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189
datasets available to search
ShareScore release 0.9.0
Dataset results
189 results for “feature model”
Patient-derived xenograft model identifies clinically relevant subtype-specific features of colorectal cancer
GEO Series GSE103340. Homo sapiens. 71 samples. Type: Expression profiling by array.
Transcriptomic reveals the ferroptosis features of host response in a mouse model of Zika virus infection
GEO Series GSE213898. Mus musculus. 12 samples. Type: Expression profiling by high throughput sequencing.
An iPSC-derived midbrain dopaminergic neuronal model of aromatic amino acid decarboxylase (AADC) deficiency gives insight into neurodevelopmental disease features
GEO Series GSE153990. Homo sapiens. 9 samples. Type: Expression profiling by high throughput sequencing.
Modeling lethal prostate cancer variant with small cell carcinoma features [expression profile]
GEO Series GSE32967. Homo sapiens. 22 samples. Type: Expression profiling by array.
Targeted PDGFRα+ fibroblast ablation exacerbates pathologic features of asthma in house dust mite-induced allergenic asthma model
GEO Series GSE276389. Mus musculus. 12 samples. Type: Expression profiling by array.
Single cell RNA sequencing analysis of midbrain organoids with SNCA gene triplication modeling key features of synuclienopathy
GEO Series GSE186780. Homo sapiens. 2 samples. Type: Expression profiling by high throughput sequencing.
A new HDV mouse model showing important features of human infection and identifying MAVS as a key player in IFN-β induction
GEO Series GSE98342. Mus musculus. 12 samples. Type: Expression profiling by array.
Predictive modeling of chromatin features underlying replication timing
GEO Series GSE216475. Mus musculus. 134 samples. Type: Other; Genome binding/occupancy profiling by high throughput sequencing.
Tissue-specific features of oxidative stress-associated gene expression in a healthy mouse model
GEO Series GSE111159. Mus musculus. 50 samples. Type: Expression profiling by array.
Test Pretrained models for Understanding Anomaly Detection with Deep Invertible Networks through Hierarchies of Distributions and Features
<p>Test description</p>
Synthetic Datasets from the Article titled Privacy-preserving Ground-truth Data for Evaluating Additive Feature Attribution in Regression Models with Additive CBR and CQV
<p>Synthetic datasets were generated as benchmarks capturing the intrinsic characteristics of original data to investigate the performance of additive feature attribution methods for regression tasks. The synthetic datasets were generated based on 2, 6 and 8 clusters formed with the original data. The 6-cluster dataset was used for primary analysis and the other two were used for sensitivity analysis.</p><p>The synthetic dataset was generated from the original data acquired from <a href="https://www.eurocontrol.int/dashboard/rnd-data-archive">Aviation Data for Research Repository</a>, which was collected and processed by <a href="https://www.eurocontrol.int/">EUROCONTROL</a> from the Enhanced Tactical Flow Management System (ETFMS) flight data messages containing all flights in Europe throughout the year 2019, from May to October. The original dataset consisted of fundamental details of the flights, flight status, preceding flight legs, ATFM regulations, weather conditions, calendar information, etc. </p><p>A brief description of the columns in the synthetic data files is presented in the file 'data_description.pdf' and a more detailed discussion on features can be found in the works of Koolen and Coliban [1] and Dalmau et al. [2].</p><p> </p><p><strong>References</strong><br>[1] H. Koolen and I. Coliban, <a href="https://www.eurocontrol.int/sites/default/files/2020-06/flight-progress-msg-update-230620.pdf">Flight Progress Messages Document</a>, EUROCONTROL, Brussels, Belgium, Tech. Rep., 2020.<br>[2] R. Dalmau, F. Ballerini, H. Naessens, S. Belkoura, and S. Wangnick, <a href="https://www.sciencedirect.com/science/article/pii/S0969699721000739">An Explainable Machine Learning Approach to Improve Take-off Time Predictions</a>, Journal of Air Transport Management, vol. 95, p. 102 090, Aug. 2021. doi: 10.1016/j.jairtraman.2021.102090.</p><p><br> </p>
DualNetGO: A Dual Network Model for Protein Function Prediction via Effective Feature Selection
<p>Data used in the paper, including annotation files, graph embeddings from TransformerAE, and protein attributes for both human and mouse. Extract and place them in the <em>data </em>folder, and there will be two two folders <em>human </em>and <em>mouse </em>containing necessary data for training and testing.</p>
Improving inference in wastewater-based epidemiology by modelling the statistical features of digital PCR
<p>This repository contains the simulation code, data, and analysis scripts of the study "Improving inference in wastewater-based epidemiology by modelling the statistical features of digital PCR". The code can be used to reproduce all figures and numerical results in the paper.</p>
HybridCAD++: Expanded Dataset for Hybrid Additive-Subtractive Manufacturing Feature Recognition in B-Rep CAD Models
<p>The<strong> <em>HybridCAD++</em> </strong>dataset is a significantly expanded version of the <strong><em>HybridCAD</em></strong> dataset, offering a larger volume of CAD models and a more comprehensive set of hybrid additive-subtractive manufacturing features. This dataset includes additional feature classes, bringing the total to <strong>36</strong>, and contains over <strong>161,000 samples</strong>—making it a unique and robust resource for machine learning applications in hybrid manufacturing feature recognition.</p> <h3>Key Differences from HybridCAD</h3> <ul> <li><strong>Increased Dataset Volume</strong>: <em>HybridCAD++</em> features a total of 161,441 CAD models, significantly larger than the original <em>HybridCAD</em> dataset.</li> <li><strong>Expanded Feature Classes</strong>: This dataset includes 36 feature labels, with newly added classes. This increase in feature variety enhances the dataset's applicability to complex hybrid manufacturing scenarios.</li> </ul> <h3>Dataset Composition</h3> <p>The dataset includes three primary components:</p> <ol> <li> <p><strong>STEP Files</strong>:</p> <ul> <li>Each CAD model is stored in STEP format and includes labeled B-Rep faces for hybrid manufacturing feature recognition.</li> <li>The CAD models were generated programmatically using PythonOCC, ensuring consistent quality and scalability.</li> </ul> </li> <li> <p><strong>Feature Labels</strong>:</p> <ul> <li><strong>File</strong>: <code>feature_labels.txt</code></li> <li>Contains label IDs for each hybrid additive-subtractive feature across B-Rep faces in each CAD model.</li> <li>With 36 unique feature classes, this file allows precise mapping of CAD model faces to specific hybrid features.</li> </ul> <ul> <li> </li> </ul> </li> <li> <p><strong>Hierarchical B-Rep Graphs</strong>:</p> <ul> <li>Stored in HDF5 format, these graphs provide structured access to B-Rep data, as detailed in <code>h5_structure.txt</code>.</li> </ul> </li> </ol> <h3>Dataset Splits</h3> <p>The dataset is divided into three subsets, structured for effective model training and evaluation:</p> <ul> <li><strong>Training Set</strong>: 113,008 samples (70%)</li> <li><strong>Validation Set</strong>: 32,288 samples (20%)</li> <li><strong>Testing Set</strong>: 16,145 samples (10%)</li> </ul> <h3>Full Feature Label List</h3> <p>This comprehensive list includes both subtractive and additive manufacturing features, with added classes for more intricate hybrid manufacturing applications:</p> <p> </p> <p>Label Feature<br>0 Chamfer<br>1 Through hole<br>2 Triangular passage<br>3 Rectangular passage<br>4 6-sided passage<br>5 Triangular through slot<br>6 Rectangular through slot<br>7 Circular through slot<br>8 Rectangular through step<br>9 2-sided through step<br>10 Slanted through step<br>11 O-ring<br>12 Blind hole<br>13 Triangular pocket<br>14 Rectangular pocket<br>15 6-sided pocket<br>16 Circular end pocket<br>17 Rectangular blind slot<br>18 Vertical circular end blind slot<br>19 Horizontal circular end blind slot<br>20 Triangular blind step<br>21 Circular blind step<br>22 Rectangular blind step<br>23 Round<br>24 Extrude cylinder<br>25 Extrude rectangle<br>26 Extrude triangle<br>27 Extrude hexagon<br>28 Extrude pentagon<br>29 Elliptical/Oval blind hole<br>30 Elliptical/Oval through hole<br>31 Slot hole<br>32 Obround boss<br>33 5-sided passage<br>34 5-sided pocket<br>35 Cylinder with hole<br>36 Stock</p>
Study and Transformation of Tumor Molecular Features Screening Model of Endometrial Carcinoma Surgical Approach
ClinicalTrials.gov study NCT05894915. IPD Sharing: NO. Countries: 0. Publications: 0.
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.
Combining Model-Based and Feature-Driven Diagnosis Approaches – A Case Study on Electromechanical Actuators
Model-based diagnosis typically uses analytical redundancy to compare predictions from a model against observations from the system being diagnosed. However this approach does not work very well when it is not feasible to create analytic relations describing all the observed data, e.g., for vibration data which is usually sampled at very high rates and requires very detailed finite element models to describe its behavior. In such cases, features (in time and frequency domains) that contain diagnostic information are extracted from the data. Since this is a computationally intensive process, it is not efficient to extract all the features all the time. In this paper we present an approach that combines the analytic model-based and feature-driven diagnosis approaches. The analytic approach is used to reduce the set of possible faults and then features are chosen to best distinguish among the remaining faults. We describe an implementation of this approach on the Flyable Electro-mechanical Actuator (FLEA) test bed.
YAP1 Activation Rescues Nagashima-type Palmoplantar Keratosis Pathological Features in Skin Organoid Disease Models
GEO Series GSE285879. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
A novel Induced-recurrence PDX model recapitulates epi-genomic features of Glioblastoma recurrence [DNA methylation EPIC]
GEO Series GSE271621. Homo sapiens. 45 samples. Type: Methylation profiling by genome tiling array.
Buffering of transcription rate by mRNA half-life is a conserved feature of Rett syndrome models
GEO Series GSE191168. Homo sapiens. 26 samples. Type: Expression profiling by high throughput sequencing; Non-coding RNA profiling by high throughput sequencing.
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.