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66 results for “graph model”
Data for paper "Graph Deep Learning Model for Mapping Mineral Prospectivity"
<p>Four prospecting information, namely, NE- and NW- trending faults, Agno Batholithic pluton margins, and porphyry intrusive contacts for mineral prospectivity mapping in Baguio district, Philippines.</p>
Incremental Model Transformations with Triple Graph Grammars for Multi-version Models and Multi-version Pattern Matching Evaluation Data
<p>Java abstract syntax graphs for two software development projects in non-recreating multi-version model encoding.</p>
The features of Tissues and Patches for "Predicting microsatellite instabilitiy from histology images with a three-level hierarchical graph fusion model"
<p>This repository contains features and corresponding coordinates of patches and tissues extracted from 430 and 326 histologic images from patients with colorectal and gastric cancers from the TCGA cohort (original whole section SVS images are freely available at https://portal.gdc.cancer.gov/). All images in this library are from formalin-fixed paraffin-embedded (FFPE) diagnostic sections (“DX” on the GDC Data Portal). This blog explains this in detail: http://www.andrewjanowczyk.com/download-tcga-digital-pathology-images-ffpe/</p> <p><strong>Preprocessing.</strong></p> <p>All SVS slices were pre-processed as follows.</p> <p>According to “Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer” these histology images were categorized into The histology images were classified as “MSS” (microsatellite stable) or “MSIMUT” (microsatellite unstable or highly mutated) according to “Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer”, which corresponds to the division of the training and test sets in the article.<br><br></p> <p>Patches were extracted at 40x objective magnification and 20x objective magnification, respectively, and the corresponding features were extracted by pre-training resnet48, respectively</p> <p>The features of Tissues are thumbnails obtained at 2.5x objective magnification and further extracted by MedSAM after extracting the masks of the tissues.</p>
Cell features for "Datasets for "Predicting microsatellite instabilitiy from histology images with a three-level hierarchical graph fusion model""
<p>This repository contains features and corresponding coordinates of cells extracted from 430 and 326 histologic images from patients with colorectal and gastric cancers from the TCGA cohort (original whole section SVS images are freely available at https://portal.gdc.cancer.gov/). All images in this library are from formalin-fixed paraffin-embedded (FFPE) diagnostic sections (“DX” on the GDC Data Portal). This blog explains this in detail: http://www.andrewjanowczyk.com/download-tcga-digital-pathology-images-ffpe/</p> <p><strong>Preprocessing.</strong></p> <p>All SVS slices were pre-processed as follows.</p> <p>According to “Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer” these histology images were categorized into “MSS” (microsatellite stable) or “MSIMUT” (microsatellite unstable or highly mutated) and corresponded to the article dividing the training and test sets.<br><br></p> <p>The features of all cells were extracted by Hovernet and Transnuseg at 40x objective magnification for extraction masking and further feature extraction</p>
Cytoscape session for the potato knowledge graph extracted with IBM Watson's supervised NLP model
<p>2 cytoscape session files (.cys) representing the genotypic-phenotypic knowledge networks retrieved from scientific literature using IBM Watson. Input to these files were the following: </p> <ul> <li>cytoscapeSession_trainingSet: A training set of 34 full-test articles about potato flesh color</li> <li>cytoscapesession_testSet: A testing set of a 4023 abstracts from PubMed.</li> </ul> <p> </p>
MvGraphDTA: Multi-view-based graph deep model for drug-target affinity prediction by introducing the graphs and line graphs
<h1>MvGraphDTA</h1> <p>MvGraphDTA:通过引入图形和折线图,基于多视图的图形深度模型用于药物-靶点亲和力预测</p> <h2>要求</h2> <p><span><span>numpy</span></span><span>==1.23.5</span></p> <p><span><span>pandas</span></span><span>==1.5.2</span></p> <p><span><span>biopython</span></span><span>==1.79</span></p> <p><span><span>scipy</span></span><span>==1.9.3</span></p> <p><span><span>torch</span></span><span>==2.0.1</span></p> <p><span><span>torch_geometric</span></span><span>==2.3.1</span></p> <h2>示例用法</h2> <h3>1. 使用我们的预训练模型</h3> <p>在本节中,我们提供了 pdbbindv2016 的核心集数据和 Li 的数据(过滤后的 casf2013 和 casf2016),您可以直接执行以下命令来运行我们的预训练模型并在核心集上获取结果。</p> <pre># Run the following command.<br>python test_pretrain.py</pre> <h3>2. 在数据集上运行</h3> <p>在本节中,您必须提供药物的 .sdf 文件以及靶标的 .pdb 文件。</p> <div> <p># 您可以通过运行以下命令获取药物和靶点的图形和折线图。<br>Python data_process.py</p> <p># 当所有数据都准备好后,您可以通过运行以下命令来训练自己的模型。<br>Python training.py</p> </div>
Data and code from: Improving accuracy and reproducibility of cartilage T2 mapping in the OAI dataset through extended phase graph modeling
<p>The repository contains the OAI data used to run the experiments reported in the study "Improving accuracy and reproducibility of cartilage T2 mapping in the OAI dataset through extended phase graph modeling", along with their segmentation and processed T2 maps with the different fitting algorithms.</p> <p>The <em><strong>code_repository</strong></em> folder contains a snapshot of the <a href="https://github.com/barma7/EPGfit_for_cartilage_T2_mapping">GitHub repository</a> at the time of the submission of the manuscript. Please visit the GitHub repository for the latest version. </p>
Topologies, Checkpoints, and Configurations for the paper "GVI-RL: Graph-Invariant RL for Attack Paths Discovery using Vulnerabilities Embedded with Large Language Models"
<p>This repository consists of the <strong>files</strong> related to the <strong>paper</strong> "GVI-RL: Graph-Invariant RL for Attack Paths Discovery using Vulnerabilities Embedded with Large Language Models". In particular, this repository contains tensorboard logs, topologies, checkpoints, seeds, and results to ensure reproducibility of the results of the paper.</p> <p>The results included are related to the training/validation and hyper-parameters optimization of the outcome multi-label classifier, the GVI-RL agent, and the world model.<br>The data folder contains also the topologies used in the study, the vulnerabilities data used to generate them and the dataset for multi-label classification.</p> <p>The README.md file describes the folders' structure.</p>
Graph neural network emulator for modeling of ice dynamics and calving in the Pine Island Glacier, Antarctica
<p>These files include the following codes and datasets for developing graph neural network (GNN) emulators for the Ice-sheet and Sea-level System Model (ISSM) for modeling ice sheet dynamics and calving in the Pine Island Glacier, Antarctica</p> <ul> <li>ISSM_DGL_PIG2.py: Python file for training GNN models (*single.py: code for single GPU environment)</li> <li>ISSM_CNN_PIG.py: Python file for training convolutional neural network (CNN) models</li> <li>*.mat: Datasets of the ISSM transient simulation results (graphs for GNNs)</li> <li>*.pkl: Datasets of the ISSM transient simulation results (grids for CNNs)</li> </ul>
Video simulations for paper "Rapid Spatio-Temporal Flood Modelling via Hydraulics-Based Graph Neural Networks"
<p>Videos of the comparison between numerical and deep learning simulations for test datasets 1, 2, and 3 for paper "Rapid Spatio-Temporal Flood Modelling via Hydraulics-Based Graph Neural Networks".</p>
Raw datasets for paper "Rapid Spatio-Temporal Flood Modelling via Hydraulics-Based Graph Neural Networks"
<p>Raw datasets for paper "Rapid Spatio-Temporal Flood Modelling via Hydraulics-Based Graph Neural Networks".</p> <p>The zip folder comprises 4 subfolders (DEM, WD, VX, VY), containing the elevation, water depths in time, and velocities (in x and y directions) in time for all training and testing simulations. The overview.csv file provides the runtime of the numerical model on each different simulation, identified by its id.</p> <p>The simulations ids are divided as follows:</p> <p>- 1-80: Training and validation</p> <p>- 501-520: Testing dataset 1</p> <p>- 10001-10020: Testing dataset 2</p> <p>- 15001-15020: Testing dataset 3</p>
The performance of permutations and exponential random graph models when analysing animal networks (R code and data)
<p>Social network analysis is a suite of approaches for exploring relational data. Two approaches commonly used to analyse animal social network data are permutation-based tests of significance and exponential random graph models. However, the performance of these approaches when analysing different types of network data has not been simultaneously evaluated. Here we test both approaches to determine their performance when analysing a range of biologically realistic simulated animal social networks. We examined the false positive and false negative error rate of an effect of a two-level explanatory variable (e.g. sex) on the number and combined strength of an individual's network connections. We measured error rates for two types of simulated data collection methods in a range of network structures, and with/without a confounding effect and missing observations. Both methods performed consistently well in networks of dyadic interactions, and worse on networks constructed using observations of individuals in groups. Exponential random graph models had a marginally lower rate of false positives than permutations in most cases. Phenotypic assortativity had a large influence on the false positive rate, and a smaller effect on the false negative rate for both methods in all network types. Aspects of within- and between-group network structure influenced error rates, but not to the same extent. In grouping-event based networks, increased sampling effort marginally decreased rates of false negatives, but increased rates of false positives for both analysis methods. These results provide guidelines for biologists analysing and interpreting their own network data using these methods.</p>
S-Cypher: A Temporal Query Language on the Temporal Property Graph Model
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HiPHD: Hierarchical Classification for Protein Remote Homology Detection using Graph Neural Networks and Language Models
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Pretraining Graph Transformers with Atom-in-a-Molecule Quantum Properties for Improved ADMET Modeling
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Graph Positional and Structural Encodings (model weights and precomputed encodings)
<p>Model weights for pre-trained GPSE model and selected pre-computed GPSE encodings using the model pre-trained on MolPCBA</p> <p> </p>
The performance of permutations and exponential random graph models when analysing animal networks (R code and data)
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Embeddings of KG-COVID-19 knowledge graph (Aug 12 build), produced using node2vec, skipgram model, p=q=1, walk length = 100, num walks = 20
<p>Embeddings of KG-COVID-19 knowledge graph (Aug 12 build), produced using Embiggen, node2vec, skipgram model, p=q=1, walk length = 100, num walks = 20</p>
Embeddings of KG-COVID-19 knowledge graph (Aug 12 build), 80/20 training/test split, produced using node2vec, skipgram model, p=q=1, walk length = 100, num walks = 20
<p>KG-COVID-19 embedding data from Sep 8, 2020 experiment, for training/test split of 80/20: </p> <p>These embeddings and weights were produced from this notebook on or around Sep 8, 2020:</p> <p>https://github.com/justaddcoffee/kg_covid_19_drug_analyses/blob/master/Graph%20embedding%20using%20SkipGram%20homogeneous%20graph.ipynb</p> <p>SkipGram_80_20_training_test_epoch_500_delta_0.0001_embedding.npy<br> SkipGram_80_20_training_test_epoch_500_delta_0.0001_weights.h5</p> <p>I'm also including two runs just before this, with different epoch number and delta values:</p> <p>SkipGram_80_20_training_test_embedding_sep_6_2020_epoch_200_delta_0.001.npy</p> <p>SkipGram_80_20_training_test_weights_sep_6_2020_epoch_200_delta_0.001.h5</p> <p>SkipGram_80_20_training_test_embedding_sep_7_2020_epoch_200_delta_0.0001.npy<br> SkipGram_80_20_training_test_weights_sep_7_2020_epoch_200_delta_0.0001.h5</p>
Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modeling
<p>Dataset for ICLR 2021 submission "Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modeling".</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.