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DNA/RNA sequences, gene expression, protein structures, metagenomics, single-cell sequencing
24,762 datasets
Integrated 16S rRNA gene sequencing and RNA sequencing data were generated from a mouse model of dextran sulfate sodium-induced colitis treated with an engineered probiotic yeast strain (SB12), wild-type Saccharomyces boulardii, or control conditions. The dataset includes relative abundance matrices of gut microbial taxa from fecal samples and processed gene expression matrices from colonic tissues. It provides a resource for studying multi-omics responses to engineered probiotic intervention in inflammatory bowel disease.
Containing measurements of Tetrahydrocortisone and Pregnanetriol hormone levels for patients with Cushing's syndrome, with a Type classification for most patients. It has 4 columns: patient label, two hormone measurements, and a type. The type for the last six patients is unknown.
Grupo de Información en Reproducción Elegida (GIRE) published a 2.5 MB PDF document in 2023 analyzing possible determinants of obstetric violence and maternal death in Mexico. The analysis is based on publicly available data from the National Survey on the Dynamics of Household Relationships (ENDIREH) and mortality figures from INEGI. It aims to contribute to discussions on addressing and preventing these phenomena.
Source data for RNA-seq and pathway analysis of pulmonary sympathetic–epithelial regulation of adipose thermogenesis. The dataset includes RNA-seq results from mouse lung treated with Con and CELO, and pathway analysis for NE-treated C22 cells at 3 hours. It is a 2.1 MB collection of XLS and XLSX files authored by 俊坤 姜 and last updated on 2026-04-20.
A 2026 analysis by Jingyu Lin presents results from proteome-wide Mendelian randomization and colocalization studies to identify plasma proteins with causal links to schizophrenia. The primary analysis used genetic instruments for 4,907 plasma proteins from 35,559 Icelanders and summary statistics for schizophrenia from 35,476 cases and 46,839 controls. Findings were validated using external datasets from the Fenland study, UK Biobank Pharma Proteomics Project, and GTEx brain expression data.
Jingyu Lin's 2026 study presents results from proteome-wide Mendelian randomization and colocalization analyses to identify causal plasma proteins for schizophrenia. The primary analysis used genetic instruments for 4,907 plasma proteins from 35,559 Icelanders and summary statistics for schizophrenia from 35,476 cases and 46,839 controls. Findings were validated using external datasets including the Fenland study and UK Biobank Pharma Proteomics Project.
A six-gene diagnostic signature (FOXO1, ZBTB16, HOXB2, LYVE1, MGP, CYP26B1) was identified for sarcopenia using 113 machine learning algorithms and four transcriptomic datasets. The model achieved high predictive accuracy (AUC >0.80), and Mendelian randomization confirmed a causal role for CYP26B1. The dataset, created by Yaoqi Wu and shared on figshare, was last updated on March 18, 2026.
A six-gene diagnostic signature for sarcopenia was developed using 113 machine learning algorithms and validated via Mendelian randomization. The dataset includes results from the integration of four transcriptomic datasets, identifying 318 differentially expressed genes and 109 candidate biomarkers. Author Yaoqi Wu published the findings on figshare under a CC-BY-4.0 license in March 2026.
Yaoqi Wu published a dataset on 2026-03-18 containing a six-gene diagnostic signature for sarcopenia identified through bioinformatics analysis. The 29.6 KB file likely contains results from the integration of four transcriptomic datasets, machine learning model evaluation, and Mendelian randomization analysis. The signature includes genes FOXO1, ZBTB16, HOXB2, LYVE1, MGP, and CYP26B1, with CYP26B1 identified as a causal factor.
A research report from Geoscience Australia, last updated March 2026, focuses on reconciling vertical height datums for bathymetric data in Australia. The work addresses disparities between terrestrial datums like the Australian Height Datum and marine datums like Chart Datum or Lowest Astronomical Tide. It presents a methodology for calculating uncertainties for Mean Sea Level observations to aid in creating a unified ellipsoidal surface.
Supplementary file 4_Machine learning-assisted analysis of serum metabolomics for identifying biomarkers in intrinsic and idiosyncratic drug-induced liver injury.docx is a 12.9 KB dataset by Xianni Wei, last updated on 2026-03-18. It contains metabolomic profiling results from 44 DILI cases (17 intrinsic, 27 idiosyncratic) analyzed via HP-CIL LC-MS and machine learning. The study identified four differential metabolites and developed diagnostic models with AUC values up to 0.983.
A metabolomic study by Xianni Wei, published on figshare in March 2026, compares serum samples from 44 patients with drug-induced liver injury. The data, generated via high-performance chemical isotope labeling liquid chromatography–mass spectrometry, identifies four differential metabolites. Machine learning models were used to evaluate these metabolites as potential biomarkers for distinguishing intrinsic (n=17) from idiosyncratic (n=27) DILI types.
A metabolomic study of 44 drug-induced liver injury (DILI) cases, comprising 17 intrinsic and 27 idiosyncratic types, conducted by Xianni Wei. The dataset contains metabolomic profiles generated via high-performance chemical isotope labeling liquid chromatography–mass spectrometry (HP-CIL LC-MS) to identify differential metabolites. The research, last updated in March 2026, employed machine learning models to evaluate diagnostic biomarkers, achieving area under the curve (AUC) values greater than 0.8.
A metabolomic study of 44 drug-induced liver injury (DILI) cases, comprising 17 intrinsic and 27 idiosyncratic types, conducted by Xianni Wei and last updated in March 2026. The dataset was generated using high-performance chemical isotope labeling liquid chromatography–mass spectrometry (HP-CIL LC-MS) to profile serum samples. It includes four identified differential metabolites and results from machine learning models used for diagnostic biomarker discovery.
Supplementary file 5 contains metabolomic data from a study comparing intrinsic and idiosyncratic drug-induced liver injury (DILI). The dataset, authored by Xianni Wei and last updated in March 2026, includes results from high-performance chemical isotope labeling liquid chromatography–mass spectrometry (HP-CIL LC-MS) analysis of 44 serum samples. It was used to identify four differential metabolites and develop machine learning diagnostic models with AUC values greater than 0.8.
A 9.8 KB dataset from a case-control study of term infants with pathologic jaundice and matched healthy controls in Xinjiang, published by Muqing Niu on figshare in March 2026. It contains results from shotgun metagenomic sequencing of stool samples, including microbial diversity, taxonomic composition, and functional gene repertores, used in a bidirectional Mendelian randomization analysis.
Shotgun metagenomic sequencing data from a case-control study of term infants with pathologic jaundice and matched healthy controls in Xinjiang, published on figshare by author Muqing Niu. The dataset includes taxonomic composition, functional gene repertoires, and carbohydrate-active enzyme profiles, analyzed alongside bidirectional Mendelian randomization results. It was last updated on March 18, 2026.
A case-control study of term infants with pathologic jaundice and matched healthy controls. Stool samples were analyzed via shotgun metagenomic sequencing to assess microbial diversity, taxonomic composition, and functional gene repertoires. The dataset was uploaded by Muqing Niu on 2026-03-18.
Ogaga Okore's dataset contains results from an Exploratory Factor Analysis (EFA) performed on factors related to Contractual Governance, Relational Governance, and Collaborative Behaviour. The dataset is 235.4 KB in size and was last updated on April 24, 2026. It is available under a CC-BY-4.0 license on the figshare platform.
NCEI accession 0164569 provides an internally consistent dataset of Del13C-DIC (carbon-13 isotope) measurements from dissolved inorganic carbon in the North Atlantic Ocean. The dataset contains 6569 samples collected during 32 oceanographic research cruises between 1981 and 2014. Data underwent a two-step quality control process, including crossover analysis to quantify and adjust for systematic biases between cruises.