Kinetic Phenotype Discovery Informatics for Neurological Diseases
Kinetic Phenotype Discovery Informatics for Neurological Diseases
批准号:
9769172
负责人:
Shih-Jong J Lee
金额:
$60.19万
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 2020-11-16
关键词:
AddressAdoptedAdoptionAffectAge of OnsetAreaBedsBiological ModelsBiological ProductsCell modelCell physiologyCellsCharacteristicsClinicalCollaborationsColorComplementDataDiagnosisDiagnosticDiseaseDrug IndustryEvaluationEventFailureFluorescent ProbesGene Expression ProfilingGenomicsGovernmentHealthcareImageIn VitroIncidenceInformaticsInvestigationKineticsMethodsMicroscopyMorphologyNeuronsPathogenicityPatient RepresentativePatientsPatternPersonsPharmaceutical PreparationsPhasePhenotypeProcessRNAReporterResearchResolutionSamplingScientistSensitivity and SpecificityStratificationSystemTechnologyTestingTherapeuticTimeTranslational ResearchUpdateValidationanalytical toolbaseblindcellular imagingdisease phenotypeimaging studyinnovationinstrumentinterestmicroscopic imagingnervous system disordernext generationpatient populationpatient variabilityprototypequantumtherapy developmenttime usetool
中文摘要
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英文摘要
Project Summary
Neurological disorders significantly outnumber diseases in other therapeutic areas and are growing in
incidence faster than any other disease classes. However, the pharmaceutical industry has been unsuccessful in
coming up with effective drugs. A big factor in these failures has been a lack of adequate model systems for
fundamental disease understanding affecting both diagnosis and treatment. There is therefore a strong,
emerging interest in the use of patient-derived cell models to understand the pathogenic mechanisms
underlying neurological disease phenotypes. To gain a true understanding of these mechanisms and
phenotypes, it is necessary to analyze dynamic events using time-lapse microscopy. These tools complement
RNA profiling studies by enabling single-cell resolution of pathogenic processes at high-throughput, enabling
investigation of highly diverse or largely replicative patient sets at an unprecedented scale. To discover
predictive disease phenotypes across a large representative patient sample, a systematic, unbiased approach is
needed to mine time-lapse microscopy image sequences, patient clinical and concomitant data. There is
therefore a critical need for a next-generation analytical tool to enable the discovery of disease predictive
phenotypes robust to patient variations.
Thus, we propose to develop a teachable kinetic informatics discovery (KID) tool based on a
hierarchical inference framework. If proven, the KID tool would be rapidly adopted for translational research
using patient-derived cell models in many diseases. It could facilitate a paradigm shift towards broad adoption
of patient-cell models for therapeutics discovery, optimization, stratification and diagnostic discovery.
Our immediate objective for this Fast-Track project is to develop and validate the KID tool by showing
that it can classify patients on the basis of disease and disease characteristics such as age-of-onset. In Phase I
we will develop the prototype KID tool and preliminary patient panel. We will prove feasibility by discovering
phenotypes and then accurately scoring patients based on the phenotypes in blind tests. In Phase II we will
develop the full patient panel and pre-product KID tool. We will validate by scoring patients in blind tests and
validate phenotypes through corroboration with targeted transcriptional profiling and genomic tests. We will
reduce the phenotypes to a single time point and show efficacy in a targeted compound screen.
The Specific Aims are: Phase 1, (Aim 1): Develop the prototype KID tool and preliminary patient panel
incorporating neuronal firing reporter. (Aim 2): Verify the prototype KID tool in a preliminary patient panel.
Phase II, (Aim 1): Complete the beta prototype KID tool and patient panel for KID tool validation. (Aim 2):
Disease phenotype discovery and verification in the full patient panel. (Aim 3): Disease phenotype validation.
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批准号:8697110
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财政年份:2011
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依托单位:
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批准号:8058635
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财政年份:2011
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资助金额:$98.35万
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财政年份:2011
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依托单位:
Analysis of tracking based phenotypes in CNS
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批准号:8119649
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资助金额:$45.68万
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财政年份:2006
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负责人:Shih-Jong J Lee
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依托单位:
Robust Characterization of Moving Objects for Subcellular Time-lapse Assays
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资助金额:$10.0万
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依托单位:
Individual Cell Motility Image Informatics
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批准号:7053106
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资助金额:$10.0万
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财政年份:2006
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依托单位:
Analysis of tracking based phenotypes in CNS
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批准号:7914170
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项目类别:
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资助金额:$49.1万
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财政年份:2006
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依托单位:
Phase II: Robust analysis of subcellular time-lapse assays
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批准号:7477872
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项目类别:
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资助金额:$35.47万
-
财政年份:2005
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依托单位:
Phase II: Robust analysis of subcellular time-lapse assays
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批准号:7326926
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项目类别:
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资助金额:$39.52万
-
财政年份:2005
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依托单位:
Robust Analysis of Subcellular Time-lapse Assays
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项目类别:
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资助金额:$10.0万
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依托单位:
海外基金