Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Integrative Analysis and Machine Learning based Characterization of Single Circulating Tumor Cells

ABSTRACT: Profiling of 15 circulating tumor cells from three subjects with ER+/PR+/HER- hormone receptor status

ORGANISM(S): Homo sapiens

PROVIDER: GSE129474 | GEO | 2020/04/18

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Integrative Analysis and Machine Learning based Characterization of Single Circulating Tumor Cells

Project description:Integrative Analysis and Machine Learning based Characterization of Single Circulating Tumor Cells

| PRJNA531457 | ENA

Integrative Analysis and Machine Learning based Characterization of Single Circulating Tumor Cells.

Project description:We collated publicly available single-cell expression profiles of circulating tumor cells (CTCs) and showed that CTCs across cancers lie on a near-perfect continuum of epithelial to mesenchymal (EMT) transition. Integrative analysis of CTC transcriptomes also highlighted the inverse gene expression pattern between PD-L1 and MHC, which is implicated in cancer immunotherapy. We used the CTCs expression profiles in tandem with publicly available peripheral blood mononuclear cell (PBMC) transcriptomes to train a classifier that accurately recognizes CTCs of diverse phenotype. Further, we used this classifier to validate circulating breast tumor cells captured using a newly developed microfluidic system for label-free enrichment of CTCs.

| S-EPMC7230872 | biostudies-literature

Circulating miRNAs and Machine Learning for Lateralizing Primary Aldosteronism

Project description:Primary aldosteronism (PA) is the most prevalent cause of secondary hypertension. Its two main clinical forms are unilateral adenoma (UPA) and bilateral hyperplasia (BAH), which require markedly different medical treatments, so differentiating between the two is of utmost clinical importance. The current gold standard method for this is adrenal vein sampling (AVS), the application of which is hindered by limited availability and high skill requirements. Our goal was to identify circulating microRNAs – or their combinations – which enable differentiation between the two most prevalent aetiologies of PA from a peripheral blood sample. MicroRNA specific sequencing was performed on an Illumina platform, using EDTA coagulated blood samples taken during AVS, from 18 patients (10 uni-, and 8 bilateral). First, plasma samples from both adrenal veins were evaluated. Bioinformatical analysis applying the DeSeq2 algorithm was used to evaluate the differences in expression; and a neural network model, tasked to identify the most fit individual and groups of microRNAs for differentiation was used. The microRNAs comprising the five best performing models were then validated using reverse transcription real-time PCR.

2024-10-31 | GSE264578 | GEO

Circulating miRNAs and Machine Learning for Lateralizing Primary Aldosteronism

Project description:Circulating miRNAs and Machine Learning for Lateralizing Primary Aldosteronism

| PRJNA1103178 | 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 Analysis of Circulating Alternative Spliced RNAs Reveal Segregation of Retained Intron Species with New-onset Type 1 Diabetes

Project description:Distinct patterns of RNA splicing differentiated participants with T1D from healthy unaffected controls. Notably, certain splicing events, particularly involving retained introns, showed significant association with T1D. Machine learning analysis using these splicing events as features from the training cohort demonstrated high accuracy in distinguishing between T1D subjects and controls in the validation cohort. Gene Ontology pathway enrichment analysis of the retained intron category showed evidence for a systemic viral response in T1D subjects.

2024-09-18 | GSE277133 | GEO

Machine Learning Identifies Plasma Proteomic Signatures of Descending Thoracic Aortic Disease

Project description:Descending thoracic aortic aneurysms and dissections can go undetected until severe and catastrophic, and few clinical indices exist to screen for aneurysms or predict their risk of dissection or rupture. This study generated a plasma proteomic dataset from 150 patients with descending thoracic aortic disease and 52 controls to identify proteomic signatures capable of differentiating descending thoracic aortic disease from non-disease controls, as well as between cases with aneurysm versus descending ‘type B’ dissection. Of the 1,468 peptides and 195 proteins quantified across all samples, 853 peptides and 99 proteins were quantitatively different between disease and control patients (BH adjusted p-value < 0.01 from t-tests). Supervised machine learning (ML) methods were used to classify disease from control and aneurysm from descending dissection cases. The highest precision-recall area under the curve (PR AUC) was achieved on the held-out test set using significantly different proteins between disease and control patients (PR AUC 0.99), followed by input of significant peptides (PR AUC 0.96). Despite no statistically significant proteins between aneurysm and dissection cases, use of all proteins was able to modestly classify between the two disease states (PR AUC 0.77). To overcome correlation in the proteins and enable biological pathway analysis, a disease versus control classifier was optimized using only seven unique protein clusters, which achieved comparable performance to models trained on all/significant proteins (accuracy 0.88, F1-score 0.78, PR AUC 0.90). Model interpretation with permutation importance revealed that proteins in the most important clusters for differentiating disease and control function in coagulation, protein-lipid complex remodeling, and acute inflammatory response.

2024-06-16 | PXD041337 | Pride

A machine learning breast cancer prediction model based on a panel from circulating exosomal miRNAs

Project description:In this study, we aim to reveal the value of plasma exo-miRNA in early diagnosis of breast cancer.In this study, after determining the success of plasma exocrine separation, we analyzed the expression of miRNA in plasma exocrine and selected 16 strong correlation features miRNA by Lasso logistic regression. Different machine learning algorithm models were constructed to evaluate the performance of 16 miRNA for early detection and diagnosis of breast cancer. The biological significance of 16 characteristic miRNAs was evaluated by bioinformatics analysis. Overall, these data highlight the value of exo-miRNA as a biomarker for breast cancer. They may be used for early detection and diagnosis of breast cancer in future clinical practice.

2022-02-21 | GSE197020 | GEO

A machine learning breast cancer prediction model based on a panel from circulating exosomal miRNAs

Project description:A machine learning breast cancer prediction model based on a panel from circulating exosomal miRNAs

| PRJNA808405 | ENA

Molecular characterization of Georgia cohort of primary breast cancer FFPE tissues on custom breast cancer DASL panel

Project description:Analysis of 143 formalin-fixed, paraffin-embedded (FFPE) primary breast tumors using a Custom Breast Cancer Panel and Human Cancer Panel for the DASL platform. Molecular markers between the pathology defined subtypes of breast cancer were assessed to hypothesize potential therapeutic targets specific to the subtypes Molecular Characterization of 143 primary breast carcinomas including 101 triple negative (TN: ER-, PR-, HER2-), 3 HER2-positive (HER2+: ER-, PR-, HER2+), and 39 hormone receptor-positive (HR+: ER+ and/or PR+)

2009-10-23 | E-GEOD-18539 | biostudies-arrayexpress

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