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80
datasets available to search
ShareScore release 0.9.0
Dataset results
80 results for “feature learning”
Using prognostic signatures and machine learning to identify core features associated with response to CDK4/6 inhibitor-based therapy in metastatic breast cancer
GEO Series GSE285861. Homo sapiens. 168 samples. Type: Expression profiling by high throughput sequencing.
Establishment of interpretable cytotoxicity prediction models using machine learning analysis of transcriptome features
GEO Series GSE252529. Homo sapiens. 15 samples. Type: Expression profiling by high throughput sequencing.
SPELLING FEATURES OF PLACE NAMES ABOUT LEARNING
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Feature Reuse and Scaling: Understanding Transfer Learning with Protein Language Models
<p>Data and checkpoints for 'Feature Reuse and Scaling: Understanding Transfer Learning with Protein Language Models'</p>
Dataset from "Identifying zebrafish segmentation phenotype features using multiple instance learning"
<p>The dataset was used to produce the results from the "Identifying zebrafish segmentation phenotype features using multiple instance learning" manuscript. It contains images of zebrafish embryos obtained by in situ hybridization that were used to train and evaluate the performance of different neural network-based image classifiers. Images were split into 4 classes: unmodified (WT) zebrafish embryo, and 3 more phenotypes reflecting different segmentation clock defects.<br> Folder 'data' contains 3 different directories: 'training', the set used for training of the classifiers, 'validation', the set used for evaluation of the performance of the classifiers, with 20 images from each class, and 'fish_part_labels' that contains the annotation of zebrafish embryo parts (head, trunk, tail, yolk, and yolk extension) of the images from the 'validation' set.</p>
Database For Finite Volume Features, Global Geometry Representations, and Residual Training for Deep Learning-based CFD Simulation
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Visualization Engineering Platform for TCM Pulse Diagnosis - Pulse Diagnosis Based on Federated Learning to Diagnose Slippery and Choppy and Other Pulses Waveform Image Features to Assist in the Study
ClinicalTrials.gov study NCT05630248. IPD Sharing: NO. Countries: 1. Publications: 0.
ANEURYSM@RISK: Automatic Intracranial Aneurysm Quantification and Feature Learning Modelling to Optimize Intracranial Aneurysm Rupture Prediction
ClinicalTrials.gov study NCT07111975. IPD Sharing: YES. Countries: 1. Publications: 0.
ALS molecular subtypes are a combination of cellular and pathological features learned by deep multiomics classifiers (snRNA-seq)
GEO Series GSE271156. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
Open Chromatin Guided Interpretable Machine Learning Reveals Cancer-Specific Chromatin Features in Cell-free DNA
GEO Series GSE279542. Homo sapiens. 20 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Machine Learning Model Guided by TLS Predicts Survival and Immune Features in Gastric Cancer
ClinicalTrials.gov study NCT06979817. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Application of machine learning (ML) / deep learning (DL) using multiple epigenetic features reveals H3K27Ac as driver of gene expression prediction across patients with glioblastoma
GEO Series GSE296948. Homo sapiens. 11 samples. Type: Genome binding/occupancy profiling by high throughput sequencing; Expression profiling by high throughput sequencing.
Application of machine learning (ML) / deep learning (DL) using multiple epigenetic features reveals H3K27Ac as driver of gene expression prediction across patients with glioblastoma [ATAC-seq]
GEO Series GSE296947. Homo sapiens. 2 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Application of machine learning (ML) / deep learning (DL) using multiple epigenetic features reveals H3K27Ac as driver of gene expression prediction across patients with glioblastoma [RNA-Seq]
GEO Series GSE296945. Homo sapiens. 2 samples. Type: Expression profiling by high throughput sequencing.
Glioma - Human Leukocyte Antigen Tissue Microarray. Data from: Deep Learning Glioma Grading with the Tumor Microenvironment Analysis Protocol for Comprehensive Learning, Discovering, and Quantifying Microenvironmental Features
<p>This repository contains images of Human Leukocyte Antigen Stained Tissue Microarray used for the experiments in the manuscript: Pytlarz, M., Wojnicki, K., Pilanc, P. et al. Deep Learning Glioma Grading with the Tumor Microenvironment Analysis Protocol for Comprehensive Learning, Discovering, and Quantifying Microenvironmental Features. J Digit Imaging. Inform. med. (2024). https://doi.org/10.1007/s10278-024-01008-x <br><br>Please cite this paper in case of using the data or methods described in it. <br>Data is shared for scientific research purposes only. Users must not take any actions that could compromise the privacy of the data.</p>
Application of machine learning (ML) / deep learning (DL) using multiple epigenetic features reveals H3K27Ac as driver of gene expression prediction across patients with glioblastoma [ChIP-Seq]
GEO Series GSE296944. Homo sapiens. 7 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Machine Learning to Predict In-Hospital Mortality in COVID-19 Patients Using Computed Tomography-Derived Pulmonary and Vascular Features
<p>Dataset from Schiaffino S, Codari M, Cozzi A, Albano D, Alì M, Arioli R, Avola E, Bnà C, Cariati M, Carriero S, Cressoni M, Danna PSC, Della Pepa G, Di Leo G, Dolci F, Falaschi Z, Flor N, Foà RA, Gitto S, Leati G, Magni V, Malavazos AE, Mauri G, Messina C, Monfardini L, Paschè A, Pesapane F, Sconfienza LM, Secchi F, Segalini E, Spinazzola A, Tombini V, Tresoldi S, Vanzulli A, Vicentin I, Zagaria D, Fleischmann D, Sardanelli F. Machine Learning to Predict In-Hospital Mortality in COVID-19 Patients Using Computed Tomography-Derived Pulmonary and Vascular Features. J Pers Med. 2021 Jun 3;11(6):501. doi: 10.3390/jpm11060501. PMID: 34204911; PMCID: PMC8230339.</p> <p>Abstract</p> <p>Pulmonary parenchymal and vascular damage are frequently reported in COVID-19 patients and can be assessed with unenhanced chest computed tomography (CT), widely used as a triaging exam. Integrating clinical data, chest CT features, and CT-derived vascular metrics, we aimed to build a predictive model of in-hospital mortality using univariate analysis (Mann-Whitney <em>U</em> test) and machine learning models (support vectors machines (SVM) and multilayer perceptrons (MLP)). Patients with RT-PCR-confirmed SARS-CoV-2 infection and unenhanced chest CT performed on emergency department admission were included after retrieving their outcome (discharge or death), with an 85/15% training/test dataset split. Out of 897 patients, the 229 (26%) patients who died during hospitalization had higher median pulmonary artery diameter (29.0 mm) than patients who survived (27.0 mm, <em>p</em> < 0.001) and higher median ascending aortic diameter (36.6 mm versus 34.0 mm, <em>p</em> < 0.001). SVM and MLP best models considered the same ten input features, yielding a 0.747 (precision 0.522, recall 0.800) and 0.844 (precision 0.680, recall 0.567) area under the curve, respectively. In this model integrating clinical and radiological data, pulmonary artery diameter was the third most important predictor after age and parenchymal involvement extent, contributing to reliable in-hospital mortality prediction, highlighting the value of vascular metrics in improving patient stratification.</p>
Data set from Machine learning to predict in-hospital mortality in covid-19 patients using computed tomography-derived pulmonary and vascular features
<p>Data set from Machine learning to predict in-hospital mortality in covid-19 patients using computed tomography-derived pulmonary and vascular features</p>
Data set from Machine learning to predict in-hospital mortality in covid-19 patients using computed tomography-derived pulmonary and vascular features
<p>Data Set from the study Machine learning to predict in-hospital mortality in covid-19 patients using computed tomography-derived pulmonary and vascular features</p>
Bacteria-Specific Features Selection for Enhanced Antimicrobial Peptide Activity Predictions Using Machine-Learning Methods
<p>We developed a new computational approach that allowed us to train several supervised machine-learning models using a specific set of data associated with peptides targeting E. coli bacteria. LASSO regression and Support Vector Machine techniques have been utilized to select, among more than 1500 physio-chemical descriptors, the most important features that can be used to classify a peptide as antimicrobial or ineffective against E. coli. We then performed the classification of active versus inactive AMPs using the Support Vector classifiers, Logistic Regression, and Random Forest methods. This computational study allows us to make recommendations of how to design more efficient anti-bacterial drug therapies.</p>
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.