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ShareScore release 0.7.1
Dataset results
921 results for “neural networks”
Neural Network Radiation Emulator (KMA/NIMS), May
<p>The dataset is a part of https://doi.org/10.5281/zenodo.5220712 (May)</p>
Model-Independent Learning of Quantum Phases of Matter with Quantum Convolutional Neural Networks
<p>Some data and codes for Model-Independent Learning of Quantum Phases of Matter with Quantum Convolutional Neural Networks.</p>
Convolutional Neural Networks for Scops Owl Sound Classification
<p>These are the training, validation, and testing datasets used in a publication titled "Convolutional Neural Networks for Scops Owl Sound Classification" . Each set consists of WAV files of seven Indonesian scops owl species. If you use this dataset for your research publication please cite the following paper: <a href="https://doi.org/10.1016/j.procs.2020.12.010">https://doi.org/10.1016/j.procs.2020.12.010</a></p>
Geomagnetic datasets of BJI station reconstructed through Artificial Neural Network improved by Genetic Algorithm in 2021
<p>Beijing station established in 1954 is one of the oldest geomagnetic observatories in China, which plays an important role in data exchange, and further provide data or standardization for satellite observation and geomagnetic model construction. With the development of urbanization, the observed data are greatly disturbed by subways, and data disturbed are almost unavailable. The dataset was reconstructed through Artificial Neural Network improved by Genetic Algorithm, including minutely data of three components (D, H and Z) in 2021. This reconstruction method has been proved to be effective.</p>
Mutate and Observe: Utilizing Deep Neural Networks to Investigate the Impact of Mutations on Translation Initiation
<p>Datasets used in the paper.</p>
Learning-induced reorganization of number neurons and emergence of numerical representations in a biologically-inspired neural network
<p>Data published along with paper: <em>Learning-induced reorganization of number neurons and emergence of numerical representations in a biologically-inspired neural network</em></p> <p>In this data is shared: models (before & after training), stimuli used to train and test, and activity of the model in test.</p>
Med-ReLU: A Hybrid Activation Function Tailored for Deep Artificial Neural Networks in Medical Image Segmentation without Parameter Tuning
<p>Background: Deep learning (DL) is derived from the domain of Artificial Neural Network (ANN). It makes one of the most important elements of deep learning algorithms. Deep learning segmentation models are based on layer-by-layer convolution learning attribute representation directed by forward and backward propagation. Throughout the process vital role is played by appropriately chosen activation function (AF) in order to guarantee the robustness of the model learning. However, the existing activation functions are either ineffective in addressing the vanishing gradient problem or get burdened with multiple parameters that need to be manually tuned. Moreover, the current research on activation function design mainly focuses on classification tasks using natural images from the MNIST, CIFAR-10 and CIFAR-100 datasets. Therefore,Med-ReLU as a novel activation function for medical image segmentation, is proposed. The proposed activation function avoids deep learning models from the attacks of dead neurons or from the vanishing gradient problems. Method: Med-ReLU is a hybrid activation function that combines the property of two activation functions of ReLU and Softsign. For positive inputs, Med-ReLU utilizes the linear property just like ReLU to produce an output without vanishing gradient. The negative inputs converge in polynomial ways towards their asymptotes as property of the softsign AF that ensures robust training processing without the problem of dead neurons that rarely activate across the entire training dataset. Results: The training performance and segmentation accuracy of Med-ReLU have been investigated. The proposed function has demonstrated stable training and does not suffer from over-fitting. Hence, Med-ReLU has consistently outperformed the existing state-of-art activation functions in medical image segmentation tasks. Conclusion: Med-ReLU has been designed as a parameter-free activation function for DL image segmentation tasks. This activation function is easy-to-implement on complex and deep learning models. The utility of this research lies in affirming the impact of Med-ReLU on different Artificial Neural Network architectures and for various kinds of anomaly addressing tasks.</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>
Can Neural Network Instability in Schizophrenia be Improved With a Very Low Carbohydrate Ketogenic Diet?
ClinicalTrials.gov study NCT05268809. IPD Sharing: Not stated. Countries: 1. Publications: 0.
An Observational Clinical Study on the Construction of an Artificial Neural Network Model for ICU Pneumonia
ClinicalTrials.gov study NCT06661499. IPD Sharing: NO. Countries: 1. Publications: 0.
The Quantitative Study of the Habenula Based on Multi-channel Cascaded Neural Network and the Establishment of the Prediction Model of the Curative Effect in Patients With Depression
ClinicalTrials.gov study NCT05872607. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Automatic Estimation of the Apnea-hypopnea Index Using Neural Networks to Detect Sleep Apnea
ClinicalTrials.gov study NCT02143297. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Database Registry for Neural Network Biomarkers in Psychosis
ClinicalTrials.gov study NCT01409109. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Multimodal Magnetoencephalography and Electroencephalography Exploration of the Acute Effects of THC Exposure on Neural Noise and Information Transmission Within Working Memory Networks
ClinicalTrials.gov study NCT05641766. IPD Sharing: NO. Countries: 1. Publications: 0.
Positron Emission Tomography (PET) Images Using Deep Neural Networks
ClinicalTrials.gov study NCT04140565. IPD Sharing: NO. Countries: 1. Publications: 0.
Artificial Neural Network Directed Therapy of Severe Obstructive Sleep Apnea
ClinicalTrials.gov study NCT01286636. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Using NLP and Neural Networks to Autonomously Identify Severe Asthma and Determine Study Eligibility in a Large Healthcare System
ClinicalTrials.gov study NCT06389058. IPD Sharing: NO. Countries: 1. Publications: 0.
Deep Neural Network Approaches for Closed-Loop Deep Brain Stimulation
ClinicalTrials.gov study NCT04277689. IPD Sharing: NO. Countries: 1. Publications: 0.
The Value of a Convolutional Neural Network-Based Renal Artery Perfusion Model in Predicting Renal Function After Partial Nephrectomy: A Prospective Study
ClinicalTrials.gov study NCT06751498. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Research on the Risk Warning Model and Prevention Strategies for Acute Kidney Injury Associated With Cyclosporine Based on Explainable Deep Neural Networks and Therapeutic Drug Monitoring
ClinicalTrials.gov study NCT06596811. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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.