Interpretable Deep Learning Methods to Investigate Genetics and Epigenetics of Alzheimer's Disease at a Single-Cell Resolution
可解释的深度学习方法以单细胞分辨率研究阿尔茨海默病的遗传学和表观遗传学
基本信息
- 批准号:10698166
- 负责人:
- 金额:$ 63.43万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2022
- 资助国家:美国
- 起止时间:2022-08-30 至 2027-07-31
- 项目状态:未结题
- 来源:
- 关键词:3-DimensionalAccelerationAddressAdultAffectAgeAlzheimer&aposs DiseaseAlzheimer&aposs disease related dementiaAlzheimer&aposs disease riskAutopsyBenchmarkingBiological AssayBoundary ElementsBrainBrain regionCellsChromatinClustered Regularly Interspaced Short Palindromic RepeatsCodeCollaborationsCommunitiesComplexComputer ModelsComputer softwareConfusionDataDimensionsDiseaseDropoutElderlyEpigenetic ProcessEtiologyEventExcisionFamilyGenderGene Expression RegulationGenesGeneticGenomeGenome engineeringGenomicsGenotypeGraphHealth Care CostsHeterogeneityHi-CHybridsImpaired cognitionIndividualKnowledgeLearningLifeLinkMemory LossMethodsModalityModelingMolecularMultiomic DataNeurosciencesPatientsPatternPersonal SatisfactionPhenotypePrevention strategyRegulator GenesRegulatory ElementReportingResearchResolutionRisk FactorsSamplingSchemeSeriesSoftware ToolsSpecificitySystemTranscriptional RegulationVariantcell typecognitive abilitycomputer sciencedeep learningdeep learning modeldifferential expressioneffective therapyepigenomeepigenomicsflexibilityfunctional genomicsgenetic variantgenome annotationgenome wide association studygenomic datahigh dimensionalityinfancylearning strategymembermultimodal datamultimodalitymultiple omicsneuralnovelopen sourcepredictive modelingreconstructionrisk variantsequencing platformsingle cell sequencingsupervised learningtooltranscription factortranscriptometranscriptomicstreatment strategy
项目摘要
Alzheimer's disease and related dementias (ADRDs) are complex multifactorial disorders characterized
by progressive memory loss, confusion, and impaired cognitive abilities in older adults. In addition to
genetic variants, studies have reported that certain epigenetic, network, and genome organizational
perturbations, and their complex interplay, contribute to ADRD progression, informing new cellular
etiologies. The recent single-cell revolution, especially multimodal genomic profiling, makes it possible
to scrutinize multi-scale dysregulations in ADRDs at the finest possible resolution. However, few
methods have been developed to address this critical yet challenging task due to the high missingness,
dimensionality, and complex feature interactions in single-cell data. In this project, we will develop
interpretable deep learning methods and software tools to highlight multi-scale dysregulations
contributing to ADRDs, including genetic, epigenetic, network, and chromatin structural alterations at
a single-cell resolution.
Distinct from previous efforts reporting a set of one-dimensional (1D) functional cis-regulatory
elements (CREs) from only one genome and applying it to all samples, we aim to construct personal,
compact, gene-centric, and cell-type-specific brain regulome from sc-multiome data. Specifically,
we will first propose a scalable multimodal deep generative model to integrate large-scale,
heterogeneous ADRD single-cell data with single-, multi-, and hybrid modalities. Distinct to existing
methods, we will include an invariant representation learning scheme to derive latent cell
representations uncorrelated with confounding factors (e.g., age, gender, read depth, and batch effects)
for bias-free transcriptome and epigenome reconstruction (Aim 1). Then, we will go beyond the 1D
genome annotation by deciphering the multi-scale gene regulation code (Aim 2), including cell-type-
specific chromatin compartmentation, CREs and their target genes for functional interpretation, and
transcription factor (TF) regulatory networks (TRNs). Lastly, we will develop interpretable deep learning
models to link multi-scale dysregulations to ADRD with mechanistic explanation (Aim 3).
This proposal is built on an existing multi-year collaboration among the Zhang, Won, and
Gerstein labs that originated from the ENCODE and PsychENCODE projects, with diverse expertise in
computer science, neuroscience, and genomics. Upon completion, our proposal will significantly
accelerate research in a broader scientific community by providing essential tools to investigate
functional regions in the genome and prioritize multi-scale risk factors for ADRD.
阿尔茨海默病及相关痴呆(ADRDs)是一种复杂的多因素疾病
项目成果
期刊论文数量(0)
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科研奖励数量(0)
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JING ZHANG其他文献
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{{ truncateString('JING ZHANG', 18)}}的其他基金
Interpretable Deep Learning Methods to Investigate Genetics and Epigenetics of Alzheimer's Disease at a Single-Cell Resolution
可解释的深度学习方法以单细胞分辨率研究阿尔茨海默病的遗传学和表观遗传学
- 批准号:
10515457 - 财政年份:2022
- 资助金额:
$ 63.43万 - 项目类别:
A big data approach to explore epigenetic heterogeneity and interpret noncoding variants for psychiatric disorders
探索表观遗传异质性并解释精神疾病非编码变异的大数据方法
- 批准号:
10431884 - 财政年份:2020
- 资助金额:
$ 63.43万 - 项目类别:
A big data approach to explore epigenetic heterogeneity and interpret noncoding variants for psychiatric disorders
探索表观遗传异质性并解释精神疾病非编码变异的大数据方法
- 批准号:
10219797 - 财政年份:2020
- 资助金额:
$ 63.43万 - 项目类别:
A big data approach to explore epigenetic heterogeneity and interpret noncoding variants for psychiatric disorders
探索表观遗传异质性并解释精神疾病非编码变异的大数据方法
- 批准号:
10640918 - 财政年份:2020
- 资助金额:
$ 63.43万 - 项目类别:
A big data approach to explore epigenetic heterogeneity and interpret noncoding variants for psychiatric disorders
探索表观遗传异质性并解释精神疾病非编码变异的大数据方法
- 批准号:
10039384 - 财政年份:2020
- 资助金额:
$ 63.43万 - 项目类别:
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