Integrative genomic framework for dissecting regulatory mechanisms underlying hepatocellular carcinoma
Integrative genomic framework for dissecting regulatory mechanisms underlying hepatocellular carcinoma
批准号:
9296968
负责人:
Nathalie Pochet
金额:
$22.73万
依托单位国家:
美国
项目类别:
财政年份:
2017
资助国家:
美国
项目状态:
已结题
起止时间:
2017-05-12 至 2019-04-30
关键词:
Algorithmic SoftwareAlgorithmsAutomobile DrivingBioconductorBiogenesisBiological ModelsBiologyCancer BiologyCancer EtiologyCause of DeathCellsChronicChronic Hepatitis BCirrhosisCommunitiesComputer AnalysisDataDiseaseDisease ProgressionEtiologyFutureGene ExpressionGenesGenetic TranscriptionGenomic approachGenomicsGoalsHepatitis BHepatitis CHepatocarcinogenesisHepatocyteInfectionInflammationLaboratoriesLiverLiver diseasesMalignant NeoplasmsMalignant neoplasm of liverMeasuresModelingPathogenesisPathway interactionsPatientsPreventionPrimary carcinoma of the liver cellsProteomeProteomicsResearch PersonnelRiskRisk FactorsSystemTechnologyTimeTissuesVirusVirus Diseasesbasecancer preventioncancer riskcarcinogenesiscomputer frameworkcomputerized toolsdisorder riskfunctional genomicsgenetic signaturegenome-widehigh riskinsightmodel developmentnovel strategiespathogenpredictive markerprogramsresponsesoftware developmenttooltranscriptometranscriptome sequencinguser-friendlyviral carcinogenesis
中文摘要
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英文摘要
Project Summary
Chronic viral infections are major causes of chronic inflammation and cancer. A well characterized example
is virus-induced liver disease and hepatocellular carcinoma, the second leading cause of death world-wide.
Using virus-induced liver disease and cancer as a model, we aim to develop sophisticated computational
tools to investigate the host cell reprogramming induced by viruses during cancerogenesis. Leveraging and
enhancing advanced computational approaches, we will develop an integrative genomic framework to
identify the regulatory modules driving transcriptional and proteomic reprogramming associated with
persistent viral infection and carcinogenesis. More specifically, we will develop a toolbox that allows for
inferring regulatory mechanisms from time course data derived from different viral infections associated
with similar disease biology and multiple functional genomics levels in an integrated manner. We
hypothesize that the computational toolbox developed within this program will contribute to understand viral
carcinogenesis and identify novel strategies for cancer prevention.
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