Genetics of deep-learning-derived neuroimaging endophenotypes for Alzheimer's Disease
Genetics of deep-learning-derived neuroimaging endophenotypes for Alzheimer's Disease
批准号:
10660742
负责人:
Laila Rasmy Gindy Bekhet
金额:
$11.7万
依托单位国家:
美国
项目类别:
财政年份:
2021
资助国家:
美国
项目状态:
未结题
起止时间:
2021-07-01 至 2026-06-30
关键词:
2019-nCoVAcuteAdmission activityAll of Us Research ProgramAlzheimer&aposs DiseaseAlzheimer&aposs disease related dementiaAwardBig DataBrainBrain imagingCOVID-19COVID-19 complicationsCOVID-19 patientCOVID-19 survivorsCategoriesCerebrospinal FluidClassificationClinicalClinical DataCognitiveCommunitiesDataElectronic Health RecordEncephalopathiesEventGeneticGenetic studyHeadacheHealthKnowledgeLabelLong COVIDMedical GeneticsModelingNeural Network SimulationNeurologicNeurologic SymptomsOutcomeParentsParticipantPathogenesisPatientsPatternPhasePhenotypePhysiciansPost-Acute Sequelae of SARS-CoV-2 InfectionPreventionReportingResearchResearch PersonnelResourcesRiskStructureTimeTrainingUnited StatesUnited States National Institutes of HealthVirusWorkbasebiobankbrain tissueclinical phenotypecohortcommon symptomcoronavirus diseasecytokinedeep learningdeep learning modelendophenotypeexperiencegenetic associationgenetic informationgenome wide association studyhealth dataindexinginsightneuroimagingparent grantpost COVID-19 complicationspost-COVID-19predict clinical outcomepredictive modelingpreventpublic health relevancerecurrent neural networksupervised learning
中文摘要
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英文摘要
Abstract
There is still a lack of knowledge on the key genetic factors associated with post acute syndrome for COVID-19
patients, especially those related to neurological complications. In this study, we will utilize both the genetic
and clinical data available through the All of Us researchers platform to study the genetic association with
COVID-19 complications. In order to more accurately phenotype the patients based on their clinical trajectory
mostly recorded in their electronic health records, we will utilize a pretrained deep learning model trained on
more than four million patients from the N3C cohort. The pretrained model will be further fine-tuned on the All
of US data, and will be used to phenotype the patients with genetic data. Further GWAS study will be
performed to correlate between the deep learning based phenotype and the genetic information. Successful
completion of this project will bring new insights to guide COVID-19 patients treatment to better prevent or
manage further complications.
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Clinical foundation model for structured clinical data
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批准号:10639397
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项目类别:
-
资助金额:$35.1万
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财政年份:2023
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负责人:Laila Rasmy Gindy Bekhet
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依托单位:
海外基金