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170
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ShareScore release 0.9.0
Dataset results
170 results for “predictive mapping”
ChromaFold predicts the 3D contact map from single-cell chromatin accessibility
GEO Series GSE246859. Homo sapiens; Mus musculus. 3 samples. Type: Other; Genome binding/occupancy profiling by high throughput sequencing.
Hormone-responsive enhancer-activity maps reveal predictive motifs, indirect repression, and targeting of closed chromatin
GEO Series GSE47691. Drosophila melanogaster. 20 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing; Other.
Cancer type and survival prediction based on transcriptomic feature map
<p>Upload all source data of our paper "Cancer type and survival prediction based on transcriptomic feature map"</p> <p># Zendo Project</p> <p> </p> <p>This project encompasses a series of files related to the integration of cancer survival data, feature extraction, and the augmentation of data using Generative Adversarial Networks (GANs). The project aims to improve the accuracy of survival predictions and support cancer research through in-depth analysis and processing of cancer-related data.</p> <p> </p> <p>## Project Overview</p> <p> </p> <p>This repository includes HDF5 files of cancer survival data, intermediate files for feature extraction, and data files augmented using GAN networks. Among them, `Data Integration.ipynb` is a Jupyter notebook used for slicing and merging `feat_part1.h5` to `feat_part6.h5`, while files starting with four letters are data files for specific cancer types, and files starting with `GAN` are data that have been augmented by GAN networks.</p> <p> </p> <p>## File Descriptions</p> <p> </p> <p>### Data Files</p> <p> </p> <p>- BLCA.h5: Survival data for bladder cancer.</p> <p>- BRCA.h5: Survival data for breast cancer.</p> <p>- HNSC.h5: Survival data for head and neck cancer.</p> <p>- KIRC.h5: Survival data for kidney clear cell carcinoma.</p> <p>- LIHC.h5: Survival data for liver hepatocellular carcinoma.</p> <p>- LUAD.h5: Survival data for lung adenocarcinoma.</p> <p>- LUSC.h5: Survival data for lung squamous cell carcinoma.</p> <p>- OVxx.h5: Survival data for ovarian cancer.</p> <p>- SKCM.h5: Survival data for skin cutaneous melanoma.</p> <p>- STAD.h5: Survival data for stomach adenocarcinoma.</p> <p> </p> <p>### Feature Extraction Files</p> <p> </p> <p>- feat_part1.h5 to feat_part6.h5: Intermediate data files for feature extraction, used to store partial feature data during the processing.</p> <p> </p> <p>### Feature Map Files</p> <p> </p> <p>- fmap_BLCA.h5: Feature map data for bladder cancer.</p> <p>- fmap_BRCA.h5: Feature map data for breast cancer.</p> <p>- fmap_HNSC.h5: Feature map data for head and neck cancer.</p> <p>- fmap_KIRC.h5: Feature map data for kidney clear cell carcinoma.</p> <p>- fmap_LIHC.h5: Feature map data for liver hepatocellular carcinoma.</p> <p>- fmap_LUAD.h5: Feature map data for lung adenocarcinoma.</p> <p>- fmap_LUSC.h5: Feature map data for lung squamous cell carcinoma.</p> <p>- fmap_OVxx.h5: Feature map data for ovarian cancer.</p> <p>- fmap_SKCM.h5: Feature map data for skin cutaneous melanoma.</p> <p>- fmap_STAD.h5: Feature map data for stomach adenocarcinoma.</p> <p> </p> <p>### GAN Augmented Data Files</p> <p> </p> <p>- GAN500sur_BLCA.h5: Augmented survival data for bladder cancer.</p> <p>- GAN500sur_BRCA.h5: Augmented survival data for breast cancer.</p> <p>- GAN500sur_HNSC.h5: Augmented survival data for head and neck cancer.</p> <p>- GAN500sur_KIRC.h5: Augmented survival data for kidney clear cell carcinoma.</p> <p>- GAN500sur_LIHC.h5: Augmented survival data for liver hepatocellular carcinoma.</p> <p>- GAN500sur_LUAD.h5: Augmented survival data for lung adenocarcinoma.</p> <p>- GAN500sur_LUSC.h5: Augmented survival data for lung squamous cell carcinoma.</p> <p>- GAN500sur_OVxx.h5: Augmented survival data for ovarian cancer.</p> <p>- GAN500sur_SKCM.h5: Augmented survival data for skin cutaneous melanoma.</p> <p>- GAN500sur_STAD.h5: Augmented survival data for stomach adenocarcinoma.</p> <p> </p> <p>### Jupyter Notebook</p> <p> </p> <p>- Data Integration.ipynb: The code for slicing and merging `feat_part1.h5` to `feat_part6.h5`, a core tool in the feature integration process.</p> <p> </p> <p>## Usage Guide</p> <p> </p> <p>1. Data Preparation: Place the original data files in the project directory.</p> <p>2. Feature Extraction: Run the `Data Integration.ipynb` notebook to merge the intermediate feature extraction files.</p> <p>3. Data Augmentation: Use GAN networks to augment survival data, generating new data files.</p> <p>4. Analysis Results: Use the augmented data for survival prediction analysis.</p> <p> </p> <p>## Contributing and Collaboration</p> <p> </p> <p>We welcome contributions and collaboration in any form. If you have any questions or suggestions about the project, please contact us through GitHub Issues.</p> <p> </p> <p> </p>
The dataset for paper Roughness prediction of end milling surface for behavior mapping of digital twined machine tools
<p>The original real measured surface roughness is in the "Ra.xlsx". The origigital sensor data is named as "ACF-x-x". In each excel document, three directions of vibration, current and force sensor data is included. <br> Naming rules is as follows: In“ACF-1-2", "1-2"represents the second slot in first layler. <br> The corresponding cutting parameters for each slot are illustraed in "Ra.xlsx" document. </p>
Predicting Outcomes of GPOEM Using Gastric Electrical Mapping
ClinicalTrials.gov study NCT06381349. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Prediction of WMH in Migraine Using a BOLD-CVR Map
ClinicalTrials.gov study NCT03494673. IPD Sharing: Not stated. Countries: 1. Publications: 0.
T2 Heart Mapping in AMI Population for the Prediction of Short Term Major Adverse Cardiovascular Events
ClinicalTrials.gov study NCT01796743. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Multi-modal Adverse Events Prediction for Premature Coronary Heart Disease Trial: MAP-CHD Trial
ClinicalTrials.gov study NCT07004452. IPD Sharing: Not stated. Countries: 1. Publications: 0.
3D Non-invasive Myocardial Electrical Propagation Mapping as a New Tool to Predict Sudden Death Risk in Patients With Hypertrophic Cardiomyopathy
ClinicalTrials.gov study NCT03550573. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Deep Learning-Based Multidimensional Body Composition Mapping for Outcome Prediction in HCC Patients Undergoing TACE
ClinicalTrials.gov study NCT07235410. IPD Sharing: NO. Countries: 1. Publications: 0.
Gd-EOB-DTPA-enhanced T1 Map for Predicting Postoperative Liver Failure
ClinicalTrials.gov study NCT05592106. IPD Sharing: NO. Countries: 1. Publications: 0.
A Study on Predicting the Risk of Distant Metastasis in Breast Cancer Using AI-Generated Spatial Pathological Maps
ClinicalTrials.gov study NCT07244094. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Noninvasive 3D Mapping in Persistent Atrial Fibrillation, to Describe Modifications of the Arrhythmogenic Substrate After Pulmonary Vein Isolation and Identify Potential Predicting Factors of Ablation
ClinicalTrials.gov study NCT04229160. IPD Sharing: NO. Countries: 1. Publications: 0.
MAP Autism Prediction Study
ClinicalTrials.gov study NCT04672967. IPD Sharing: NO. Countries: 1. Publications: 0.
MAP THE SMA: a Machine-learning Based Algorithm to Predict THErapeutic Response in Spinal Muscular Atrophy
ClinicalTrials.gov study NCT05769465. IPD Sharing: Not stated. Countries: 1. Publications: 0.
A high resolution organ expression map reveals novel expression patterns and predicts cellular function
GEO Series GSE8934. Arabidopsis thaliana. 33 samples. Type: Expression profiling by array.
Cardiac T1 Mapping Enables Risk Prediction of LV Dysfunction After Surgery for Aortic Regurgitation
ClinicalTrials.gov study NCT05332184. IPD Sharing: UNDECIDED. Countries: 0. Publications: 0.
Lymphatic Mapping, Sentinel Lymph Node Analysis, and Blood Tests in Detecting and Predicting Early Micrometastases in Patients With Colorectal Cancer
ClinicalTrials.gov study NCT00625625. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Figure 70 in Predicted and recorded distribution maps of the genus Phanaeus (Coleoptera: Scarabaeidae). Supplementary material of Distribution, Regionalization, and Diversity of the dung beetle genus Phanaeus MacLeay (Coleoptera: Scarabaeidae) using Species Distribution Models
Figure 70: Predicted distribution of Phanaeus triangularis species group.
Figure 63 in Predicted and recorded distribution maps of the genus Phanaeus (Coleoptera: Scarabaeidae). Supplementary material of Distribution, Regionalization, and Diversity of the dung beetle genus Phanaeus MacLeay (Coleoptera: Scarabaeidae) using Species Distribution Models
Figure 63: Predicted and recorded distribution of Phanaeus quadridens.
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.