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Rule-based machine learning to address heterogeneity in high-dimensional survival data

基于规则的机器学习解决高维生存数据的异质性

基本信息

批准号：
10478828
负责人：
Alexa Abigail Woodward
金额：
$ 2.51万
依托单位：
UNIVERSITY OF PENNSYLVANIA
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：
2021-09-01 至 2022-12-05
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10478828
关键词：
Address Adult Age Algorithms Alkylating Agents Architecture Automobile Driving Biological Brain Neoplasms Cancer Prognosis Cells Clinical Complex Cox Models CpG Island Methylator Phenotype DNA DNA Integration DNA Methylation DNA Sequence DNA Sequence Alteration Data Data Set Development Dimensions Disease Disease Outcome Drug Targeting Elements Epigenetic Process Foundations Genes Genetic Genetic Epistasis Genetic Heterogeneity Genetic Models Genetic Transcription Genomics Glioma Head Heritability Heterogeneity Histones Hypermethylation Incidence Informatics Intervention MGMT gene Machine Learning Malignant - descriptor Malignant Neoplasms Methodology Methods Methylation MicroRNAs Modeling Modification Molecular Multiomic Data Outcome Output Pathway Analysis Pathway interactions Pattern Performance Persons Pharmacology Phenotype Play Precision therapeutics Prediction of Response to Therapy Primary Brain Neoplasms Prognosis Promoter Regions Proteomics RNA Research Research Personnel Risk Sample Size Single Nucleotide Polymorphism Somatic Mutation System Testing The Cancer Genome Atlas Training Treatment Efficacy Update Validation Visual Visualization Visualization software base cancer heterogeneity cancer risk cancer type data modeling deep learning disorder risk epigenomics experience feature selection forest genetic analysis genetic architecture genetic epidemiology genome wide association study genome wide methylation genomic data high dimensionality improved innovation insight large datasets learning classifier machine learning method multidimensional data multiple omics novel personalized approach personalized care potential biomarker preservation promoter skills success survival prediction temozolomide therapeutic target therapy resistant tool transcriptomics treatment response treatment strategy tumor tumor progression

项目摘要

Project Summary In the post-genomic era, researchers are met with an abundance of data to analyze and interpret. Genome- wide association analyses (GWAS) often boast millions of single-nucleotide polymorphisms (SNPs), alongside increasingly large epigenomic, transcriptomic, proteomic (multi-omic) and other data sets. While the current standard in genetic epidemiology emphasizes increased sample sizes, we propose that substantial progress can be made by developing improved methods to analyze the vast amount of multi-omic data that currently exists. A number of methodological challenges including dimensionality and the multiple testing burden have limited the success of many approaches thus far. Furthermore, only considering simple, linear associations leaves out the more likely scenario of complex genetic and multi-omic relationships driving risk and outcomes in common diseases. Heterogeneity is just one of the complex mechanisms that underlies disease risk and outcomes, but is arguably among the most difficult to model and detect. This project tackles this and other challenges in glioma, a highly heterogeneous cancer type. Improving upon available treatment strategies in cancer and glioma specifically will undoubtedly require a full characterization of genetic heterogeneity and epigenetic mechanisms. In addition to confronting the dimensionality of genetic and epigenetic data using a feature selection strategy that can detect both main effects and interaction and preserve heterogeneity, we will modify an existing method for detecting heterogeneity to accommodate censored survival data. First, in Aim 1, we will use simulated genetic survival data to establish the utility of a Relief-based feature selection algorithm in capturing complex genetic architectures (i.e., main effects, heterogeneity, and epistasis). We will compare it against standard approaches for high-dimensional feature selection of survival data. Aim 2 updates a learning classifier system (LCS), a type of rule-based machine learning that uses IF/THEN rules to model complex and heterogeneous problem spaces. To our knowledge, no LCS that handles censored survival data has been developed to date. After testing our survival LCS on simulated data and comparing it to standard survival methods, in Aim 3 we will implement it using somatic mutation and methylation data from the TCGA glioma dataset. Finally, as part of Aim 3, we will perform a pathway analysis using the LCS output in an effort to identify common biological pathways underlying heterogeneous associations. We will also utilize a network visualization tool to better understand interactions between features and provide a visual interpretation of the results. Findings from this project will lay the foundation for precision care and treatment of glioma. Our innovative approach to high-dimensional, heterogeneous survival data will be both generalizable and interpretable, qualities that are missing from current machine learning approaches. This project and the accompanying training plan undeniably provide an ideal setting to develop the skills and experience necessary to become and independent investigator at the forefront of genetic epidemiology and informatics.

项目总结