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 映射驱动人类疾病的基因调节网络可以设计网络校正 针对核心疾病机制的治疗方法，而不仅仅是管理症状。我以前 开发了绘制依赖疾病的基因网络的框架，以实现基于网络的筛查 利用机器学习和人类诱导的多能干细胞建模，确定了诺言 据报道 Cell1和Science2。但是，在计算上推断网络图需要大量 转录组数据以学习基因之间的联系，这阻碍了网络纠正的药物发现 在数据有限的环境中，包括罕见疾病和影响临床上无法访问的时机的疾病。 尽管在这些环境中数据仍然有限，但是测序技术的最新进展已驱动A 从人体组织获得的转录组数据量的快速扩展。最近， 转移学习的概念已彻底改变了自然语言理解和计算机等领域 通过利用在大规模一般数据集上预测的深度学习模型来进行的视觉，然后可以很好地 朝着有限的特定于应用程序的数据朝着各种各样的下游任务进行调整 孤立地产生有意义的预测。测试类似方法是否可以启用基因网络 我的数据有限的预测，我开发并鉴定了我的新型深度学习模型Geneformer，并用 我与约3000万人类单细胞转录组组装的大规模预处理语料库，从而 生成一个宝贵的检查站 可以追求以加速关键网络监管机构和候选网络纠正的发现 疗法。 Geneformer使用使用 只是一组有限的特定任务培训示例。我现在建议利用Geneformer学到的 了解情境基因网络动态，以应对心脏生物学的两个主要挑战。目标 1，我将确定新型剂量敏感基因组合及其在心脏细胞中的上下文依赖性 类型，从而为基因单独或组合产生上下文剂量敏感性的图 有可能显着改善我们对副本数变异的解释， 心脏病。在AIM 2中，我将映射失调的基因网络并发现候选网络校正 在典型的罕见疾病中的治疗剂，影响临床上无法访问的组织，而进展已经取得进展 受到有限数据的阻碍，肥厚的心肌病，以加速急需的目标 这种威胁生命的渐进疾病的治疗性。总的来说，我的新颖学习模型，基因形式， 通过大规模单细胞转录组数据预处理的潜力是彻底改变 通过转移学习的网络生物学，以加速有限数据的设置中的发现。