Causal Effect Estimation of Regulatory Molecules

调节分子的因果效应估计

基本信息

批准号：
10626830
负责人：
Amir Asiaeetaheri
金额：
$ 24.06万
依托单位：
VANDERBILT UNIVERSITY MEDICAL CENTER
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-08-06 至 2025-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10626830
关键词：
Address Adverse effects Advisory Committees Affect Algorithms 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 Knowledge 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 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 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 framework machine learning method machine learning model machine learning prediction physical model predicting response predictive modeling predictive tools response statistical and machine learning statistics targeted treatment tenure track transcription factor transcriptome tumor vector

项目摘要

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.

项目概要/摘要转录因子和 microRNA 是控制信使 RNA 的重要调节分子 (RM) (mRNA) 并已知在人类疾病中失调。如果治疗针对正确的 RM，它可以从根本上攻击疾病。另一方面，靶向治疗可能会导致严重的副作用。由于我们对 RM 操纵的下游因果效应的了解有限，尽管局部绑定。通过计算和实验研究了单个 RM 与其目标之间的强度这种结合对转录组的功能影响尚不清楚。在这里，我提出了统计机器。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对整个下游的因果影响为了实现这一目标，我从目标 1 开始，构建了一个多响应预测模型来进行预测。仅使用 RM 来实现整个转录组这个目标具有挑战性，因为响应向量的维度。超过了样本的数量，我将使用高维统计技术来解决这个问题在目标 2 中，我将通过估计 RM 对转录组的因果影响来超越预测模型。使用不变的因果预测，我将利用快速增长的连接因果推理的文献。跨异构数据源的不变预测精度来推断 RM 对 mRNA 的因果效应。在目标 3 中，开发了 RM 对基因调控贡献的预测模型和因果模型 R00 阶段，我将重点关注双重/去偏机器学习的最新进展，这使得使用可扩展的机器学习方法来可靠估计 RM 对转录的因果影响。拟议的研究将把最先进的统计机器学习和因果推理技术带到基因组学研究并通过提供对全球作用的见解来帮助设计更有效的靶向疗法在该奖项的培训阶段，在我杰出的支持下。指导团队和科学顾问委员会，我将获得分子生物学和基因组学方面的专业知识，同时完善我的因果推理和机器学习知识。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选择连接数据科学和基因组学的跨学科环境将帮助我实现我的职业生涯发展目标以及向终身教授职位的过渡。