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| Status |
Public on Jul 18, 2019 |
| Title |
Machine Learning Classifiers for Endometriosis Using Transcriptomics and Methylomics Data [Transcriptomics] |
| Organism |
Homo sapiens |
| Experiment type |
Expression profiling by high throughput sequencing
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| Summary |
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). We concluded that an appropriate machine learning diagnostic pipeline for endometriosis should use TMM normalization for transcriptomics data, and quantile or voom normalization for methylomics data, GLM for feature space reduction and classification performance maximization.
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| Overall design |
Total 38 RNA-seq samples were analyzed where 22 were control and 16 were disease samples
Please note that the GSE134052 records represent the methylomics data.
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| Contributor(s) |
Akter S, Xu D, Nagel SC, Bromfield J, Pelch K, Wilshire GB, Joshi T |
| Citation(s) |
31552087 |
| |
| Submission date |
Jul 09, 2019 |
| Last update date |
Oct 23, 2019 |
| Contact name |
Trupti Joshi |
| E-mail(s) |
joshitr@health.missouri.edu
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| Organization name |
University of Missouri
|
| Street address |
NW 502, One Hospital Drive
|
| City |
Columbia |
| State/province |
Missouri |
| ZIP/Postal code |
65212 |
| Country |
USA |
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| Platforms (1) |
| GPL18573 |
Illumina NextSeq 500 (Homo sapiens) |
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| Samples (38)
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| Relations |
| BioProject |
PRJNA553574 |
| SRA |
SRP213937 |
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