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NHLBI Big Data Analysis Heart Failure Challenge Data Collection

ABSTRACT: NHLBI Big Data Analysis Heart Failure Challenge Data Collection

PROVIDER: PRJNA610994 | ENA |

REPOSITORIES: ENA

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NHLBI Big Data Analysis Heart Failure Challenge Data Collection

Project description:NHLBI Big Data Analysis Heart Failure Challenge Data Collection

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Expression data from BIG LEAF

Project description:We study differences in gene expression between Populus P35S::BL (BL-oe) lines and control, affecting plant growth and differentiation, and dormancy. We used microarrays to detail the global program of gene expression underlying morphological and developmental changes droved by overexpression of BL gene. We identified an activation tagging mutant with increased leaf size and correspondingly name it BIG LEAF (BL). We positioned the tag, localized a putative candidate gene and verified transcription activation. The activated gene encodes a WD40 putative transcription regulator similar to the Arabidopsis thaliana STERILE APETALA (SAP). We recapitulated the phenotype by overexpression of the gene into the same genotype under strong constitutive promoter (P35S::BL, BL-oe). Transgenic up-regulation of the BL gene caused enhanced leaf size, early bud-break, and suppression of secondary growth. BL transcript abundance in wild type plants is in apical tissues, mostly in shoot meristem, leaf primordia and axillary meristem. Our data indicates that BL plays an important role in the process of tree growth.

2017-05-11 | GSE68859 | GEO

Expression data from BIG LEAF

2017-05-11 | E-GEOD-68859 | biostudies-arrayexpress

Small Sample-Big Data: Integrative Indexed Systems Biology Reveals Dramatic Molecular Ontogeny over the First Week of Human Life

Project description:Systems biology provides a powerful approach to unravel complex biological processes yet it has not been applied systematically to samples from newborns, a group highly vulnerable to a wide range of diseases. Published methods rely on blood volumes that are not feasible to obtain from newborns. We optimized methods to extract transcriptomic, proteomic, metabolomic, cytokine/chemokine, and single cell immune phenotyping data from <1ml of blood, a volume readily obtained from newborns. Furthermore, indexing to baseline and applying innovative integrative computational methods that address the challenge of few data points with many features enabled identification of robust findings within a readily achievable sample size. This approach uncovered dramatic changes along a stable developmental trajectory over the first week of life. The ability to extract information from ‘big data’ and draw key insights from such small sample volumes will enable and accelerate characterization of the molecular ontogeny driving this crucial developmental period.

2019-03-20 | GSE111404 | GEO

Construction of an intelligent system for prevention and treatment of colorectal cancer basing on big data analysis and deep learning model

Project description:Interventions: Gold Standard:Pathological diagnosis;Index test:Intelligent prevention and treatment system for colorectal cancer based on big data analysis and deep learning model. Primary outcome(s): Intestinal flora;SEN, SPE, ACC, AUC of ROC Study Design: Factorial

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Project description:NHLBI / ODSS Pilot GRU (GSR Sharing Allowed) Collection

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NHLBI / ODSS Pilot GRU (GSR Sharing Allowed) Collection

Project description:NHLBI / ODSS Pilot GRU (GSR Sharing Allowed) Collection

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