Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Machine learning analysis of the T cell receptor repertoire identifies sequence features that predict self-reactivity

ABSTRACT: Machine learning analysis of the T cell receptor repertoire identifies sequence features that predict self-reactivity

PROVIDER: PRJNA915397 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	SRR22882245_1.fastq.gz	Fastqsanger.gz
	SRR22882245_2.fastq.gz	Fastqsanger.gz
	SRR22882246_1.fastq.gz	Fastqsanger.gz
	SRR22882246_2.fastq.gz	Fastqsanger.gz
	SRR22882247_1.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 40

Similar Datasets

Machine learning analysis of the T cell receptor repertoire identifies sequence features that predict self-reactivity

Project description:The T cell receptor (TCR) determines the specificity and affinity for both foreign and self-peptides presented by MHC. It is established that self-pMHC reactivity impacts T cell function, but it has been challenging to identify TCR sequence features that predict T cell fate. To discern patterns distinguishing TCRs from naïve CD4+ T cells with low versus high self-pMHC reactivity, we used data from 42 mice to train a machine learning (ML) algorithm that predicts self-reactivity directly from TCRβ sequences. This approach revealed that n-nucleotide additions and acidic amino acids weaken selfreactivity. We tested our ML predictions of TCRβ sequence self-reactivity using retrogenic mice. Extrapolating our analyses to independent datasets, we found high predicted self-reactivity for regulatory CD4+ T cells and low predicted self-reactivity for T cells responding to chronic infection. Our analyses suggest a potential trade-off between repertoire diversity and self-reactivity intrinsic to the architecture of a TCR repertoire.

2023-01-09 | GSE221703 | GEO

Machine Learning Identifies Signatures of Macrophage Reactivity and Tolerance

Project description:Machine Learning Identifies Signatures of Macrophage Reactivity and Tolerance

| PRJNA839943 | ENA

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

Machine Learning Identifies Signatures of Macrophage Reactivity and Tolerance

Project description:A continuum of macrophage polarization states is essential tissue homeostasis. We used machine learning approaches to identify universally relevant definition of macrophage polarization states and create a predictive framework for developing macrophage-targeted precision diagnostics and therapeutics. CCDC88A was identified as a key gene in the continuum state clusters that is essential for the tolerant polarization state.

2022-05-20 | GSE203423 | GEO

Machine Learning Models Predict the Primary Sites of Head and Neck Squamous Cell Carcinoma Metastases Based on DNA Methylation

Project description:In head and neck squamous cell cancers (HNSCs) that present as metastases with an unknown primary (HNSC-CUPs), the identification of a primary tumor improves therapy options and increases patient survival. However, the currently available diagnostic methods are laborious and do not offer a sufficient detection rate. Predictive machine learning models based on DNA methylation profiles have recently emerged as a promising technique for tumor classification. We applied this technique to HNSC to develop a tool that can improve the diagnostic workup for HNSC-CUPs. On a reference cohort of 405 primary HNSC samples, we developed four classifiers based on different machine learning models (random forest (RF), neural network (NN), elastic net penalized logistic regression (LOGREG), support vector machine (SVM)) that predict the primary site of HNSC tumors from their DNA methylation profile. The classifiers achieved high classification accuracies (RF=83%, NN=88%, LOGREG=SVM=89%) on an independent cohort of 64 HNSC metastases. Further, the NN, LOGREG, and SVM models significantly outperformed p16 status as a marker for an origin in the oropharynx. In conclusion, the DNA methylation profiles of HNSC metastases are characteristic for their primary sites and the classifiers developed in this study, which are made available to the scientific community, can provide valuable information to guide the diagnostic workup of HNSC-CUP.

2021-12-08 | GSE171994 | GEO

Design of an unbiased machine learning workflow to predict Multiple Sclerosis staging from blood transcriptome

Project description:Design of an unbiased machine learning workflow to predict Multiple Sclerosis staging from blood transcriptome

| PRJNA562515 | ENA

Machine Learning-based Peptide ID Rescoring

Project description:Machine Learning-based Mascot Rescoring to Reduce False Positives in Tumor Immunopeptidomics using Mass Spectrometry

2024-08-25 | PXD044852 | JPOST Repository

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

Multi-omics and Machine Learning Accurately Predicts Clinical Response to Adalimumab and Etanercept Therapy in Patients with Rheumatoid Arthritis [RNA-Seq]

Project description:Objectives: To predict response prior to anti-TNF treatment and comprehensively understand the mechanism how patients respond differently to anti-TNF treatment in rheumatoid arthritis (RA). Methods: Gene expression and/or DNA methylation profiling on PBMC, monocytes, and CD4+ T cells, from 80 RA patients before initiating either adalimumab (ADA) or etanercept (ETN) therapy was studied. After 6-month treatment response was evaluated according to the EULAR criteria of disease response. Differential expression and methylation analyses were performed to identify the response-associated transcriptional and epigenetic signatures. Machine learning models were built using these signatures by random forest algorithm to predict response prior to anti-TNF treatment and were further validated by a follow-up study. Results: Transcriptional signatures in ADA and ETN responders are divergent in PBMCs, and this phenomenon was reproduced in monocytes and CD4+ T cells. The genes upregulated in CD4+ T cells of ADA responders were enriched in the TNF signaling pathway, while very few pathways were differential in monocytes. Differential methylation positions (DMPs) of responders to ETN but not to ADA are majorly hypermethylated. The machine learning models to predict the response to ADA and ETN using differential genes reached overall accuracy of 85.9% and 79%, respectively. The models using DMPs reached overall accuracy of 84.7% and 88% for ADA and ETN, respectively. A follow-up study validated the high performance of these models. Conclusions: Machine learning models based on these molecular signatures could accurately predict response before ADA and ETN treatment, paving the path towards personalized anti-TNF treatment.

2020-08-24 | GSE138746 | GEO

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

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