grant

Machine learning methods for interpreting spatial multi-omics data

Organization COLUMBIA UNIV NEW YORK MORNINGSIDELocation NEW YORK, UNITED STATESPosted 17 Feb 2023Deadline 31 Jan 2028
NIHUS FederalResearch GrantFY2025ATAC sequencingATAC-seqATACseqAddressAreaArtifactsAssay for Transposase-Accessible Chromatin using sequencingBayesian learningBayesian machine learningBody TissuesBrainBrain Nervous SystemBreast CancerCell BodyCell CommunicationCell InteractionCell-to-Cell InteractionCellsChromatinCollaborationsCommunitiesComplexComputational toolkitComputer ModelsComputer softwareComputerized ModelsComputing MethodologiesDNA AlterationDNA Sequence AlterationDataData AnalysesData AnalysisData SetDependenceDevelopmentDiseaseDisease ProgressionDisorderDissociationDropoutEmbryoEmbryo DevelopmentEmbryogenesisEmbryonicEmbryonic DevelopmentEncephalonEnvironmentExpression SignatureFoundationsFutureGene ExpressionGene Expression ProfileGenesGenetic AlterationGenomeGenomic SegmentGenomicsGlioblastomaGoalsGrade IV Astrocytic NeoplasmGrade IV Astrocytic TumorGrade IV AstrocytomaHistologyHumanImageIn SituInterpretable MLInterpretable machine learningJointsKnowledgeLocationMalignant Breast NeoplasmMapsMeasurementMessenger RNAMethodsMiceMice MammalsModelingModern ManMolecularMorphologic artifactsMultiomic DataMurineMusNeighborhoodsNoiseNormal RangeNormal ValuesOrganoidsOutcomePatternPhysiologyProbabilistic ModelsProbability ModelsProteinsProteomicsRNA SeqRNA sequencingRNAseqRegulationResearchResolutionRoleSequence AlterationSoftwareSpatial DistributionStatistical ModelsSystemTechniquesTechnologyTissue imagingTissuesTonsilWorkanalytical toolassay for transposase accessible chromatin followed by sequencingassay for transposase accessible chromatin seqassay for transposase accessible chromatin sequencingassay for transposase-accessible chromatin with sequencingbiological systemsbrain tissuecell typecomputational frameworkcomputational methodologycomputational methodscomputational modelingcomputational modelscomputational toolboxcomputational toolscomputational toolsetcomputer based methodcomputer based modelscomputer frameworkcomputer methodscomputerized modelingcomputerized toolscomputing methodcopy number alterationdata integrationdata interpretationdata modalitiesdesigndesigningdevelopmentalepigenomicsexperienceexplainable MLexplainable machine learningflexibilityflexiblefunctional genomicsgene expression patterngene expression signaturegene manipulationgene regulatory networkgenerative modelsgenetic manipulationgenetically manipulategenetically perturbgenome segmentgenomic alterationgenomic datagenomic datasetgenomic regionglioblastoma multiformehigh dimensional datahigh dimensionalityhistologic imagehistological imageimaginginnovateinnovationinnovativeinsightmRNAmachine learning based frameworkmachine learning based methodmachine learning based modelmachine learning frameworkmachine learning methodmachine learning methodologiesmachine learning modelmalignant breast tumormulti-modal datamulti-modal datasetsmultidimensional datamultidimensional datasetsmultimodal datamultimodal datasetsmultiple omic dataneuropsychiatric diseaseneuropsychiatric disordernovelopen sourceprogramsresolutionssocial rolespatial RNA sequencingspatial gene expression analysisspatial gene expression profilingspatial integrationspatial multiomicsspatial omicsspatial resolved transcriptome sequencingspatial transcriptome analysisspatial transcriptome profilingspatial transcriptome sequencingspatial transcriptomicsspatial tri-omicsspatially resolved transcriptomicsspatio transcriptomicsspongioblastoma multiformestatistical linear mixed modelsstatistical linear modelssupervised learningsupervised machine learningtonsillartooltranscriptional profiletranscriptional signaturetranscriptome sequencingtranscriptomic sequencingtranscriptomicsuser friendly computer softwareuser friendly software
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PROJECT SUMMARY
The proposed research program aims to develop innovative computational tools for the analysis and

integration of data from emerging spatially-resolved genomic technologies, which have the potential to uncover

the role of interactions with the environment in normal development and disease. Existing analytical tools for

analyzing…

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Machine learning methods for interpreting spatial multi-omics data — COLUMBIA UNIV NEW YORK MORNINGSIDE | UNITED STATES | Dev Procure