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Incorporating molecular network knowledge into predictive data-driven models

将分子网络知识纳入预测数据驱动模型

基本信息

批准号：
10246414
负责人：
Christopher Andrew Mancuso
金额：
$ 7.05万
依托单位：
MICHIGAN STATE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-01 至 2022-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10246414
关键词：
Accounting Address Age Architecture Back Binding Biological Biological Models Biological Process Biology Cell physiology Complex Computational Technique Data Data Analyses Data Collection Development Plans Dimensions Disease Engineering Ethnic Origin Fellowship Follow-Up Studies Freedom Gene Expression Gene Proteins Genes Genetic Goals Human Joints Knowledge Label Machine Learning Measures Metadata Methods Modeling Modernization Molecular Nature Noise Pathway interactions Patients Pattern Phenotype Play Precision Medicine Initiative Process Research Research Personnel Role Sampling Source Structure Techniques Tissues Translating base data to knowledge deep learning design disease phenotype driving force experimental study functional genomics gene function genetic association genetic signature genomic data genomic profiles human data interest machine learning method mathematical sciences novel predictive modeling professor sex statistical and machine learning tool trait transcriptome

项目摘要

Modern computational techniques based on machine-learning (ML) and, more recently, deep-learning (DL) are playing a critical role in realizing the precision medicine initiative. However, there is a critical need to systematically combine these powerful data-driven techniques with prior molecular network knowledge to make more accurate predictive models while also satisfactorily explaining their predictions in terms of mechanisms underlying complex traits and diseases. I propose to use domain specific knowledge from biology and computing to tackle three outstanding problems: 1) how to predict missing labels associated with millions of publicly available samples? 2) what molecular/cellular function can be attached to these samples and 3) how can we translate the findings from human data to a model species and back? Network-constrained Deep Learning for Metadata Imputation: Most multifactorial phenotypes are tissue dependent and manifest differently depending on age, sex, and ethnicity. However, a majority of publicly-available genomic data lack these labels. I will develop a network-guided approach to predict missing metadata of samples based on their expression profiles by designing novel data-driven models where the model architecture and/or structure of the input data are constrained by an underlying gene network. Network-guided Functional Analysis of Genomic Data: High-throughput experiments often generate lists of genes of interest that are hard to interpret. Functional enrichment analysis (FEA) is a powerful tool that attaches functional meaning to an experimental set of genes by summarizing them into sets of pathways/processes. However, standard FEA analysis is limited by incomplete knowledge of gene function, lack of context of the underlying gene network, and noise in expression data. I will address these limitations by developing a network-guided approach that jointly captures genes, their interactions, and their known biological pathways/processes into a common, low-dimensional space that facilitates deriving biological meaning by comparing the distance between the experimental gene set and the pathway/process of interest. Joint Multi-Species Genomic Data Analysis and Knowledge Transfer: In particular, finding the optimal model system to use in a follow-up study based on genetic signatures derived from human experiments is challenging because genetic networks can be quite different from species to species. I propose to use data-driven models to embed heterogeneous networks comprised of human genes and model species genes into a common, low-dimensional space to better compare genetic signatures between two (or even multiple) species. I will apply these methods to three specific tasks, but I emphasize that the results of this study will be transferable to any other biological problem where complex gene/protein interactions are a major component. I have surrounded myself with a great support team and developed a strong professional development plan. The freedom and support provided by the F32 fellowship will be instrumental in achieving my goal of becoming a professor with an independent research group.

基于机器学习（ML）和最近的深度学习（DL）的现代计算技术