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Items: 3

1.

Machine Learning Classifiers for Endometriosis Using Transcriptomics and Methylomics Data [Methylomics]

(Submitter supplied) We experimented how well various supervised machine learning methods such as decision tree, partial least squares discriminant analysis (PLSDA), support vector machine and random forest perform in classifying endometriosis from the control samples trained on both transcriptomics and methylomics data. The assessment was done from two different perspectives for improving classification performances: (a) implication of three different normalization techniques, and (b) implication of differential analysis using the generalized linear model (GLM). more...
Organism:
Homo sapiens
Type:
Methylation profiling by high throughput sequencing
Platform:
GPL16791
77 Samples
Download data: TXT
Series
Accession:
GSE134052
ID:
200134052
2.

Illumina HiSeq 2500 (Homo sapiens)

Platform
Accession:
GPL16791
ID:
100016791
3.

disease rep33

Organism:
Homo sapiens
Source name:
endometrial tissue
Platform:
GPL16791
Series:
GSE134052
Download data
Sample
Accession:
GSM3934801
ID:
303934801
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db=gds|term=GSM3934801[Accession]|query=9|qty=2|blobid=MCID_67287fe4895d6b68cf7d68e5|ismultiple=true|min_list=5|max_list=20|def_tree=20|def_list=|def_view=|url=/Taxonomy/backend/subset.cgi?|trace_url=/stat?
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