Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
187
datasets available to search
ShareScore release 0.9.0
Dataset results
187 results for “Multi-omics integration”
Unsupervised neural network for single cell Multi-omics INTegration (UMINT): An application to health and disease
<p>This dataset repository corresponds to the project Unsupervised neural network for single cell Multi-omics INTegration (UMINT): An application to health and disease.</p>
Profiling of pancreatic adenocarcinoma using artificial intelligence-based integration of multi-omic and computational pathology features - Validation Data Sets
<p>Two public validation cohorts were utilized in the MT-Pilot study, the Cancer Genome Atlas (TCGA) and cohort-1 Johns Hopkins University (JHU). These datasets included DNA, RNA, clinical data, and tissue protein analytes analyzed for survival outcome prediction using AI/Machine Learning modeling. </p>
GeoMx DSP dataset of "Integrated multi-omics reveals cellular and molecular interactions governing the invasive niche of basal cell carcinoma"
<p>This data set is linked to the paper "Integrated multi-omics reveals cellular and molecular interactions governing the invasive niche of basal cell carcinoma". It contains datasets and images from GeoMx DSP analysis. </p>
Multi-omic integration of DNA methylation and gene expression data reveals molecular vulnerabilities in glioblastoma (processed data)
<p>Glioblastoma multiforme (GBM) is one of the most aggressive types of cancer and exhibits profound genetic and epigenetic heterogeneity, making the development of an effective treatment a major challenge. The recent incorporation of molecular features into the diagnosis of GBM patients has led to an improved categorisation into various tumour subtypes with different prognoses and disease management. In this work, we have exploited the benefits of genome-wide multi-omic approaches to identify potential molecular vulnerabilities existing in GBM patients. Integration of gene expression and DNA methylation data from both bulk GBM and patient-derived GBM stem cell lines has revealed the presence of major sources of GBM variability, pinpointing subtype-specific tumour vulnerabilities amenable to pharmacological interventions. In this sense, inhibition of the AP1, SMAD3 and RUNX1 / RUNX2 pathways, in combination or not with the chemotherapeutic agent temozolomide, led to the subtype-specific impairment of tumour growth, particularly in the context of the aggressive, mesenchymal-like subtype. These results emphasize the involvement of these molecular pathways in the development of GBM and have potential implications for the development of personalized therapeutic approaches.</p>
Integrated multi-omics analysis of early lung adenocarcinoma links tumor biological features with predicted indolence or aggressiveness
<p>This is a collection of datasets presented in the manuscript "Multi-omics data analysis identifies correlations between tumor biology features and predicted behaviors in early lung adenocarcinoma". This study provides a comprehensive profiling of LUAD indolence and aggressiveness at the biological bulk and single cell levels, as well as at the clinical and radiomics levels. This hypothesis generating study uncovers several potential future research avenues. It also highlights the importance and power of data integration to improve our systemic understanding of LUAD and to help reduce the gap between basic science research and clinical practice.</p>
Integrated Multi-omics Data for Personalized Treatment of Obesity-associated Fatty Liver Disease
ClinicalTrials.gov study NCT05554224. IPD Sharing: YES. Countries: 1. Publications: 1.
Precision Subtyping and Prognostic Study of Heart Failure Based on Multi-Omics Integration and Clinical Indicators: A Prospective Single-Center Cohort Study
ClinicalTrials.gov study NCT07355088. IPD Sharing: NO. Countries: 1. Publications: 9.
MOLI: multi-omics late integration with deep neural networks for drug response prediction
<p>Harmonized data used in "MOLI: multi-omics late integration with deep neural networks for drug response prediction", 2019, <em>Bioinformatics </em><a href="https://academic.oup.com/bioinformatics/article/35/14/i501/5529255">https://academic.oup.com/bioinformatics/article/35/14/i501/5529255</a>. <br> CNA.tar.gz contains CNA profiles with non-integer estimates of copy number, e.g. log-ratios. Please use binarized CNA profiles (CNA_binary.tar.gz) to replicate the results described in the paper. </p> <p><br> All raw data were obtained from open sources:<br> - https://www.cancerrxgene.org/<br> - ArrayExpress https://www.ebi.ac.uk/arrayexpress/<br> - Firehose Broad GDAC http://gdac.broadinstitute.org/runs/stddata__2016_01_28/data/<br> - Supplementary of Gao et al., 2015 https://www.nature.com/articles/nm.3954</p> <p>Gene symbols were mapped to Entrez Gene IDs. Data preprocessing is described in detail in supplementary materials. The code is available at <a href="https://github.com/hosseinshn/MOLI/tree/master/preprocessing_scr">https://github.com/hosseinshn/MOLI/tree/master/preprocessing_scr</a>.</p>
Processed data from "Human and rat skeletal muscle single-nuclei multi-omic integrative analyses nominate causal cell types, regulatory elements, and SNPs for complex traits"
<p>This is the processed data from our manscript "Human and rat skeletal muscle single-nuclei multi-omic integrative analyses nominate causal cell types, regulatory elements, and SNPs for complex traits"</p>
Processed data from "Human and rat skeletal muscle single-nuclei multi-omic integrative analyses nominate causal cell types, regulatory elements, and SNPs for complex traits"
<p>This is the processed data from our manscript "Human and rat skeletal muscle single-nuclei multi-omic integrative analyses nominate causal cell types, regulatory elements, and SNPs for complex traits"</p>
Processed data from "Human and rat skeletal muscle single-nuclei multi-omic integrative analyses nominate causal cell types, regulatory elements, and SNPs for complex traits"
<p>This is the processed data from our manscript "Human and rat skeletal muscle single-nuclei multi-omic integrative analyses nominate causal cell types, regulatory elements, and SNPs for complex traits"</p>
Processed data from "Human and rat skeletal muscle single-nuclei multi-omic integrative analyses nominate causal cell types, regulatory elements, and SNPs for complex traits"
<p>This is the processed data from our manscript "Human and rat skeletal muscle single-nuclei multi-omic integrative analyses nominate causal cell types, regulatory elements, and SNPs for complex traits"</p>
Processed data from "Human and rat skeletal muscle single-nuclei multi-omic integrative analyses nominate causal cell types, regulatory elements, and SNPs for complex traits"
<p>This is the processed data from our manscript "Human and rat skeletal muscle single-nuclei multi-omic integrative analyses nominate causal cell types, regulatory elements, and SNPs for complex traits"</p>
Processed data from "Human and rat skeletal muscle single-nuclei multi-omic integrative analyses nominate causal cell types, regulatory elements, and SNPs for complex traits"
<p>This is the processed data from our manscript "Human and rat skeletal muscle single-nuclei multi-omic integrative analyses nominate causal cell types, regulatory elements, and SNPs for complex traits"</p>
Test dataset for "Spatial Integration of Multi-Omics Data from Serial Sections using the novel Multi-Omics Imaging Integration Toolset"
<p>The uploaded tar file contains anonymized and reduced test data for the paper "Spatial Integration of Multi-Omics Data from Serial Sections using the novel Multi-Omics Imaging Integration Toolset". (doi: https://doi.org/10.1101/2024.06.11.598306; https://github.com/mwess/miit)</p> <p>Dataset description:<br>- 9 serial histology sections with the following stains: (HES, HE, HES, HES, HES, MTS, IHC, IHC, HES)<br>- Sections are indexed in the following way (due to some sections not being part of this project): 1,2,3,6,7,8,9,10,11<br>- Each serial section contains: <br> - landmarks with matching labels across all sections.<br> - semi-manually generated tissue masks <br>- Section 2 contain spatial transcriptomics data and one annotation file in geojson format.<br>- Sections 6 and 7 contain imzml data that were generated with MALDI-MSI in positive ion mode (section 6) and negative ion mode (section 7) and additional histology annotations.<br>- MALDI-MSI is reduced. The positive ion data contains only intensities and spectra for spermine. The negative ion mode data contains only intensities and spectra for citrate and zinc.<br>- ST data contains only locations of spots and scalefactors. (I.e. no count data is included.). Barcode ids are randomly generated. <br>- In addition, for each ST spot histopathological annotations and GSEA scores for the Citrate-Spermine Secretion gene signature are provided.</p> <p>Abbreviations:</p> <p>- HES = Hematoxylin-Erythrosine-Saffron<br>- HE = Hematoxylin-Eosin<br>- MTS = Masson's Trichrome Staining<br>- IHC = Immunohistochemistry<br>- ST = Spatial Transcriptomics, here refers to Visium10X arrays.<br>- MALDI-MSI = Matrix-Assisted Laser Desorption Ionization - Mass Spectrometry Imaging.</p> <p> </p>
Integrative Multi-omics Analysis to Predict Monoclonal Gammopathies Clinical Evolution
ClinicalTrials.gov study NCT07214324. IPD Sharing: Not stated. Countries: 1. Publications: 7.
Multi-omics Database for Integrative Microbiome Analysis in a Cohort of Korean Patients With Ankylosing Spondylitis
ClinicalTrials.gov study NCT06076083. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
MONDRIAN: Multi-omics Integrative Modelling for Stereotactic Body Radiotherapy in Early-stage Non-small Cell Lung Cancer
ClinicalTrials.gov study NCT05974475. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Neuroendocrine Neoplasm Based on Multi-omics Integrated Analysis
ClinicalTrials.gov study NCT04931446. IPD Sharing: NO. Countries: 1. Publications: 12.
Research on Intelligent Screening and Decision-making for Neoadjuvant Therapy in Locally Advanced Gastric Cancer Based on Multi-omics Integration
ClinicalTrials.gov study NCT06396143. IPD Sharing: NO. Countries: 1. Publications: 1.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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.