Causal Effect Estimation of Regulatory Molecules

调节分子的因果效应估计

• 批准号: 10455118
• 负责人: Amir Asiaeetaheri
• 金额: $ 24.9万
• 依托单位: VANDERBILT UNIVERSITY MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-06 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10455118
• 关键词: Address; Adverse effects; Advisory Committees; Affect; Autoimmunity; Award; Base Pairing; Binding; Binding Sites; Biological; Biological Sciences; Cardiovascular Diseases; Cells; ChIP-seq; Collaborations; Communities; Complex; Comprehensive Cancer Center; Computational Biology; DNA; Data; Data Science; Data Sources; Diabetes Mellitus; Differential Equation; Dimensions; Disease; Drug Design; Economics; Faculty; Gene Expression; Gene Expression Regulation; Genes; Genetic Transcription; Genomics; Genotype; Genotype-Tissue Expression Project; Goals; Hospitals; Institutes; Knowledge; Light; Linear Models; Literature; Machine Learning; Malignant Neoplasms; Mathematics; Measures; Mentors; Messenger RNA; Methods; MicroRNAs; Modeling; Modernization; Molecular Biology; Natural experiment; Ohio; Outcome; Output; Pathway interactions; Performance; Pharmaceutical Preparations; Phase; Phenotype; Positioning Attribute; RNA Binding; Regulator Genes; Regulatory Element; Research; Role; Sample Size; Sampling; Site; Somatic Mutation; Source; Techniques; The Cancer Genome Atlas; Tissues; Toxic effect; Training; Transfection; Translations; Universities; base; cancer cell; career development; causal model; collaborative environment; computer studies; computerized tools; deep neural network; design; experimental study; feature selection; heterogenous data; high dimensionality; human disease; improved; insight; machine learning method; physical model; predicting response; predictive modeling; response; statistical and machine learning; statistics; targeted treatment; tenure track; tool; transcription factor; transcriptome; tumor; vector

Project Summary/Abstract Transcription factors and microRNAs are essential regulatory molecules (RM) that control messenger RNAs (mRNA) and are known to be dysregulated in human diseases. Each RM may affect multiple pathways of the cell which is both a blessing and a curse. If a therapy targets the proper RM, it can attack the disease from multiple fronts and increase efficacy. On the other hand, targeted therapy may result in serious adverse effects due to our limited knowledge of the downstream causal effect of RM manipulation. Although the local bindings between single RMs and their targets have been studied computationally and experimentally, the intensity of functional consequences of such bindings on the transcriptome is unclear. Here, I propose statistical machine learning techniques and causal inference methods to predict the observed variability of gene expression using only regulatory molecules and estimate their downstream causal effect on the entire transcriptome. To achieve this goal, I start in Aim 1 by building a multi-response predictive model to predict the whole transcriptome using only RMs. This goal is challenging because the dimension of the response vector is more than the number of samples and I will use techniques from high-dimensional statistics to address this issue. In Aim 2, I will go beyond predictive modeling by estimating the causal effect of RMs on the transcriptome using invariant causal prediction. I will leverage the rapidly growing literature which connects causal inference to invariant prediction accuracy across heterogeneous data sources to infer the causal effect of RMs on mRNA. Having developed both predictive and causal models of RMs contribution to gene regulation, in Aim 3 during the R00 phase, I will focus on the most recent advances in double/debiased machine learning which allows the use of scalable machine learning methods for reliable estimation of causal effect of RMs on transcription. My proposed research will bring the most advanced statistical machine learning and causal inference techniques to genomics research and help design more effective targeted therapies by providing insights into the global role of RMs in gene expression regulation. During the training phase of the award, with the support of my outstanding mentoring team and scientific advisory committee, I will gain expertise in molecular biology and genomics while perfecting my knowledge of causal inference and machine learning. The Ohio State University Comprehensive Cancer Center – James Hospital and the Mathematical Biosciences Institute will provide me with the ideal interdisciplinary environment to bridge data science and genomics and will help me achieve my career development goals and transition to a tenure-track faculty position.

项目总结/摘要 转录因子和microRNA是控制信使RNA的重要调控分子 在人类疾病中，这些蛋白质（mRNA）是不稳定的，并且已知在人类疾病中失调。每个RM都可能影响多个通路， 这既是一种祝福，也是一种诅咒。如果一种疗法靶向适当的RM，它可以从 多个方面，提高效率。另一方面，靶向治疗可能导致严重的不良反应 由于我们对RM操纵的下游因果效应的了解有限。虽然本地绑定 已经通过计算和实验研究了单个RM与其目标之间的关系， 这种结合对转录组的功能影响尚不清楚。在这里，我建议统计机器 学习技术和因果推理方法来预测观察到的基因表达的变异性 仅使用调节分子，并估计其下游因果效应对整个 转录组为了实现这一目标，我从目标1开始，通过构建多响应预测模型来预测 整个转录组。这一目标具有挑战性，因为响应向量的维数 这比样本的数量要多，我将使用高维统计技术来解决这个问题 问题.在目标2中，我将通过估计RM对转录组的因果效应来超越预测模型 使用不变因果预测。我将利用快速增长的文献， 到跨异质数据源的不变预测准确性，以推断RM对mRNA的因果影响。 在开发了RM对基因调控的贡献的预测模型和因果模型之后，在目标3中， R 00阶段，我将重点介绍双/去偏机器学习的最新进展，它允许 使用可扩展的机器学习方法来可靠地估计RM对转录的因果效应。我 拟议的研究将带来最先进的统计机器学习和因果推理技术， 基因组学研究，并通过提供对全球作用的见解，帮助设计更有效的靶向治疗 在基因表达调控中的作用。在培训阶段的奖励，与我的支持优秀 指导团队和科学咨询委员会，我将获得分子生物学和基因组学方面的专业知识， 完善我的因果推理和机器学习知识。俄亥俄州州立大学综合 癌症中心-詹姆斯医院和数学生物科学研究所将为我提供理想的 跨学科的环境，以桥梁数据科学和基因组学，并将帮助我实现我的职业生涯 发展目标和过渡到终身教职。