GSE133642

syn77078874

Created By Aditya Nath aditya.nath

doi: 10.1158/0008-5472.CAN-20-0354
title: State-Transition Analysis of Time-Sequential Gene Expression Identifies Critical Points That Predict Leukemia Development
funder: NIH-NCI
series:
creator: Rockne Russell Branciamore Sergio Qi Jing Frankhouser David O’Meally Denis Hua Wei-Kai Cook3 Guerry Carnahan Emily Zhang Lianjun Marom Ayelet Wu Herman Maestrini Davide Wu Xiwei Yuan Yate-Ching Liu Zheng Wang Leo Forman Stephen Carlesso Nadia Kuo Ya-Huei Marcucci Guido
license: CC-BY 4.0
species: Mus musculus
studyId: syn22115279
subject: C57BL/6
ageGroup:
citation: Rockne RC et al. (2020). State-Transition Analysis of Time-Sequential Gene Expression Identifies Critical Points That Predict Development of Acute Myeloid Leukemia. Cancer Res. 80(15):3157-3169. doi: 10.1158/0008-5472.CAN-20-0354
dataType: gene expression
keywords: PBMC C57BL/6
accessType: Open Access
contributor:
description: Temporal dynamics of gene expression are informative of changes associated with disease development and evolution. Given the complexity of high-dimensionaltemporal datasets, an analytical framework guided by a robust theory is needed to interpret time-sequential changes and to predict system dynamics. Herein, we use acute myeloid leukemia as a proof-of-principle to model gene expression dynamics in a transcriptome state-space constructed based on time-sequential RNA-sequencing data. We descri...
grantNumber: CA250046
diseaseFocus:
downloadType: Synapse Hosted
alternateName: GSE133642
manifestation:
specimenCount: 248
yearProcessed: 2019
countryOfOrigin:
individualCount: 248
visualizeDataOn:
dataUseModifiers:
conditionsOfAccess: No restriction
measurementTechnique: RNA-seq
externalRepositoryUri: geo:GSE133642
includedInDataCatalog:

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