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1,273 results for “Deep Learning”
Small molecules targeting the structural dynamics of AR-V7 partially disordered protein using deep learning and physics based models.
<p>Partially disordered proteins can contain both stable and unstable secondary structure segments and are involved in various (mis)functions in the cell. The extensive conformational dynamics of partially disordered proteins scaling with extent of disorder and length of the protein hampers the efficiency of traditional experimental and in-silico structure-based drug discovery approaches. Therefore new efficient paradigms in drug discovery taking into account conformational ensembles of proteins need to emerge. In this study, using as a test case the AR-V7 transcription factor splicing variant related to prostate cancer, we present an automated methodology that can accelerate the screening of small molecule binders targeting partially disordered proteins. By swiftly identifying the conformational ensemble of AR-V7, and reducing the dimension of binding-sites by a factor of 90 by applying appropriate physicochemical filters, we combine physics based molecular docking and multi-objective classification machine learning models that speed up the screening of thousands of compounds targeting AR-V7 multiple binding sites. Our method not only identifies previously known binding sites of AR-V7, but also discovers new ones, as well as increases the multi-binding site hit-rate of small molecules by a factor of 17 compared to naive physics-based molecular docking. </p>
Predicting glycan structure from tandem mass spectrometry via deep learning
<p>Curated set of LC-MS/MS data from glycomics studies. Used for training and applying CandyCrunch, a deep learning model to predict glycan structure from LC-MS/MS data, described in Urban et al., Nat Methods, 2024 and https://github.com/BojarLab/CandyCrunch.</p> <p>Files:</p> <p>full_dataset.xlsx: Full dataset with all annotated LC-MS/MS glycan spectra</p> <p>X_train.pkl: spectra and metadata from our training set</p> <p>y_train.pkl: labels from our training set</p> <p>X_test.pkl: spectra and metadata from our independent test set</p> <p>y_test.pkl: labels from our independent test set</p> <p>glycans.pkl: glycans in IUPAC-condensed nomenclature in the same order as the label-encoding</p>
The Data for Cloud Removal in Full-disk Solar Images Using Deep Learning
<p><em>Cloud Removal in Full-disk Solar Images Using Deep Learning. For more information, please refer to README.md.</em></p> <p><em>Please note that the data and model parameters zip is not available in github. Please download them in this file.</em></p> <p>If you need help, feel free to contact me at <a href="mailto:szh@kust.edu.cn" target="_blank" rel="noopener">szh@kust.edu.cn</a> or <a href="mailto:dp24163@163.com" target="_blank" rel="noopener">dp24163@163.com</a>.</p>
Future Projections of Temperature Extremes and Urban Heat Island in Paris using Deep Learning
<p>Future projections of 2-meter maximum and minimum temperature and land surface temperature in Paris, France, using Deep Learning, under four Shared Socioeconomic Pathways. ERA5 and GCM ensemble data at their original resolution are also included. The DL (Convolutional Neural Network) model architecture and trained weights are also available. The Python script to generate the boxplots of the future projections is also included.</p>
Dataset related to the publication "Accelerating Pinned Specimen Digitization: A Deep Learning Pipeline for Collaborative Robots"
<p>The dataset related to the publication "Accelerating Pinned Specimen Digitization: A Deep Learning Pipeline for Collaborative Robots". 250 training images and 50 annotated test images (YOLOv8 format).</p>
Data from: Coronary artery segmentation in non-contrast calcium scoring CT images using deep learning
<p><strong>Abstract</strong></p> <p>Precise segmentation of coronary arteries in non-contrast Computed Tomography (CT) scans plays an important role in the assessment of the coronary artery disease, where it is the key component for evaluating the Calcium Score (Agatston et al. 1990). In the paper by Bujny et al. (2024), a deep-learning approach for high-precision segmentation of coronary arteries in non-contrast CT was proposed along with a novel method for generating Ground Truth (GT) test data (<em>test-GT</em>) via manual registration of high-resolution coronary tree models obtained based on contrast CT with the non-contrast CT scans. In this dataset, we present the inferences of the neural network model together with the corresponding <em>test-GT</em> samples, based on 6 CT scans from the openly available OrCaScore dataset (Wolterink et al. 2016). The geometrical models included in the dataset can be used both for inspection of the proposed deep learning model and for testing of new non-contrast coronary vessel segmentation approaches, which is a unique opportunity since, to the best of our knowledge, manual generation of GT for non-contrast coronary artery segmentation was not addressed so far due to very challenging character of this particular segmentation task.</p> <p> </p> <p><strong>Methods</strong></p> <p><strong><em>Manual Generation of test-GT</em></strong></p> <p>The geometric models of coronary arteries used for the evaluation of the proposed neural network model were generated according to the manual mesh-to-image registration process as described by Bujny et al. (2024). In this approach, the high-resolution coronary artery masks obtained based on contrast CT scans are manually aligned with the corresponding non-contrast CT images using tools available in the open-source 3D computer graphics software, Blender (<a href="https://www.blender.org/">https://www.blender.org/</a>). To ease the manual alignment process, specialized add-ons for medical image processing such as Cardiac add-on for Blender of Graylight Imaging (<a href="https://graylight-imaging.com/3d-modelling/">https://graylight-imaging.com/3d-modelling/</a>) can be used, as well. The STL models in this dataset were manually generated by a medical expert with 4 years of experience.</p> <p><strong><em>Segmentation of Coronary Arteries using a Deep Learning Model</em></strong></p> <p>For each of the cases presented in this dataset, we run an inference of an nnU-Net (Isensee et al. 2021) model trained according to the process described in our paper (Bujny et al. 2024). Since we use a standard nnU-Net, which utilizes a sliding window approach for processing of the CT scan, the context information within a patch is limited, which can lead to some false-positive detections. To mitigate this problem, we additionally post-process the inferences by eliminating small vessel fragments of less than 50 [mm^3] volume and structures outside of pericardium, which we segment using another nnU-Net model, SegTHOR (Lambert et al. 2020). The resulting geometric models are stored using the STL format and presented as green masks in the HTML reports with an embedded viewer based on the K3D-jupyter library (<a href="https://k3d-jupyter.org/">https://k3d-jupyter.org/</a>).</p> <p> </p> <p><strong>Dataset organization</strong></p> <p>The root folder contains 6 folders whose names correspond to the CT scans from the OrCaScore dataset (Wolterink et al. 2016). In each of the folders, there are the following 4 files available:</p> <ul> <li><span>‘manualGT_rater1.stl’ – high-resolution STL model of coronary arteries obtained via manual alignment of the geometric model segmented in contrast CT with the corresponding non-contrast CT scan by the first rater.</span> A sample belonging to the <em>test-GT</em> set (Bujny et al. 2024).</li> <li>‘manualGT_rater2.stl’ – corresponding <em>test-GT</em> sample by the second rater.</li> <li>‘ML.stl’ – post-processed inference of the nnU-Net ML model in the STL format.</li> <li>‘report.html’ – interactive HTML report consisting of a manually-aligned <em>test-GT</em> sample (red mask), the ML segmentation based on the non-contrast CT scan (green mask), and selected slices of the non-contrast CT scan. The reports contain the relevant information related to the scanning device and present the main segmentation quality metrics for the ML model inference.</li> </ul>
Microscopy Imaging Dataset: Trypanosoma brucei Bloodstream Form Classification Using Deep Learning
<p>This dataset provides a comprehensive collection of microscopic images and associated labels, specifically designed to facilitate the automated classification of <em>Trypanosoma brucei</em> bloodstream forms—slender and stumpy. Accurate differentiation of these life cycle stages is vital for understanding the parasite's biology, transmission dynamics, and adaptation mechanisms in its mammalian host.</p> <p><strong>Contents:</strong></p> <ul> <li><strong>Image Data</strong>: Microscopic images of <em>T. brucei</em> bloodstream forms captured under standard imaging conditions, encompassing a broad array of image quality, cellular arrangements, and morphological characteristics.</li> <li><strong>Label Data</strong>: Annotation files for each image, specifying cellular forms as slender or stumpy, essential for supervised machine learning applications.</li> <li><strong>Supplementary Files</strong>: Additional Excel files providing information on training, testing, and validation splits, alongside test results for model evaluation.</li> </ul> <p><strong>Purpose:</strong></p> <p>This dataset serves as a valuable resource for researchers in parasitology, machine learning, and computational biology. It supports investigations into the biology and life cycle of <em>T. brucei</em>, while also providing a robust testbed for developing, validating, and benchmarking image processing and classification algorithms tailored to parasite morphology.</p> <p><strong>Data Collection and Methodology:</strong></p> <p>The dataset was compiled using advanced deep learning techniques, integrating the Cellpose segmentation algorithm with a custom-trained Xception model optimized for classifying <em>T. brucei</em> forms. The model achieved 97% classification accuracy, demonstrating effective application in handling complex cell images and distinguishing between slender and stumpy forms in challenging imaging conditions.</p> <p><strong>Usage:</strong></p> <p>Researchers are encouraged to use this dataset to:</p> <ul> <li>Analyze and classify the life cycle stages of <em>T. brucei</em> bloodstream forms in microscopic images.</li> <li>Develop and test deep learning models for single-cell image segmentation and classification.</li> <li>Explore cellular morphology patterns and refine machine learning approaches for other single-cell imaging applications.</li> </ul> <p><strong>Citation:</strong></p> <p>Please cite the original dataset if you utilize this resource in your research to acknowledge its contribution to the field.</p> <p><strong>Access and Availability:</strong></p> <p>This dataset is openly available through Zenodo, enabling researchers to download, explore, and apply it in various fields, from parasitology to advanced computational biology.</p>
First Results on Wake Detection in SAR Images by Deep Learning
<p>Supplementary Materials of the paper "First Results on Wake Detection in SAR Images by Deep Learning" published on MDPI - Remote Sensing</p>
Performance Prediction of Deep Learning Applications
<p>The repository includes the source code and the datasets used to predict the performance of Deep Learning applications executed on different environments. The results are included in the AI-SPRINT project deliverable "D2.1 - First release and evaluation of the AI-SPRINT design tools".</p>
Deep-Learning-Based Harmonization and Super-Resolution of Near-Surface Air Temperature from CMIP6 Models (1850-2100)
<p>A long-term (1850-2100) monthly air temperature (tas) product with a spatial resolution of 0.5 degree. This is a merged product from 31 CMIP6 models using the Deep-learning model which reduce bias, spatial downscaling and data merge at the same time,. To facilitate user-friendly access and download the dataset is stored individually for each year in a separate file. These files contain one historical data (1850-2014) , four future scenarios data during 2015-2100 (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5) and four future scenarios data in Australia . The dataset is stored in NetCDF format, containing the variable tas, representing air temperature, produced in centigrade (℃) as a unit. There are three dimensions included in the dataset: longitude, latitude, and time, with the longitude ranging from -179.75E to 179.75E, the latitude from -89.75N to 89.75N. </p>
An Image Dataset for Training Deep Learning Segmentation Models to Identify Karst Sinkholes
<p>The image dataset was prepared for training deep learning image segmentation models to identify karst sinkholes. Information about the work can be found at (https://github.com/mvrl/sink-seg/). The dataset consists of a DEM image, an aerial image, and a binary sinkhole label image in an area in central Kentucky, USA. It also includes four images derived from the DEM image. The image dataset is sourced from publicly available data from Kentucky's Elevation Data & Aerial Photography Program (https://kyfromabove.ky.gov/) and Kentucky LiDAR-derived sinkholes (https://kgs.uky.edu/geomap).</p> <p> </p>
Predictors and predictand for "Repeatable high-resolution statistical downscaling through deep learning"
<p>Predictors and predictand for "Repeatable high-resolution statistical downscaling through deep learning". Predictors from the ERA5 reanalysis and predictand from ReKIS (https://rekis.hydro.tu-dresden.de). Data is saved in ".rda" format, to be read from R.</p>
Extra Testing Data for paper "OC_Finder: A deep learning-based software for osteoclast segmentation, classification, and counting"
<pre>Here we have 9 datasets we used to validate OC_Finder's performance on various imaging settings. The 9 datasets are inside the folder named "9 datasets for validation experiment". Each dataset is composed of image files and csv files for the coordination of osteoclasts and non-osteoclasts that were manually labelled by human examiner. csv files ending "_posi" has coordination of osteoclasts and "_nega" has coordination of non-osteoclasts. Images in dataset #4, #5, #6, #7, #8, and #9 were resized so the scale of the images matched to the OC_Finder's training dataset. Images in original size before resizing are also provided in "Original images before resizing". Detailed capture setting and resizing information of images in each dataset can be found in "capture setting.xlsx". The number of images in each dataset are as following: #1: 18 #2: 18 #3: 18 #4: 36 #5: 36 #6: 36 #7: 16 #8: 16 #9: 16</pre>
A Deep Learning-Based Hybrid Model of Global Terrestrial Evaporation
<p>This repository contains the datasets used in the research article "A Deep Learning-Based Hybrid Model of Global Terrestrial Evaporation".</p> <p>The repository contains the following files: 1) Input - contains all the processed input used for training the deep learning models and the datasets used for creating the figures in the article. 2) Output - contains the final deep learning models and the outputs (evaporation and transpiration stress factor) outputs from the hybrid model developed in the study.</p> <p>Formats: All scripts are in the programming language Python. The datasets are in HDF5 and NetCDF file formats.</p> <p>The codes related to the research article and deep learning model are available in the following repository: https://github.com/akashkoppa/StressNet</p>
deepNIR: Dataset for generating synthetic NIR images and improved fruit detection system using deep learning techniques
<p>In this paper, we present datasets that can be utilised for synthetic near infrared (NIR) image and bounding box level fruit detection system. It is undeniable fact that high-caliber machine learning software frameworks such as Tensorflow or Pytorch and large scale dataset such as ImageNet and COCO, and accelerated GPU hardware support have pushed the limit of machine learning for more than decades.</p> <p>Among these breakthroughs quality dataset is one of important key building blocks that can lead to success in model generalisation and deployment for data-driven deep neural networks. Particularly, synthetic data generation such as generative adversarial networks often requires relatively larger scale data than other supervised approaches. In addition, posing constrains such as geometrical facial constrains in fake face generation or consistent and radiometrically calibrated reflectances from satellite imagery commonly yield better results. We share NIR+RGB dataset that are re-processed from other two public datasets (nirscene and SEN12MS) and our own novel sweetpepper dataset to be able to timely adopt to other following studies.</p> <p>We oversampled from original nirscene dataset at 10, 100, 200, and 400 ratios and total of 127k pair of images. For SEN12MS satellite multispectral dataset, we selected one largest subset; Summer (45k) and All seasons (180k). Our sweetpeppr dataset consists of 1,615 pairs of NIR+RGB images. We demonstrate these NIR+RGB datasets are sufficient to be used for synthetic NIR generation quantitatively and qualitatively. We achieved Frechet Inception Distance (FID) of 11.36, 26.53, and 40.15 for nirscene1, SEN12MS, and sweetpepper dataset respectively.</p> <p>We also release <em>11</em> fruits' bounding box annotations that can be exported as various formats using cloud service. 4 newly added fruits [blueberry, cherry, kiwi, and wheat] compounds 11 novel bounding box dastaset together with our previous work in deepFruits project [apple, avocado, capsicum, mango, orange, rockmelon, strawberry]. The total number of bounding box instances is 162k and all bounding box dataset is ready for use from cloud service. For evaluation of these dataset, Yolov5 single stage detector is exploited and reported impressive mean-average-precision, mAP[0.5:0.95] results of [min:0.49, max:0.812]. We hope these dataset is useful and serves as one of baseline for the following up studies.</p>
Companion for "Understanding Distributed Deep Learning Performance by Correlating HPC and Machine Learning Measurements"
<p>This is the Companion Material for the paper “Understanding Distributed Deep Learning Performance by Correlating HPC and Machine Learning Measurements”, by Ana Luisa Veroneze Solórzano and Lucas Mello Schnorr. The manuscript was approved for publication in the <a href="https://www.isc-hpc.com/research-papers-2022.html">ISC High Performance 2022</a> for the Research Papers session. A public companion is also availabl in GitLab: <a href="https://gitlab.com/anaveroneze/isc2022-companion/">https://gitlab.com/anaveroneze/isc2022-companion</a>.</p> <p> </p>
Multiple Nuclei HeLa cell ground truth images with four labels (nuclear envelope, nucleus, rest of the cell, and background) for deep learning architecture training.
<p>This is a data set that contains <strong>labelled HeLa cell images</strong>, indicating the four different classes - nuclear envelope, nucleus, rest of the cell, and background. Similar ground truth have been published for this data set, but in this case, multiple nuclei have been labelled, whilst previous ones only focused on the central cell (https://doi.org/10.5281/zenodo.3874949)</p> <p>Details of the imaging, preparation and segmentation have been published in:</p> <ul> <li>Cefa Karabağ, Martin L. Jones, Christopher J. Peddie, Anne E. Weston, Lucy M. Collinson, Constantino Carlos Reyes-Aldasoro. Segmentation and Modelling of the Nuclear Envelope of HeLa Cells Imaged with Serial Block Face Scanning Electron Microscopy. <em>J. Imaging</em> <strong>2019</strong>, <em>5</em>(9), 75; <a href="https://doi.org/10.3390/jimaging5090075">https://doi.org/10.3390/jimaging5090075</a></li> <li>Cefa Karabağ, Martin L. Jones, Christopher J. Peddie, Anne E. Weston, Lucy M. Collinson, Constantino Carlos Reyes-Aldasoro. Semantic segmentation of HeLa cells: An objective comparison between one traditional algorithm and four deep-learning architectures, PLOS ONE, <strong>2020</strong>; <a href="https://doi.org/10.1371/journal.pone.0230605">https://doi.org/10.1371/journal.pone.0230605</a></li> <li> <p>Cefa Karabağ, Martin L. Jones, Constantino Carlos Reyes-Aldasoro, Segmentation of the Plasma Membrane of HeLa Cells,<em> J. Imaging</em> <strong>2021</strong>, <em>7</em>(6), 93; <a href="https://doi.org/10.3390/jimaging7060093">https://doi.org/10.3390/jimaging7060093</a></p> </li> </ul> <ul> <li>The data sets are freely available through EMPIAR: http://dx.doi.org/10.6019/EMPIAR-10094 EMPIAR.</li> </ul>
Avoiding high frequency thermoacoustic instabilities in cyrogenic rocket engines using Bayesian deep learning
<p>Destructive high-frequency thermoacoustic instabilities have afflicted liquid propellant rocket engine development for decades. The 90 MW cryogenic liquid oxygen/hydrogen multi-injector research combustor BKD operated by DLR Lampoldshausen is a platform that allows their study under realistic conditions. In this study, we use data from BKD experimental campaigns where the static chamber pressure and reactor-oxidizer ratio were varied such that the first tangential mode of the combustor is excited under some conditions. We train a Bayesian neural network to predict the occurence probability of thermoacoustic instabilities 500 ms in the future, given the power spectra of the most recent 300 ms sample of the dynamic pressure data and mass flowrate control signals as input. The Bayesian nature of our algorithms allow us to work in this "small data" setting where the size of our dataset is restricted by the effort and expense associated with each experimental run, without making overconfident extrapolations. We find that the network is able to accurately forecast the occurence probability of instabilities on unseen experimental runs. We envision that these algorithms will eventually be used online by rocket engine controllers to avoid regions of thermoacoustic instabilities.</p> <p> </p>
Supplementary Dataset for Deep learning based kcat prediction enables improved enzyme constrained model reconstruction
<p>This dataset is the supplementary dataset for the paper "<strong>Deep learning based <em>k</em><sub>cat</sub> prediction enables improved enzyme constrained model reconstruction</strong>". Protein sequence fasta files, deep learning predicted <em>k</em><sub>cat</sub> values, classcial-ecGEMs, DL-ecGEMs and <em>Posterior</em>-mean-ecGEMs for 343 yeast/fungi species are available in this dataset.This repository also contains the computed results for reproducing the figures as model_build_files . The scripts can be found in Github (https://github.com/SysBioChalmers/DLKcat)</p>
Replication package for "To What Extent do Deep Learning-based Code Recommenders Generate Predictions by Cloning Code from the Training Set?
<p>Replication package for "To What Extent do Deep Learning-based Code Recommenders Generate Predictions by Cloning Code from the Training Set?"</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.