Transfer learning leveraging large-scale transcriptomics to map disrupted gene networks in cardiovascular disease

利用大规模转录组学的转移学习来绘制心血管疾病中被破坏的基因网络

• 批准号: 10696753
• 负责人: Christina Vicky Theodoris
• 金额: $ 47.25万
• 依托单位: J. DAVID GLADSTONE INSTITUTES
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-22 至 2028-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10696753
• 关键词: Acceleration; Address; Affect; Automobile Driving; Back; Biology; Cardiac; Cardiac Myocytes; Cardiovascular Diseases; Cardiovascular system; Cells; Chromosome Mapping; Clinical; Clinical Trials; Computational Biology; Computer Vision Systems; Computing Methodologies; Copy Number Polymorphism; Data; Data Set; Dependence; Disease; Disease Progression; Financial Support; Fostering; Gene Combinations; Gene Dosage; Genes; Genetic; Growth; Heart; Heart Diseases; Heart Valve Diseases; Human; Hypertrophic Cardiomyopathy; Individual; Institution; Interdisciplinary Study; Laboratories; Leadership; Learning; Life; Machine Learning; Maps; Medical; Mentors; Mentorship; Modeling; Natural Language Processing; Network-based; Patients; Phenotype; Physicians; Positioning Attribute; Process; Progressive Disease; Rare Diseases; Reporting; Research; Role; Sampling; Science; Scientist; Technology; Testing; Therapeutic; Tissues; Training; Uncertainty; Validation; base; candidate identification; cell type; collaborative environment; computing resources; deep learning model; design; dosage; drug discovery; experience; experimental study; gene network; gene regulatory network; genetic disorder diagnosis; genetic variant; human disease; human tissue; improved; in silico; induced pluripotent stem cell; knowledge base; machine learning model; medical schools; novel; programs; screening; stem cell model; success; symptom management; targeted treatment; therapy design; transcriptome; transcriptomics; transfer learning

PROJECT SUMMARY/ABSTRACT Mapping the gene regulatory networks driving human disease enables the design of network-correcting treatments that target the core disease mechanism rather than merely managing symptoms. I previously developed a framework for mapping disease-dependent gene networks to enable network-based screening leveraging machine learning and human induced pluripotent stem cell modeling that identified a promising network-correcting therapy for cardiac valve disease currently progressing towards clinical trial, reported in Cell1 and Science2. However, computationally inferring the network map requires large amounts of transcriptomic data to learn the connections between genes, which impedes network-correcting drug discovery in settings with limited data including rare disease and disease affecting clinically inaccessible tissues. Although data remains limited in these settings, recent advances in sequencing technologies have driven a rapid expansion in the amount of transcriptomic data available from human tissues more broadly. Recently, the concept of transfer learning has revolutionized fields such as natural language understanding and computer vision by leveraging deep learning models pretrained on large-scale general datasets that can then be fine- tuned towards a vast array of downstream tasks with limited application-specific data that would be too limited to yield meaningful predictions in isolation. To test whether an analogous approach could enable gene network predictions with limited data, I developed and pretrained my novel deep learning model, Geneformer, with a large-scale pretraining corpus I assembled with ~30 million human single cell transcriptomes, thereby generating an invaluable checkpoint from which fine-tuning towards a broad range of downstream applications could be pursued to accelerate discovery of key network regulators and candidate network-correcting therapies. Geneformer consistently boosted predictive accuracy in a diverse panel of downstream tasks using just a limited set of task-specific training examples. I now propose to leverage Geneformer’s learned understanding of contextual gene network dynamics to address two major challenges in cardiac biology. In Aim 1, I will determine novel dosage-sensitive gene combinations and their context-dependency in cardiac cell types, thereby generating a map of contextual dosage sensitivity for genes individually or in combination that has the potential of dramatically improving our interpretation of copy number variants in genetic diagnosis of cardiac disease. In Aim 2, I will map the dysregulated gene network and discover candidate network-correcting therapeutics in a prototypical rare disease affecting clinically inaccessible tissue where progress has been impeded by limited data, hypertrophic cardiomyopathy, to accelerate the discovery of a much-needed targeted therapeutic for this life-threatening progressive disease. Overall, my novel deep learning model, Geneformer, pretrained with large-scale single cell transcriptomic data has the potential of revolutionizing the field of network biology through transfer learning to accelerate discovery in settings with limited data.

项目摘要/摘要 绘制驱动人类疾病的基因调控网络图使网络纠正的设计成为可能 针对核心疾病机制而不仅仅是控制症状的治疗。我之前 开发了一个绘制疾病依赖基因网络的框架，以实现基于网络的筛查 利用机器学习和人类诱导的多潜能干细胞建模，确定了一种有希望的 心脏瓣膜疾病的网络矫正治疗目前正在进入临床试验阶段，报道于 细胞1和科学2。然而，通过计算推断网络图需要大量的 转录数据，以了解基因之间的联系，这阻碍了网络纠正药物发现 在数据有限的情况下，包括罕见疾病和影响临床无法接触到的组织的疾病。 尽管数据在这些环境中仍然有限，但测序技术的最新进步推动了 从更广泛的人体组织中获得的转录数据的数量迅速扩大。最近， 迁移学习的概念已经给自然语言理解和计算机等领域带来了革命性的变化 通过利用在大规模通用数据集上预先培训的深度学习模型来实现愿景，然后这些模型可以很好地- 调整为使用有限的特定于应用程序的数据的大量下游任务，这些数据将过于有限 孤立地做出有意义的预测。测试一种类似的方法是否可以实现基因网络 利用有限的数据进行预测，我开发并预先训练了我的新深度学习模型Geneformer，它具有 大规模预训练语料库I组装了约3000万个人类单细胞转录本，从而 生成一个无价的检查点，从该检查点针对广泛的下游应用程序进行微调 可用于加速发现关键网络监管机构和候选网络纠正 治疗。Geneformer在一系列不同的下游任务中持续提高预测准确性 只是一组有限的特定于任务的培训示例。我现在提议利用Geneform所学到的 了解背景基因网络动力学，以应对心脏生物学中的两个主要挑战。在AIM 1，我将确定新的剂量敏感基因组合及其在心肌细胞中的上下文依赖性 类型，从而为单独或组合的基因生成上下文剂量敏感度图 有可能极大地改进我们对拷贝数变异在基因诊断中的解释 心脏病。在目标2中，我将绘制失调的基因网络图，并发现候选网络校正 对一种影响临床难以接触到的组织的典型罕见疾病的治疗取得了进展 受阻于有限的数据，肥厚型心肌病加速发现亟需的靶向 治疗这种危及生命的进行性疾病。总体而言，我的新深度学习模型Geneform， 用大规模单细胞转录数据进行预训练，有可能使 网络生物学通过迁移学习来加速在数据有限的环境中的发现。