Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

A Machine Learning Classifier for Assigning Individual Patients with Systemic Sclerosis to Intrinsic Molecular Subsets

ABSTRACT: A Machine Learning Classifier for Assigning Individual Patients with Systemic Sclerosis to Intrinsic Molecular Subsets

PROVIDER: PRJNA515920 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Similar Datasets

Prediction of Breast Cancer Estrogen Receptor Status using Machine Learning

Project description:Gene expression profiles were generated from 199 primary breast cancer patients. Samples 1-176 were used in another study, GEO Series GSE22820, and form the training data set in this study. Sample numbers 200-222 form a validation set. This data is used to model a machine learning classifier for Estrogen Receptor Status. RNA was isolated from 199 primary breast cancer patients. A machine learning classifier was built to predict ER status using only three gene features.

2013-01-01 | E-GEOD-29210 | biostudies-arrayexpress

A Machine Learning Classifier for Assigning Individual Patients with Systemic Sclerosis to Intrinsic Molecular Subsets

Project description:DNA microarray and RNA-seq were performed on samples from four controls and eight SSc patients for testing the performance of intrinsic subset classification in two different gene expression profiling platforms. N0901, N0903, N1002, N1003, SSc0882, SSc0916, SSc0918, S0920 have previously been deposited on NCBI SRA at PRJNA237826. The remaining four SSc RNA-seq samples will be available at PRJNAXXXXXX.

2019-01-19 | GSE125362 | GEO

Prediction of Breast Cancer Estrogen Receptor Status using Machine Learning

2013-01-01 | GSE29210 | GEO

A Machine Learning Classifier for Assigning Individual Patients With Systemic Sclerosis to Intrinsic Molecular Subsets.

Project description: Not available

| S-EPMC6764877 | biostudies-literature

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

Project description: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.

2019-07-18 | GSE134056 | GEO

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

2019-07-18 | GSE134052 | GEO

DNA-Methylome based Tumor Hypoxia Classifier Identifies HPV-negative Head and Neck Cancer Patients at Risk for Locoregional Recurrence After Primary Radiochemotherapy

Project description:We used machine-learning algorithms to identify a hypoxia-associated methylation signature in patients with HPV negative HNSCC in the TCGA-HNSCC cohort. This current submission forms the basis of the independent validation cohort used to test the Hypoxia-M classifier in our study.

2023-04-05 | E-MTAB-12431 | biostudies-arrayexpress

A machine learning classifier trained on cancer transcriptomes detects NF1 inactivation signal in glioblastoma

Project description:Analysis of gene expression in pathologically confirmed glioblastoma (GBM) samples. These data were used to test a classifier that was generated to distinguish GBM tumor samples with loss of neurofibromin 1 (NF1) function

2017-02-01 | GSE85033 | GEO

Machine learning for discovery: deciphering RNA splicing logic

Project description:Machine learning methods, particularly neural networks trained on large datasets, are transforming how scientists approach scientific discovery and experimental design. However, current state-of-the-art neural networks are limited by their uninterpretability: despite providing accurate predictions, they cannot describe how they arrived at their predictions. Here, using an ``interpretable-by-design'' approach, we present a neural network model that provides insights into RNA splicing, a fundamental process in the transfer of genomic information into functional biochemical products. Although we designed our model to emphasize interpretability, its predictive accuracy is on par with state-of-the-art models. To demonstrate the model's interpretability, we introduce a visualization that, for any given exon, allows us to trace and quantify the entire decision process from input sequence to output splicing prediction. Importantly, the model revealed novel components of the splicing logic, which we experimentally validated. This study highlights how interpretable machine learning can advance scientific discovery.

2022-10-01 | GSE200096 | GEO

An unbiased machine learning exploration reveals gene sets predictive of allograft tolerance after kidney transplantation

Project description:Efforts at finding potential biomarkers of tolerance after kidney transplantation have been hindered by limited sample size, as well as the complicated mechanisms underlying tolerance and the potential risk of rejection after immunosuppressant withdrawal. In this work, three different publicly available genome-wide expression data sets of peripheral blood lymphocyte (PBL) from 63 tolerant patients were used to compare 14 different machine learning models for their ability to predict spontaneous kidney graft tolerance. We found that the Best Subset Selection (BSS) regression approach was the most powerful with a sensitivity of 91.7% and a specificity of 93.8% in the test group, and a specificity of 86.1% and a sensitivity of 80% in the validation group. A feature set with five genes (HLA-DOA, TCL1A, EBF1, CD79B, and PNOC) was identified using the BSS model. EBF1 downregulation was also an independent factor predictive of graft rejection and graft loss. An AUC value of 84.4% was achieved using the two-gene signature (EBF1 and HLA-DOA) as an input to our classifier. Overall, our systematic machine learning exploration suggests novel biological targets that might affect tolerance to renal allografts, and provides clinical insights that can potentially guide patient selection for immunosuppressant withdrawal.

2021-06-04 | GSE166865 | GEO

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data