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

个体基因和蛋白质之间相互作用的详细表征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生化反应和信号通路的复杂级联的细胞过程。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，以描述它们的全局特征。然而，生物数据的异构性及其不断演化对深度神经网络的架构设计提出了独特的挑战。最先进的解决方案严重依赖于专业知识、启发式和实验，而且耗时且不可扩展。本研究提出了使深度神经网络能够自动经历定性增长，以适应新的异构数据在积累过程中产生的更丰富的信息。该项目将提供强大的新型计算工具，以发现和瞄准基因-蛋白质相互作用，驱动癌症、糖尿病和神经系统疾病等调节性疾病，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，以推断基因-蛋白质相互作用网络推断和集成的深度神经网络架构。该项目将为提出的模型选择方法设计有效的推理算法，使其能够转化为生物学家使用的可部署工具。研究人员将开发新的原则性模型选择方法，以推断由异质生物数据保证的最合理的深度神经网络架构。该方法将神经网络结构的假设空间建模为随机过程，使神经网络结构能够根据生物数据进行进化；设计和实现有效的技术，使推理的使用在计算上易于处理。该项目提出了基于变分方法近似评估选择偏好的边际可能性。研究者将通过将蛋白质功能预测作为一个多标签分类问题，并使用交叉验证和时间保留验证两种互补的方法来衡量性能，将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。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值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.