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Items: 3

1.

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

(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:
Expression profiling by high throughput sequencing
Platform:
GPL18573
38 Samples
Download data: TXT
Series
Accession:
GSE134056
ID:
200134056

PubMed Full text in PMC Similar studies Analyze with GEO2RSRA Run Selector

2.

Illumina NextSeq 500 (Homo sapiens)

Organism:
Homo sapiens
12323 Series
337323 Samples
Download data
Platform
Accession:
GPL18573
ID:
100018573

3.

disease rep2 [W_006]

Organism:
Homo sapiens
Source name:
disease_endometrial tissue
Platform:
GPL18573
Series:
GSE134056
Download data
Sample
Accession:
GSM3935004
ID:
303935004

SRA Run Selector

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db=gds|term=GSM3935004[Accession]|query=6|qty=2|blobid=MCID_66cb9266ba59ee397ad3e4c7|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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