Drug Repurposing for Cancer Therapy: From Man to Molecules to Man
Drug Repurposing for Cancer Therapy: From Man to Molecules to Man
批准号:
9337383
负责人:
Alexander Tropsha
金额:
$41.71万
依托单位国家:
美国
项目类别:
财政年份:
2016
资助国家:
美国
项目状态:
已结题
起止时间:
2016-09-01 至 2019-08-31
关键词:
AddressAdverse drug effectAnimal Cancer ModelBig DataBiologicalBiological AssayBiological ModelsCancer PatientCellsChemicalsClinical TrialsCognitiveCollectionComputer SimulationDataData SetDatabasesDiseaseDockingDrug ModelingsDrug TargetingElectronic Health RecordGenerationsGoalsHumanIn VitroInternetKnowledge DiscoveryLabelLaboratoriesLanguageLinkLiteratureMalignant Childhood NeoplasmMalignant NeoplasmsMedicalMetadataMethodsMiningModelingMolecularMolecular ModelsNamesPaperPathway interactionsPatientsPharmaceutical PreparationsPhase II Clinical TrialsPhysiciansPubChemPubMedPublicationsPublishingQuantitative Structure-Activity RelationshipReportingResearchResearch Project GrantsSemanticsSourceStreamStructureTechnologyTextTherapeutic EffectTranslationsValidationbasecancer therapycomputer based Semantic Analysiscomputer frameworkcomputer studiescomputerized toolsdrug candidategenomic datahealth recordhigh rewardhuman datain vivomannew therapeutic targetnovelnovel therapeuticspre-clinicalrepositoryscreeningsmall molecule librariessocial mediatext searchingtoolvirtual
中文摘要
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英文摘要
Abstract
This project aims to develop and implement multiple but integrated computational tools for drug
repurposing by exploiting complimentary big data streams, i.e., unstructured texts (social media
networks, published biomedical literature, and electronic health records), electronic databases
of chemical-biological interactions and pathways, and laboratory data (biological screening of
chemical libraries). We expect that tools to be developed in this project will be useful for
repurposing observational textual data for research projects (addressing the second challenge
of the underlying RFA). In addition, the envisioned translation of this data into a format
amenable to quantitative modeling of drug effects will also enable integration of textual and
laboratory data to create minable metadata (cf. the third challenge).
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