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

CRII：III：基因-蛋白质网络推理和集成中深度学习的可扩展概率模型选择方法

• 批准号: 1850492
• 负责人: Rui Li
• 金额: $ 17.19万
• 依托单位: Rochester Institute of Tech
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1850492&HistoricalAwards=false
• 关键词: CRII; III; Scalable; Probabilistic; Model

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.

各个基因与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复杂的生化反应和信号通路的复杂级联反应的细胞过程。这些途径的任何组成部分的失调可能导致多种人类病理，包括癌症，心血管疾病，神经退行性疾病和代谢疾病。深度学习技术归因于人工智能和机器学习的最新进展，并且已经出现了，许多研究试图以基因组/蛋白质组范围的规模构建和分析基因/蛋白质相互作用，以描述其全球特征。但是，生物学数据的异质性质及其连续进化对深神经网络的建筑设计构成了独特的挑战。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，并且耗时且不可扩展。这项研究建议使深层神经网络自动经历定性增长，以适应新的异质数据的富裕信息。该项目将提供强大的新型计算工具，以发现和靶向基因蛋白相互作用，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。该项目将着重于开发可伸缩的概率概率选择方法，以推导基因网络构造网络网络互动网络的深度神经网络互动互动互动互动互动分级和集成。该项目将为提出的模型选择方法设计有效的推理算法，以使其转换为可部署的工具，以供生物学家使用。研究人员将开发新型的原始模型选择方法，以推断出异质生物学数据所必需的深神经网络的最合理的架构。通过将神经网络体系结构的假设空间建模为随机过程，提出的方法使神经网络体系结构能够根据生物学数据发展。 设计和实施有效的技术，以利用推理计算可牵引。该项目提议评估基于变化方法的大约基于替代方案的边际可能性。研究者将比较深层神经网络的性能，其构造方法是通过提出的模型选择方法与最新的网络中的最新方法以及真核有机体的功能注释S. cerevisiae和人类细胞的功能注释，通过将蛋白质功能视为多型型分类问题，并在两种辅助效果中预测蛋白质功能：法定任务，并被认为是值得通过基金会的智力优点和更广泛影响的审查标准来评估的值得支持的。

项目成果

Joint Inference for Neural Network Depth and Dropout Regularization

• 发表时间: 2021
• 作者: C. KishanK.;Rui Li;MohammadMahdi Gilany

SFusion: Self-attention Based N-to-One Multimodal Fusion Block

• DOI: 10.1007/978-3-031-43895-0_15
• 发表时间: 2022-08
• 作者: Ze Liu;Jia Wei;Rui Li;Jianlong Zhou

Joint Inference for Neural Network Depth and Dropout Regularizatio

神经网络深度和 Dropout 正则化的联合推理

• 发表时间: 2021
• 期刊: Advances in neural information processing systems
• 作者: KC, Kishan;Li, Rui;Gilany, Mehdi
• 通讯作者: Gilany, Mehdi

Interpretable Structured Learning with a Sparse Sequence Encoder for Prediction Analysis on Protein-Protein Interaction

使用稀疏序列编码器进行可解释的结构化学习，用于蛋白质-蛋白质相互作用的预测分析

• 发表时间: 2021
• 期刊: International Conference on Pattern Recognition
• 作者: KC, Kishan;Li, Rui;Cui, Feng;Haake, Anne R
• 通讯作者: Haake, Anne R

AdaVAE: Bayesian Structural Adaptation for Variational Autoencoders

AdaVAE：变分自动编码器的贝叶斯结构适应

• 发表时间: 2023
• 期刊: Advances in neural information processing systems
• 作者: Regmi, P;Li, R.
• 通讯作者: Li, R.