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125 results for “Structure learning”
Dataset for paper [Application of Deep Learning in Generating Structured Radiology Reports: A Transformer-Based Technique]
<p>Since radiology reports needed for clinical practice and research are written and stored in free-text narrations, extraction of relative information for further analysis is difficult. In these circumstances, natural language processing (NLP) techniques can facilitate automatic information extraction and transformation of free-text formats to structured data. In recent years, deep learning (DL)-based models have been adapted for NLP experiments with promising results. Despite the significant potential of DL models based on artificial neural networks (ANN) and convolutional neural networks (CNN), the models face some limitations to implement in clinical practice. Transformers, another new DL architecture, have been increasingly applied to improve the process. Therefore, in this study, we propose a transformer-based fine-grained named entity recognition (NER) architecture for clinical information extraction. We collected 88 abdominopelvic sonography reports in free-text formats and annotated them based on our developed information schema. The text-to-text transfer transformer model (T5) and Scifive, a pre-trained domain-specific adaptation of the T5 model, were applied for fine-tuning to extract entities and relations and transform the input into a structured format. Our transformer-based model in this study outperformed previously applied approaches such as ANN and CNN models based on ROUGE-1, ROUGE-2, ROUGE-L, and BLEU scores of 0.816, 0.668, 0.528, and 0.743, respectively, while providing an interpretable structured report.</p>
Detection and tracking of turbulent structures in the edge of tokamak plasmas using machine learning
<p>This repository contains the data used in the study entitled "Detection and tracking of turbulent structures in the edge of tokamak plasmas: use of an ultra-fast camera and comparison of machine learning and Kalman filter methods". The data was collected using an ultra-fast camera on the COMPASS device.</p> <p><strong><em>This repository includes:</em></strong></p> <p><strong>Fast passive imaging data:</strong> High-frequency captures of turbulent structures present in the edge of tokamak plasmas.<br><strong>Labels :</strong> Annotation of turbulent structures for training and validation of detection and tracking algorithms.<br><strong>Detection and tracking results using YOLO:</strong> Outputs from the YOLO (You Only Look Once) model applied to turbulent data.<br><br><strong><em>Objective</em></strong><br>This dataset is intended to provide a complete and reproducible set for research into the detection and tracking of turbulent structures in tokamak plasmas. It can be used to compare the performance of machine learning methods with traditional techniques, and to encourage further research in this crucial area for plasma physics and nuclear fusion.</p> <p><br>Researchers are encouraged to use these data to:</p> <p>Replicate the results of the original study.<br>Develop and test new techniques for detecting and tracking turbulent structures.Compare the performance of machine learning algorithms with conventional methods.</p> <p><br>References Please cite the repository as follows: S. Chouchene et al., (2024). Detection and tracking of turbulent structures in the edge of tokamak plasmas using machine learning. Zenodo. https://doi.org/10.5281/zenodo.12608068</p>
dataset for "Rapid Prediction of Liquid Structure from a Single Molecular Configuration Using Deep Learning"
<p>The data sets are used to train, validate, and test the PointNet-MD model for rapid prediction of radial distribution functions for different liquids. Details about this work can be found in the <a href="https://pubs.acs.org/doi/10.1021/acs.jcim.3c00472">paper</a> entitled <a href="https://pubs.acs.org/doi/10.1021/acs.jcim.3c00472"><em><span>Rapid Prediction of a Liquid Structure from a Single Molecular Configuration Using Deep Learning</span></em></a>. </p>
Gene expression profile Predictor on chemical Structures (GPS): Deep Learning-based platform to screen and design novel therapeutics
GEO Series GSE291867. Homo sapiens. 27 samples. Type: Expression profiling by high throughput sequencing.
Inactive-enriched machine-learning models exploiting patent data improve structure-based virtual screening for PDL1 dimerizers
<p>The 12 VS scenarios considered in this study employing six training-test data partitions<strong> </strong>(A-F). All training sets employ the same set of 371 actives (WO2015160641A2), but differ on the considered set of inactives and hence are uniquely identified by the latter (either TrueInactives, DeepCoys, RandomDecoys or ActivesOnly). Likewise, all test sets employ the same 297 actives (WO201503820A1), none of them also included in the training set, but different sets of inactives (TrueInactives or DeepCoys). </p> <p> </p> <table align="center"> <caption>Table 1. Six virtual screening scenarios corresponding to six pairs of training-test data for each type of SFs (classification or regression)</caption> <thead> <tr> <th scope="col">Partition ID</th> <th scope="col">Training set</th> <th scope="col">Test set</th> <th scope="col">Type</th> </tr> </thead> <tbody> <tr> <td>A</td> <td>DeepCoys</td> <td>TrueInactives</td> <td>Classification</td> </tr> <tr> <td>B</td> <td>RandomDecoys</td> <td>TrueInactives</td> <td>Classification</td> </tr> <tr> <td>C</td> <td>ActivesOnly</td> <td>TrueInactives</td> <td>Classification</td> </tr> <tr> <td>D</td> <td>TrueInactives</td> <td>DeepCoys</td> <td>Classification</td> </tr> <tr> <td>E</td> <td>RandomDecoys</td> <td>DeepCoys</td> <td>Classification</td> </tr> <tr> <td>F</td> <td>ActivesOnly</td> <td>DeepCoys</td> <td>Classification</td> </tr> <tr> <td>A</td> <td>DeepCoys</td> <td>TrueInactives</td> <td>Regression</td> </tr> <tr> <td>B</td> <td>RandomDecoys</td> <td>TrueInactives</td> <td>Regression</td> </tr> <tr> <td>C</td> <td>ActivesOnly</td> <td>TrueInactives</td> <td>Regression</td> </tr> <tr> <td>D</td> <td>TrueInactives</td> <td>DeepCoys</td> <td>Regression</td> </tr> <tr> <td>E</td> <td>RandomDecoys</td> <td>DeepCoys</td> <td>Regression</td> </tr> <tr> <td>F</td> <td>ActivesOnly</td> <td>DeepCoys</td> <td>Regression</td> </tr> </tbody> </table> <p> </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.