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DNA/RNA sequences, gene expression, protein structures, metagenomics, single-cell sequencing
23,935 datasets
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.
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.
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 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.
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.
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.
A 33.3 KB document presents findings on immune tolerance mechanisms at the maternal-fetal interface in early pregnancy. It details how decidual stromal cell-derived hyaluronic acid, via CD44 signaling, influences the differentiation and function of decidual natural killer cells, comparing phenotypes in spontaneous abortion versus normal pregnancies. The analysis is based on reanalysis of single-cell RNA sequencing data and functional assays.
PRIMED is a machine learning framework for predicting DNA-binding residues using protein language models. The model was evaluated on benchmark datasets Test-46, Test-129, and Test-10K, achieving AUC scores up to 0.93.
DoubleStar Protocol — Soul Alignment Corpus is a collection of Chinese-language text data created by author Alicea123. The dataset includes approximately 20,000 pages of Word document dialogues with an AI, over 800 files of user statements, more than 20 papers and HTML presentations, and a 140,000-word novel. It also references synthetic datasets of 100,000, 1 million, 100 million, and 5 billion entries. The dataset was last updated on HuggingFace in May 2026.
A table of characteristics for pregnant women who were followed until the end of pregnancy, broken down by country. The dataset is 13.5 KB in size, stored in an XLS file, and was authored by Wen-Chien Yang. It was last updated on April 13, 2026, and is shared under a CC-BY-4.0 license on figshare.
802.7 KB of single-cell T-cell receptor (scTCRseq) and B-cell receptor (scBCRseq) sequencing data supporting specific figures in a published research article. The dataset was authored by Taeyong Kwon and uploaded to Figshare in April 2026. It provides the underlying cellular receptor data used to generate Figures 7 and 8 of the associated study.
Global ionospheric maps of total electron content (TEC) estimated every two hours by the International GNSS Service (IGS) Analysis Centre. The data includes rapid and final TEC maps and movies in cylindrical and polar projections, derived from averages of four methods from CODE, ESA, JPL, and UPC. Continuous coverage is not guaranteed.