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59 results for “graph neural networks”
Data for "Learning Collective Cell Migratory Dynamics from a Static Snapshot with Graph Neural Networks"
<p>This dataset contains snapshots of cell monolayers, represented as graphs, along with their corresponding average displacement measurements.</p>
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>
Multivariate prediction on wake-affected wind turbines using graph neural networks (Eurodyn) database
<p>Database consisting of graphs generated using randomized layouts and PyWake simulations used in '<em>Multivariate prediction on wake-affected wind turbines using graph neural networks</em>', contribution to Eurodyn 2023. </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>
Research on key generic technology prediction based on graph neural networks under the perspective of patent citation - An example from the field of genetic engineering
<p>In this research, we adopted graph neural network models for key generic prediction based on cited patent data. Through the construction of the patent citation network and the design of a key generic evaluation system, 20879 relevant patents and 51,610 irrelevant patents were screened out. Further, we utilized the LDA topic model to interpret technical topics at a finer granularity. Finally, to test the effectiveness of this method, we took the field of genetic engineering as an example for key generic technology prediction, with an accuracy rate of 95%.</p>
Data for Evaluating the generalizability of graph neural networks for predicting collision cross section
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Knowledge Graph Neural Network with Spatial-Aware Capsule for Drug-Drug Interaction Prediction
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HiPHD: Hierarchical Classification for Protein Remote Homology Detection using Graph Neural Networks and Language Models
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MuGNN: API Misuse Detection using Graph Neural Networks and Clustering
<div> <div>This artifact presents `MuGNN`, a novel framework for efficiently detecting API misuse in Java code. The approach leverages a `Graph Neural Network (GNN)` model to generate embeddings of Java API usage code using a custom `API Flow Graph (AFG)` representation. This representation captures execution sequences, data flow, and control flow, enabling better understanding of API usage patterns. MuGNN employs self-supervised pre-training and clustering to analyze API usage and identify potential misuse.</div> </div>
Key generic technology prediction in patent citation using graph neural networks
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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>
Dataset related to article: Equivariant graph neural network interatomic potential for Green-Kubo thermal conductivity in phase change materials
<p>This repository contains the dataset to train and test the GeTe Machine Learning Interatomic Potential (MLIP). The computational details are given in the manuscript. </p>
Dataset from: Structure-based prediction of protein-nucleic acid binding using graph neural networks
<p>Datasets used for training/evaluating the neural network models described in our article. Additional documentation related to how these datasets were constructed can be found in the github repository https://github.com/jaredsagendorf/pnabind/tree/master/datasets</p>
Dataset for efficient modelling of ionic and electronic interactions by resistive memory- based reservoir graph neural network
<p>Dataset for training the resistive memory-based reservoir graph neural network.</p> <p>In the atomic force calculation experiment, <span lang="EN-HK"><span>a Li</span><sub>3</sub><span>PO</span><sub>4</sub><span> dataset is derived from the melting and quenching trajectory via AIMD simulations. The training, validation, and testing datasets consist of 40,000, 5,000, and 5,000 samples, respectively. </span></span></p> <p><span lang="EN-HK"><span>In the Hamiltonian calculation, a dataset </span><span lang="EN-HK">of various graphene (72 atoms) configurations are generated by AIMD simulations at room temperature, with Hamiltonian data calculated via the OpenMX code</span><span lang="EN-HK">.</span><span lang="EN-HK"> <span>The training, validation, and testing datasets consist of 270, 90, and 90 samples (including atomic structure and Hamiltonian matrix), respectively.</span></span></span></p> <p>Code: https://github.com/hustmeng/RGNN.git</p> <p>1-Atomic_force_dataset.zip and 2-Hamiltonian_dataset.zip are original data.</p> <p>3-Graph_atomic_force.zip and 4-Graph_training_Hamiltonian.zip are graphs. </p> <p> </p> <p>References:</p> <p> </p> <p>1. C.W. Park, M. Kornbluth, J. Vandermause, C. Wolverton, B. Kozinsky, J.P. Mailoa, Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture, npj Comput. Mater. 7(1) (2021) 73. https://github.com/ken2403/gnnff.git</p> <p>2. H. Li, Z. Wang, N. Zou, M. Ye, R. Xu, X. Gong, W. Duan, Y. Xu, Deep-learning density functional theory Hamiltonian for efficient ab initio electronic-structure calculation, Nat. Comput. Sci. 2(6) (2022) 367-377. https://github.com/mzjb/DeepH-pack.git</p> <p>3. D. Pfau, J.S. Spencer, A.G.D.G. Matthews, W.M.C. Foulkes, Ab initio solution of the many-electron Schrödinger equation with deep neural networks, Phys. Rev. Res. 2(3) (2020) 033429. https://github.com/google-deepmind/ferminet.git</p> <p> </p>
Graph neural networks for predicting metal–ligand coordination of transition metal complexes
<p>Supporting Information data for associated publication "Graph neural networks for predicting metal–ligand coordination of transition metal complexes".</p>
Graph-pMHC: Graph Neural Network Approach to MHC Class II Peptide Presentation and Antibody Immunogenicity
<p>Antigen presentation on MHC Class II (pMHCII presentation) plays an essential role in the adaptive immune response to extracellular pathogens and cancerous cells. But it can also reduce the efficacy of large-molecule drugs by triggering an anti-drug response. Significant progress has been made in pMHCII presentation modeling due to the collection of large-scale pMHC mass spectrometry datasets (ligandomes) and advances in machine learning. Here, we develop graph-pMHC, a graph neural network approach to predict pMHCII presentation. We derive adjacency matrices for pMHCII using Alphafold2-multimer, and address the peptide-MHC binding groove alignment problem with a simple graph enumeration strategy. We demonstrate that graph-pMHC dramatically outperforms methods with suboptimal inductive biases, such as the multilayer-perceptron-based NetMHCIIpan-4.0 (+20.17% absolute average precision). Finally, we create an antibody drug immunogenicity dataset from clinical trial data, and develop a method for measuring anti-antibody immunogenicity risk using pMHCII presentation models. Our model increases ROC AUC by 2.57% compared to just filtering peptides by hits in OASis alone for predicting antibody drug immunogenicity.<br><br>NOTE!!</p> <p>It's been brought to my attention that I accidentally shuffled the graph-pmhc and netmhciipan predictions on the antibody immunogenicity dataset (AB_df_w_preds), zenodo is not allowing me to add a new version. Besides these prediction columns the data is good, so the ada labels for the antibodies is fine. The graph-pmhc and netmhciipan predictions can be derived from AB_df_all_preds_w_preds with code like this:</p> <p>df.groupby('Antibody').apply(lambda x: sum((x['Peptide Num OAS Subjects']<23)&(x[column]>0))).values</p> <p>Where df is AB_df_all_preds_w_preds loaded in pandas, and column is the prediction column (graph-pmhc or netmhciipan) of interest. Sorry about the error!!</p>
Graph Neural Network vs. Large Language Model: A Comparative Analysis for Bug Report Priority and Severity Prediction
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Structure classification of glass-forming liquids by graph neural networks: Explaining predictions with the Self-Attention mechanism
<p>This repository includes the dataset and Python scripts used in the article, "Structure classification of glass-forming liquids by graph neural networks: Explaining predictions with the Self-Attention mechanism". The repository also includes source data of figures in the article.</p>
ICPP_22_Power_Constrained_Autotuning_using_Graph_Neural_Networks
<p>Dataset for paper submission to ICPP 22</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.