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77 results for “Biomarker gene expression”
Summary Statistics from "Genetically regulated gene expression and proteins revealed discordant effects" (LWAS of biomarker)
<p>Summary statistics of 92 blood protein levels. The corresponding publication is currently under revision.</p> <p> The zipped txt file is tab-delimited and contains the following columns:</p> <ul> <li>protein: protein name abbreviation</li> <li>cytoband: genomic region</li> <li>gene: gene name abbreviation</li> <li>setting: either "combined" (adj. for sex & age) or sex-stratified ("males", "females"; adj. for age)</li> <li>variant_id_hg19: SNP ID according to hg19</li> <li>variant_id_hg38: SNP ID according to hg19</li> <li>chr: chromosome</li> <li>pos_hg19: base position according to hg19</li> <li>pos_hg38: base position according to hg19</li> <li>effect_allele: also known as counted allele in additive model</li> <li>other_allele: not-counted allele</li> <li>eaf: effect allele frequency</li> <li>maf: minor allele frequency</li> <li>info: imputation info score</li> <li>n_samples: number of samples</li> <li>beta: effect estimate</li> <li>se: standard error</li> <li>zscore: Z-statistic</li> <li>pvalue: p-value</li> <li>FDR: FDR by gene and setting</li> <li>BBFDR: hierarchical FDR by setting</li> <li>hierFDR: TRUE if SNP is significant after hierarchical FDR</li> </ul>
Soil ecotoxicology needs robust biomarkers – a meta-analysis approach to test the robustness of gene expression-based biomarkers for measuring chemical exposure effects in soil invertebrates
<p>Gene expression-based biomarkers are regularly proposed as rapid, sensitive and mechanistically informative tools to identify whether soil invertebrates are experiencing adverse effects due to chemical exposure. However, before biomarkers could be deployed within diagnostic studies, systematic evidence of the robustness of such biomarkers to detect effects is needed. Here, we present an approach for conducting a systematic meta-analysis of the robustness of gene expression-based biomarkers in soil invertebrates.</p> <p>The approach was developed and trialled for two measurements of gene expression commonly proposed as biomarkers in soil ecotoxicology: metallothionein (MT) gene expression in earthworms for metals and heat shock protein 70 (HSP70) gene expression in earthworms for organic chemicals. From a systematic analysis of the published literature, we collected 294 unique gene expression data points and used linear mixed-effect models to assess concentration, exposure duration and species effects on the quantified response.</p> <p>This database provided contains gene-expression data from publications that have used gene expression-based biomakers to study effects of chemical pollutants on soil invertebrates. R scripts are provided that were used to study the patterns of gene expression as reported in accompanying publication. </p> <p>We encourage colleagues in the field to apply this approach to other biomarkers, as such quantitative assessment is a prerequisite to ensuring that the suitability and limitations of proposed biomarkers are known and stated.</p>
Decoding diabetes biomarkers and related molecular mechanisms using machine learning, text mining, and gene expression analysis
<p>The molecular basis of diabetes mellitus is yet to be fully elucidated. We aimed to identify the most frequently reported and differential expressed genes (DEGs) in diabetes using bioinformatics approaches. Text mining was used to screen 40,225 article abstracts from diabetes literature. These studies highlighted 5939 diabetes-related genes spread across 22 human chromosomes, with 112 genes mentioned in more than 50 studies. Among these genes, HNF4A, PPARA, VEGFA, TCF7L2, HLA- DRB1, PPARG, NOS3, KCNJ11, PRKAA2, and HNF1A were mentioned in more than 200 articles. These genes are correlated with the regulation of glycogen and polysaccharide, adipogenesis, AGE/RAGE, and macrophage differentiation. Three datasets (44 patients and 57 controls) were subjected to gene expression analysis. The analysis revealed 135 significant DEGs, of which CEACAM6, ENPP4, HDAC5, HPCAL1, PARVG, STYXL1, VPS28, ZBTB33, ZFP37 and CCDC58 were the top ten DEGs. These genes were enriched in aerobic respiration, T-Cell antigen receptor pathway, Tricarboxylic acid metabolic process, vitamin D receptor pathway, Toll-like receptor signaling, and endoplasmic reticulum (ER) unfolded protein response. The results of text mining and gene expression analyses used as attribute values for ML analysis . The "Decision tree", "Extra-tree regressor" and "Random forest" algorithms were used in ML analysis to identify unique markers that could be used as diabetes diagnosis tools. These algorithms produced prediction models with accuracy ranges from 0.6364 to 0.88 and overall confidence interval (CI) of 95%. There were 39 biomarkers that could distinguish diabetic and non-diabetic patients, 12 of which were repeated multiple times. The majority of these genes are associated with stress response, signalling regulation, locomotion, cell motility, growth, and muscle adaptation. ML algorithms highlighted the use of the HLA-DQB1 gene as a biomarker for diabetes early detection. Our data mining and gene expression analysis have provided useful information about potential biomarkers in diabetes.</p>
Heat shock protein gene expression varies among populations but does not strongly track recent environmental conditions: implications for biomarker development
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Data from: Diagnostic gene expression biomarkers of coral thermal stress
Gene expression biomarkers can enable rapid assessment of physiological conditions in situ, providing a valuable tool for reef managers interested in linking organism physiology with large-scale climatic conditions. Here, we assessed the ability of quantitative PCR (qPCR) based gene expression biomarkers to evaluate (1) the immediate cellular stress response (CSR) of Porites astreoides to incremental thermal stress and (2) the magnitude of CSR and cellular homeostasis response (CHR) during a natural bleaching event. Expression levels largely scaled with treatment temperature, with the strongest responses occurring in heat shock proteins. This is the first demonstration of a "tiered" CSR in a coral, where the magnitude of expression change is proportional to stress intensity. Analysis of a natural bleaching event revealed no signature of an acute CSR in normal or bleached corals, indicating that the bleaching stressor(s) had abated by the day of sampling. Another long-term stress CHR-based indicator assay was significantly elevated in bleached corals; though assay values overall were low, suggesting good prospects for recovery. This study represents the first step in linking variation in gene expression biomarkers to stress tolerance and bleaching thresholds in situ by quantifying the severity of ongoing thermal stress and its accumulated long-term impacts.
Data from: Diagnostic gene expression biomarkers of coral thermal stress
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Evaluation of CCR6 Gene Expression and Circulating CCL20 Levels as Potential Biomarkers in Rheumatoid Arthritis
ClinicalTrials.gov study NCT07397741. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Data from: Caenorhabditis elegans genes affecting interindividual variation in life-span biomarker gene expression
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Characterization of a gene expression biomarker predictive of hypoxia-inducible factor-1 modulation
GEO Series GSE291564. Homo sapiens. 24 samples. Type: Expression profiling by high throughput sequencing.
IFI27 is a gene-expression biomarker that identifies influenza-specific immune response in symptomatic patients
GEO Series GSE82050. Homo sapiens. 39 samples. Type: Expression profiling by array.
Biomarker Validation on the ‘Analysis of geNe Expression and bioMarkers fOr poiNt-of-care dEcision support in Sepsis’ (ANEMONES) study
GEO Series GSE236713. Homo sapiens. 447 samples. Type: Expression profiling by array.
Gene expression profiles of normal mouse mammary cell subpopulations FACS sorted based on expression of biomarkers
GEO Series GSE19446. Mus musculus. 18 samples. Type: Expression profiling by array.
miR-7 as novel predictive epigenetic biomarker for platinum-resistance in cancer (gene expression)
GEO Series GSE84146. Homo sapiens. 24 samples. Type: Expression profiling by array.
Gene Expression Profiling of Primary Male Breast Cancers Reveals Two Unique Subgroups and Identifies N-acetyltransferase-1 (NAT1) as a Novel Prognostic Biomarker
GEO Series GSE31259. Homo sapiens. 74 samples. Type: Expression profiling by array.
Gene Expression Analysis of Blood Reveals S100A11 and AQP9 as Potential Biomarkers of Infective Endocarditis
GEO Series GSE29161. Homo sapiens. 15 samples. Type: Expression profiling by array.
Oral cancer metastatic gene expression profiling: Identification of novel potential biomarker
GEO Series GSE145272. Homo sapiens. 10 samples. Type: Expression profiling by high throughput sequencing; Non-coding RNA profiling by high throughput sequencing.
Differential expression and co-expression gene networks reveal candidate biomarkers of boar taint in non-castrated pigs (RNA-seq data set)
GEO Series GSE113316. Sus scrofa. 96 samples. Type: Expression profiling by high throughput sequencing.
[Gene Expression Training Set] Protein-coding and MicroRNA Biomarkers of Recurrence of Prostate Cancer Following Radical Prostatectomy
GEO Series GSE26022. Homo sapiens. 192 samples. Type: Expression profiling by array.
Predicting patient outcome based on gene-expression biomarkers learned from colorectal cancer organoids and cell lines
GEO Series GSE251958. Homo sapiens. 21 samples. Type: Expression profiling by high throughput sequencing.
Differences in metabolic biomarkers in the blood and gene expression profiles of peripheral blood mononuclear cells among normal weight, mildly obese, and moderately obese subjects
GEO Series GSE69039. Homo sapiens. 18 samples. Type: Expression profiling by array.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.