Ontology highlight
ABSTRACT:
PROVIDER: PRJNA520920 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR8528700_1.fastq.gz | Fastqsanger.gz | |||
| SRR8528700_2.fastq.gz | Fastqsanger.gz | |||
| SRR8528710_1.fastq.gz | Fastqsanger.gz | |||
| SRR8528710_2.fastq.gz | Fastqsanger.gz | |||
| SRR8528720_1.fastq.gz | Fastqsanger.gz |
Items per page: 5 1 - 5 of 192 |
Similar Datasets
Project description:The search for biomarkers to predict radiosensitivity is important not only to individualize radiotherapy of cancer patients but also to forecast radiation exposure risks. The aim of this study was to devise a machine learning method to stratify radiosensitivity and to investigate its association with copy number variations (CNVs) as markers of sensitivity to ionizing radiation. We used the Affymetrix CytoScan HD microarrays to for CNV estimation. Celluar radiosensitivity was measured by the clonogenic surviving fraction at 2 Gy (SF2).
2023-05-08 | GSE231621 | GEO
Project description:Precision medicine's potential to transform complex autoimmune-disease treatment is often challenged by limited data availability and inadequate sample size when compared to the number of molecular features found in high-throughput multi-omics datasets. Addressing this issue, the novel framework PRoBeNet (Predictive Response Biomarkers using Network medicine) was developed. ProBeNet operates under the hypothesis that the therapeutic effect of a drug propagates through a protein–protein interaction network to reverse disease states. ProBeNet prioritizes biomarkers by considering (1) therapy-targeted proteins, (2) disease-specific molecular signatures, and (3) an underlying network of interactions among cellular components (the human interactome). With ProBeNet, biomarkers were discovered predicting patient responses to both an established autoimmune therapy (infliximab) and an investigational compound (a MAPK3/1 inhibitor). Predictive power of ProBeNet biomarkers was validated with retrospective gene-expression data from ulcerative-colitis and rheumatoid-arthritis patients and prospective data from ulcerative-colitis and Crohn’s disease patient-derived tissues. Machine-learning models using ProBeNet biomarkers significantly outperformed models using either all genes or randomly selected genes, especially when data were limited (fewer than 20 samples). These results illustrate the value of ProBeNet for reducing features and for constructing robust machine-learning models when limited data are available. ProBeNet may be used to develop companion and complementary diagnostic assays for complex autoimmune-disease therapies, which may help stratify suitable patient subgroups in clinical trials, approve new drugs, and improve patient outcomes.
2024-03-13 | GSE261205 | GEO
Project description:In an effort to identify novel biomarkers capable of predicting the development of acute kidney injury (AKI) after cardiac surgery with cardiopulmonary bypass (CPB) use, urine specimens were collected before and at 4 and 24 hours after surgery from 106 patients and analyzed by means of nuclear magnetic resonance (NMR) spectroscopy and machine learning methods.
2012-10-21 | MTBLS24 | MetaboLights
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
Project description:Background: Clinical misdiagnosis between cutaneous squamous cell carcinoma (cSCC) and basal cell carcinoma (BCC) poses treatment challenges and carries risks of recurrence, metastases, and increased morbidity and mortality. Objective: We aimed to identify discriminant proteins markers for cSCC and BCC using a minimally invasive proteome sampling method called e-biopsy, employing electroporation for non-thermal cell permeabilization and machine learning. Methods: E-biopsy facilitated ex vivo proteome extraction from 21 cSCC and 21 BCC pathologically validated human cancers. LC/MS/MS profiling of 126 proteomes was followed by Machine Learning analysis to identify proteins distinguishing cSCC from BCC. For identified panel validation, we used proteomes sampled by e-biopsy from unrelated 20 cSCC and 46 BCC human cancers, and differential expression analysis of published transcriptomics. The most commonly chosen discriminant biomarker by machine learning models, cornulin, was also validated using fluorescent immunohistochemistry. Results: 192 proteomes sampled from 108 patients were analyzed. Machine Learning-based approaches resulted in a set of 11 potential biomarker proteins that can be used to construct a model with 95.2% average cross-validation accuracy, BCC precision of 93.6±14.5%, cSCC precision of 98.4±7.2%, specificity of 97.7±11.8%, and per-patient sensitivity 92.7±15.3%. Protein-protein interaction analysis revealed a novel interaction network connecting 10 of the 11 resulted proteins. Histological and transcriptomic validation confirmed cornulin as a discriminant marker significantly lower in cSCC than in BCC. Conclusions: E-biopsy combined with machine learning provides a novel approach to molecular biomarkers sampling from skin for biomarker detection and differential expression analysis between cSCC and BCC
2024-08-22 | PXD050713 | Pride
Project description:Background Hypertrophic cardiomyopathy (HCM) is defined clinically by pathological left ventricular hypertrophy (LVH). We have previously developed a plasma proteomics biomarker panel that correlates with clinical markers of disease severity and sudden cardiac death (SCD) risk in adult patients with HCM. The aim of this study was to validate the adult biomarkers and perform new discovery proteomics in childhood-onset HCM. Methods Fifty-nine protein biomarkers were identified from an exploratory plasma proteomics screen in children with HCM and augmented into our existing multiplexed targeted liquid chromatography-tandem/mass spectrometry-based assay. The association of these biomarkers with clinical phenotypes and outcomes was prospectively tested in plasma collected from 148 children with HCM and 50 healthy controls. Machine learning techniques were used to develop novel paediatric plasma proteomic biomarker panels.Results Four previously validated adult HCM markers (Aldolase Fructose-Bisphosphate A, Complement C3a, Talin-1 and Thrombospondin 1) and three new markers (Glycogen Phosphorylase B, Lipoprotein A and Profilin 1) were elevated in paediatric HCM. Using supervised machine learning applied to training (n=137) and validation cohorts (n=61), this 7-biomarker panel differentiated HCM from healthy controls with an area under the curve of 1.0 in the training dataset (sensitivity 100% [95% confidence interval: 95–100]; specificity 100% [96–100]) and 0.82 in the validation dataset (sensitivity 75% [59–86]; specificity 88% [75–94]). Reduced circulating levels of 4 other peptides (Apolipoprotein L1-, Complement 5b-, Immunoglobulin Heavy Constant Epsilon- and Serum Amyloid A4-peptide) found in children with high SCD risk provided complete separation from the low and intermediate risk groups, and predicted mortality and adverse cardiovascular outcomes (hazard ratio 1.53 [1.2 –2.0], p = 0.001). ConclusionIn children, a 7-biomarker proteomics panel can distinguish HCM from controls with high sensitivity and specificity, and a second 4-biomarker panel identifies those at high risk of adverse outcomes, including sudden cardiac death.
2024-07-03 | PXD045304 | Pride
Project description:Classifying Patients with Psoriatic Arthritis According to their Disease Activity Status using Serum Metabolites and Machine Learning
2023-08-19 | MSV000092699 | GNPS
Project description:Diabetic kidney disease is the leading cause of end-stage kidney disease worldwide; however, the integration of high-dimensional trans-omics data to predict this diabetic complication is rare. We develop artificial intelligence (AI)-assisted models using machine learning algorithms to identify a biomarker signature that predisposes high risk patients with diabetes mellitus (DM) to diabetic kidney disease based on clinical information, untargeted metabolomics, targeted lipidomics and genome-wide single nucleotide polymorphism (SNP) datasets. This involves 618 individuals who are split into training and testing cohorts of 557 and 61 subjects, respectively. Three models are developed. In model 1, the top 20 features selected by AI give an accuracy rate of 0.83 and an area under curve (AUC) of 0.89 when differentiating DM and non-DM individuals. In model 2, among DM patients, a biomarker signature of 10 AI-selected features give an accuracy rate of 0.70 and an AUC of 0.76 when identifying subjects at high risk of renal impairment. In model 3, among non-DM patients, a biomarker signature of 25 AI-selected features give an accuracy rate of 0.82 and an AUC of 0.76 when pinpointing subjects at high risk of chronic kidney disease. In addition, the performance of the three models is rigorously verified using an independent validation cohort. Intriguingly, analysis of the protein-protein interaction network of the genes containing the identified SNPs (RPTOR, CLPTM1L, ALDH1L1, LY6D, PCDH9, B3GNTL1, CDS1, ADCYAP and FAM53A) reveals that, at the molecular level, there seems to be interconnected factors that have an effect on the progression of renal impairment among DM patients. In conclusion, our findings reveal the potential of employing machine learning algorithms to augment traditional methods and our findings suggest what molecular mechanisms may underlie the complex interaction between DM and chronic kidney disease. Moreover, the development of our AI-assisted models will improve precision when diagnosing renal impairment in predisposed patients, both DM and non-DM. Finally, a large prospective cohort study is needed to validate the clinical utility and mechanistic implications of these biomarker signatures.
2022-10-12 | GSE215221 | GEO
Project description:The pathogenesis of celiac disease (CeD) remains incompletely understood. Traditional diagnostic techniques for CeD include serological testing and endoscopic examination; however, they have limitations. Therefore, there is a need to identify novel noninvasive biomarkers for CeD diagnosis. We analyzed duodenal and plasma samples from CeD patients by four-dimensional data-dependent acquisition (4D-DIA) proteomics. Differentially expressed proteins (DEPs) were identified for functional analysis and to propose blood biomarkers associated with CeD diagnosis. In duodenal and plasma samples, respectively, 897 and 140 DEPs were identified. Combining weighted gene co-expression network analysis(WGCNA) with the DEPs, five key proteins were identified across three machine learning methods. FGL2 and TXNDC5 were significantly elevated in the CeD group, while CHGA expression showed an increasing trend, but without statistical significance. The receiver operating characteristic curve results indicated an area under the curve (AUC) of 0.7711 for FGL2 and 0.6978 for TXNDC5, with a combined AUC of 0.8944. Exploratory analysis using Mfuzz and three machine learning methods identified four plasma proteins potentially associated with CeD pathological grading (Marsh classification): FABP, CPOX, BHMT, and PPP2CB. We conclude that FGL2 and TXNDC5 deserve exploration as potential sensitive, noninvasive diagnostic biomarkers for CeD.
2024-11-15 | PXD057692 | Pride
Project description:Transcriptional profiling and multiple machine learning methods identified novel microglia biomarkers for Experimental Autoimmune Encephalomyelitis
| PRJNA1065282 | ENA