model selection method with the state-of-the-art methods on networks and functional annotations of eukaryotic organism S. cerevisiae and human cells by treating protein function prediction as a multi-label classification problem, and measuring the performance with two complementary approaches: cross-validation and temporal holdout validation.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.