发表机构
Center for Data Science; New York University（数据科学中心; 纽约大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对基因调控网络重建的挑战，开发了PMF-GRN和GLM-Prior两个框架。前者将GRN推断作为概率图形模型，后者微调预训练模型预测基因相互作用，二者结合推动GRN重建的两阶段观点，改进了网络推断方法。
AI 中文摘要
基因调控网络（GRNs）将转录因子（TF）蛋白与其靶基因联系起来，但在实际和方法学限制下，从全基因组数据重建这些网络仍具有挑战性。许多方法将建模假设与特定推断程序相结合，依赖启发式模型选择，评估受不完整参考网络和缺乏不确定性的点估计输出限制。GRN重建还依赖先验知识来约束TF-基因相互作用，然而现有先验通常依赖测定法，难以跨物种和特征较少的系统转移。本文开发了两个互补框架来解决这些限制。第一个是PMF-GRN，将GRN推断视为通过变分推断优化的概率图形模型，实现有原则的模型选择和不确定性感知的边估计。第二个是GLM-Prior，通过微调预训练的核苷酸变压器直接从核苷酸序列预测TF-靶基因相互作用，同时在酵母、小鼠和人类环境中进行泛化。PMF-GRN和GLM-Prior共同推动了GRN重建的两阶段观点，即序列衍生的先验提供可转移的起始支架，概率推断在不完整评估资源下用量化的不确定性细化调控估计。
英文摘要
Gene regulatory networks (GRNs) link transcription factor (TF) proteins to their target genes, yet reconstructing these networks from genome-wide data remains challenging under practical and methodological constraints. Many methods couple modeling assumptions to a specific inference procedure and rely on heuristic model selection, while evaluation is constrained by incomplete reference networks and point-estimate outputs that lack uncertainty. GRN reconstruction also depends on prior knowledge to constrain TF-gene interactions, yet available priors are often assay-dependent and difficult to transfer across species and less-characterized systems. In this thesis, we develop two complementary frameworks that address these limitations. In the first, PMF-GRN casts GRN inference as a probabilistic graphical model optimized by variational inference, enabling principled model selection and uncertainty-aware edge estimates. In the second, GLM-Prior addresses the prior bottleneck by fine-tuning the pretrained Nucleotide Transformer to predict TF-target gene interactions directly from nucleotide sequence, while generalizing across yeast, mouse, and human settings. Together, PMF-GRN and GLM-Prior motivate a dual-stage view of GRN reconstruction in which sequence-derived priors provide a transferable starting scaffold and probabilistic inference refines regulatory estimates with quantified uncertainty under incomplete evaluation resources.
CommentsPhD thesis
