GEO DataSets

(Submitter supplied) Diabetic kidney disease is the leading cause of end-stage kidney disease worldwide; however, the integration of high-dimensional trans-omics data to predict this diabetic complication is rare. We develop artificial intelligence (AI)-assisted models using machine learning algorithms to identify a biomarker signature that predisposes high risk patients with diabetes mellitus (DM) to diabetic kidney disease based on clinical information, untargeted metabolomics, targeted lipidomics and genome-wide single nucleotide polymorphism (SNP) datasets. more...

Organism:

Homo sapiens

Type:

Genome variation profiling by array; SNP genotyping by SNP array

Platform:

GPL32738

616 Samples

Download data: CEL, TXT

(Submitter supplied) ##For information about the Annotation file content, please see the bundled README file. #%create_date=2018-05-16 GMT-08:00 11:03:39 #%chip_type=Axiom_TWB_2 #%genome-species=Homo sapiens #%genome-version=hg38 #%genome-version-ucsc=hg38 #%genome-version-ncbi=38 #%genome-version-create_date=2013-12-01 #%mitochondrion-version=NC_012920 #%dbSNP-date=2016-04-14 #%dbSNP-version=150 #%hapmap-date=2008-01-08 #%hapmap-version=23 #%omim-date=2016-07-14 #%allele_frequency_data_reference=1KGp3_2014 and Affymetrix_internal_screen #%netaffx-annotation-date=2018-05-08 #%netaffx-annotation-netaffx-build=36 #%netaffx-annotation-netaffx-revision=r2.a2 #%netaffx-annotation-tabular-format-version=1.5 This database/product contains information from the Online Mendelian Inheritance in Man(R) (OMIM(R)) database, which has been obtained under a license from the Johns Hopkins University. more...

Organism:

Homo sapiens

2 Series

1314 Samples

Download data: CDF, ZIP