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13,064 results for “predictions”
A Dataset for Engagement Prediction in Neuromotor Disorder Patients during Rehabilitation
<p>This dataset is related to the work titled "Artificial Intelligence Tools for Engagement Prediction in Neuromotor Disorder Patients undergoing Robot-Assisted Rehabilitation," whose aim is to methodologically explore the performance of artificial intelligence algorithms applied to structured datasets made of heart rate variability (HRV) and electrodermal activity (EDA) features to predict the level of patient engagement during robot-assisted gait rehabilitation (RAGR).</p> <p>It is composed of three Excel files related to the 3-minute windows data augmentation scenario applied to the bimodal dataset made of 14 HRV and 19 EDA features. Specifically:</p> <ul> <li>ds_bimodal_win_3min.xlsx contains the features extracted from 3-minute windows of HRV and EDA signals, recorded during the RAGR activity, and normalized with respect to the reference (baseline) signals. Features are not z-scored.</li> <li>labels_self_win_3min contains one single column with, for each row, the label related to the self-perceived engagement classification target. </li> <li>labels_therapist_win_3min contains one single column with, for each row, the label related to the therapist-perceived engagement classification target.</li> </ul> <p>The coding of classes for both classification targets is:</p> <ul> <li>0: Underchallenged</li> <li>1: Minimally Challenged</li> <li>2: Challenged</li> </ul>
Research data for "Predicting Dynamics from Structure in a Sodium Silicate Glass"
<p>This dataset supports the paper "Predicting Dynamics from Structure in a Sodium Silicate Glass".</p> <p>The following files are provided.</p> <p>File: dataset_800.zip</p> <p>- Pickle files for:</p> <ul> <li>400 Sodium silicate glass structures of 3000 atoms</li> <li>30 Trajectories sampled eight times at various timescales up to 1 ns for each if the 400 glass structures</li> </ul> <p>File: in.comb</p> <p>- Lammps inputfile used to generate simulation from with the data in dataset was sampled</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>
Dataset related to the article titled "Prediction of elastic modulus of basaltic rocks using machine learning methods."
<p>Dataset related to the article titled "Prediction of elastic modulus of basaltic rocks using machine learning methods."</p>
Dead Sea Scrolls data collection (images, labels, prediction plots) for dating ancient manuscripts using radiocarbon and AI-based writing style analysis
<p>The dataset is associated with the following article:<br>Title: <strong>Dating ancient manuscripts using radiocarbon and AI-based writing style analysis</strong><br>Authors: Mladen Popović, Maruf A. Dhali, Lambert Schomaker, Johannes van der Plicht, Kaare Lund Rasmussen, Jacopo La Nasa, Ilaria Degano, Maria Perla Colombini, and Eibert Tigchelaar<br><em>(Under review)</em></p> <p>This data set is collected for the ERC project:<br>The Hands that Wrote the Bible: Digital Palaeography and Scribal Culture of the Dead Sea Scrolls<br>PI: Mladen Popović<br>Grant agreement ID: 640497<br>Project website: <a href="https://cordis.europa.eu/project/id/640497">https://cordis.europa.eu/project/id/640497</a></p> <p> </p> <p><strong>Copyright (c) </strong> University of Groningen, 2024. All rights reserved.<br><strong>Disclaimer and copyright notice for all data contained on the *.tar.gz files:</strong></p> <p><strong>1)</strong> permission is hereby granted to use the data for research purposes. It is not allowed to distribute this data for commercial purposes.</p> <p><strong>2) </strong>provider gives no express or implied warranty of any kind, and any implied warranties of merchantability and fitness for purpose are disclaimed.</p> <p><strong>3) </strong>provider shall not be liable for any direct, indirect, special, incidental, or consequential damages arising out of any use of this data.</p> <p><strong>4) </strong>the user should refer to the first public article mentioned above on this data set.</p> <p><strong>5) </strong>the recipient should refrain from proliferating the data set to third parties external to his/her local research group. Please refer interested researchers to this site to obtain their own copy.</p> <p> </p> <p><strong>Organization of the data:<br></strong><em>(Update on 19 April 2024: OxCal data for accepted 2-sigma ranges are updated with the incusion and exclusion of minor peaks. New prediction plots are added after the model is trained with accepted 2-sigma ranges, including minor peaks. The old plots are also kept. <br><br><OLD Updates below; disregard><br>updated on 07-Feb-2024: OxCal data for selected ranges added in a new directory in addition to previously available original OxCal data. Enoch's prediction plots and test images are reorganized for easy access to the users.<br><OLD Updates above; disregard><br><br>Please use the files from this version and disregard the previous two versions: 10.5281/zenodo.10629480 and 10.5281/zenodo.8168210)</em></p> <p>There are four *.tar.gz files:</p> <p><em><strong>C14-Oxcal-data-updated.tar.gz</strong></em> contains one directory with radiocarbon data (OxCal [1] raw data) for all 30 manuscripts. Three additional directories contain name-corrected files for original OxCal data, files with accepted ranges, and files with accepted ranges including minor peaks. Please refer to the original article for details about OxCal data and the manuscripts. 25 out of 30 raw OxCal data are used (accepted ranges only) as the training labels during the training of Enoch, the date prediction model.</p> <p><em><strong>train-images-c14.tar.gz</strong></em> contains the clean and preprocessed (binarized, aligned, and arrangement corrected) training images for the 25 radiocarbon-dated training manuscripts (including 4Q52; 64 images in total). </p> <p><em><strong>test-images-all.tar.gz</strong></em> contains the clean and preprocessed test images for 135 previously undated manuscripts. The images are organized in three different directories: the first one with all 359 images for the 135 manuscripts, the second one with the selected 135 images, and the final one with 25 images to illustrate the poor quality of images. </p> <p><em><strong>Enoch-prediction-new-with-minor-peaks.tar.gz</strong></em> contains the new date prediction plots for each of the 135 test images, where Enoch was trained with the inclusion of minor peaks for the 2-sigma accepted ranges and with a data balancing threshold of 0.05. These plots are used by expert palaeographers' evaluation of Enoch's style-based date predictions of 135 previously undated manuscripts.</p> <p><em><strong>Enoch-predictions.tar.gz</strong></em> contains the date prediction plots for each of the 135 test images. There are two directories inside the *.tar.gz file:<br><br>- <em>prediction-plots-for-selected-135:</em> Prediction plots with data balancing threshold of 0.05. <br>- <em>extra-plots:</em> contains four additional directories:<br> - <em>Enoch-predictions-c14wo4Q52-balanced05:</em> Prediction plots with data balancing threshold of 0.05. <br> - <em>Enoch-predictions-c14wo4Q52-balanced10:</em> Prediction plots with data balancing threshold of 0.1.<br> - <em>Enoch-predictions-c14wo4Q52-unbalanced:</em> Unbalanced raw predictions.<br> - <em>Enoch-predictions-c14wo4Q52-combined:</em> Combined plots with all three prediction plots (unbalanced, 0.05, 0.1).<br>Please refer to the original article for more details.</p> <p>The updated code to run the plot is available here: <a href="https://doi.org/10.5281/zenodo.10998860">https://doi.org/10.5281/zenodo.10998860</a></p> <p><strong>If you have any questions, please get in touch with us:</strong><br>Mladen Popović <m.popovic(at)rug.nl><br>Maruf A. Dhali <m.a.dhali(at)rug.nl><br>Lambert Schomaker <l.r.b.schomaker(at)rug.nl></p> <p> </p> <p><strong>References:</strong><br>1. Bronk Ramsey, C. (2001). Development of the radiocarbon calibration program. <em>Radiocarbon</em>, <em>43</em>(2A), 355-363.</p>
Neural Network predictions and ERA5 reference of integrated water vapour, and temperature and specific humidity profiles based on simulated microwave radiometer observations
<p>This data set contains predictions of the Neural Network retrievals described in <strong>[1]</strong>, where simulated microwave radiometer observations (brightness temperatures, TBs) from the evaluation data subset of <strong>[2]</strong> (years 2001, 2006, 2011, 2015) were used as input to the Neural Network. As described in Section 3.2 of <strong>[1]</strong>, we trained an ensemble of 20 Neural Networks for each retrieved atmospheric quantity and applied them to the ERA5 evaluation data set to estimate the robustness of the retrievals with respect to random perturbations. The following atmospheric quantities were retrieved: </p> <ul> <li>temperature profile (variable name 'temp_p', filename suffix 'temp_test_417'),</li> <li>boundary layer temperature profile (variable name 'temp_p', filename suffix 'temp_test_424'),</li> <li>specific humidity profile (variable name 'q_p', filename suffix 'q_test_472'),</li> <li>integrated water vapour (variable name 'iwv_p', filename suffix 'iwv_test_126')</li> </ul> <p>The cryptic 3-digit filename suffixes represent different settings of the Neural Network retrieval. More information can be found in <strong>[3]</strong>. Variables that do not have the "_p" suffix are ERA5 data and used as reference to estimate errors of the retrievals by comparing them with the predictions. The dimension 'n_s' represents the ERA5 data sample number while the dimension 'n_rand' designates the ensemble of Neural Networks.</p> <p>These files can be created when running run_NN_retrieval (contained in NN_retrieval.py, see <strong>[3]</strong>) with exec_type='20_runs' and eval_mode=True and test_id either "126", "417", "424" or "472". However, as this might take some hours, we provide them here.</p> <p> </p> <p><strong>[1]:</strong> Walbröl, A., Griesche, H. J., Mech, M., Crewell, S., and Ebell, K.: Combining low- and high-frequency microwave radiometer measurements from the MOSAiC expedition for enhanced water vapour products, Atmospheric Measurement Techniques, 17, 6223-6245, https://doi.org/10.5194/amt-17-6223-2024, 2024.</p> <p><strong>[2]:</strong> Walbröl, A., and Mech, M.: ERA5 based training, validation and evaluation data for retrievals combining 22-58 GHz with 175-340 GHz microwave radiometer measurements during MOSAiC (1.0.0). Zenodo. https://doi.org/10.5281/zenodo.10997365, 2024.</p> <p><strong>[3]: </strong>Walbröl, A.: Codes for: Combining low and high frequency microwave radiometer measurements from the MOSAiC expedition for enhanced water vapour products (1.0.1). Zenodo. <a href="https://doi.org/10.5281/zenodo.11123136" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.11123136</a>, 2024.</p>
Morphing libraries, QSAR models, and compounds predicted to be active on the Glucocorticoid receptor (GR)
<p>This repository contains datasets and files related to the computational drug discovery project of the chemical space exploration of the Glucocorticoid receptor. The accompanying Python code is freely available in the GitHub repository (<a title="https://github.com/Iagea/GRML_analyses" href="https://github.com/Iagea/GRML_analyses" target="_blank" rel="noreferrer noopener">https://github.com/Iagea/GRML_analyses</a>).</p> <p><strong>Morphing Libraries:</strong></p> <ul> <li><strong>GRML_library.csv:</strong> The GRML library is the collection of 999,015 virtual compounds generated by Molpher [1-2] starting from GR ligands with unique Bemis-Murcko scaffolds collected from the ChEMBL17 and IMG libraries.</li> <li><strong>RML_library.csv:</strong> The RML library is the collection of 1,346,310 virtual compounds generated by Molpher starting from compounds with unique Bemis-Murcko scaffolds randomly selected from the ZINC database.</li> </ul> <p><strong>IMG library:</strong></p> <ul> <li><strong>IMG_non_proprietary.csv</strong>: The non-proprietary IMG library subset containing 12,956 compounds and their corresponding B-scores from the primary screen.</li> </ul> <p><strong>Molpher inputs:</strong></p> <ul> <li><strong>GR_inputs.csv</strong>: The GR inputs are the ligands used to create the GRML library, 204 compounds from ChEMBL17 (95 compounds) and the non-proprietary dataset from IMG (109 compounds).</li> <li><strong>Random_inputs.csv</strong>: The random inputs are 249 random ZINC compounds used to create the Random library.</li> </ul> <p><strong>Model's training sets:</strong></p> <ul> <li><strong>Model33_training_set.csv</strong>: Random forest classification model training set, it includes 865 compounds; known GR actives and inactives from ChEMBL33 (738 compounds) and non-proprietary active ligands from the IMG library (127 compounds).</li> <li><strong>Model17_training_set.csv</strong>: Random forest classification model training set, it includes 601 compounds; known GR actives and inactives from ChEMBL17 (474 compounds) and non-proprietary active ligands from the IMG library (127 compounds).</li> <li><strong>RFR_training_set.csv</strong>: Random forest regression model training set, it includes 89 compounds; known GR actives and inactives from ChEMBL33 that fit into the GR pharmacophore with the four features we describe in our paper.</li> </ul> <p><strong>Models:</strong></p> <ul> <li><strong>Model33.pkl:</strong> Python pickle file containing the trained Random forest classification models used along with Mondrian cross-conformal prediction to classify GR actives/inactives. This model was trained with ChEMBL33 and IMG libraries.</li> <li><strong>Model17.pkl</strong>: Python pickle file containing the trained Random forest classification models used along with Mondrian cross-conformal prediction to classify GR actives/inactives. This model was trained with ChEMBL17 and IMG libraries.</li> <li><strong>RFR_models.pkl</strong><em>: </em>Python pickle file containing the 100 trained random forest regression models used to rank the proposed active morphs. These models were trained with the RFR_training_set.csv.</li> </ul> <p><strong>Active predicted morphs:</strong></p> <ul> <li><strong>all_morphs_actives</strong><em><strong>_</strong></em><strong>predicted.xlsx:</strong> An Excel spreadsheet containing two sheets. 1) All 22,524 GRML active predicted morphs. 2) All 4,341 RML active predicted morphs. The QED, NIBR Severity Score, and Molskill Score are given for each morph.</li> </ul> <p><strong>Proposed GR active ligands:</strong></p> <ul> <li><strong>designed_ligands.xlsx</strong>: An Excel spreadsheet containing two sheets. 1) All 54 designed GR ligands with their QED, NIBR severity score, MolSkill score, consensus ranking from the 100 RFR models, and the result of the manual annotation and remarks, if available. 2) The structure of the 54 ligands based on their manual annotation and presence or not in ChEMBL33 database.</li> </ul> <p>Researchers and professionals in the field of drug discovery and cheminformatics may find these resources useful for further analysis and investigations.</p> <p><strong>Bibliography</strong></p> <p>[1] Hoksza, D., Škoda, P., Voršilák, M. <em>et al.</em> Molpher: a software framework for systematic chemical space exploration. <em>J Cheminform</em> <strong>6</strong>, 7 (2014). https://doi.org/10.1186/1758-2946-6-7</p> <p>[2] <a href="https://github.com/lich-uct/molpher-lib">https://github.com/lich-uct/molpher-lib</a></p>
Рис. 9. Блок-схема прогноЗирования сроков установки коллекторов и оЖидаемого количества спата [Белогрудов, 1980]. Fig. 9. The block diagram of prediction timing for installation of collectors and the expected number of spat [Belogrudov, 1980]. in Review of methods for the forecast of mollusk's spat productivity in sea-farms of Primorye and probable ways of their enhancement
Рис. 9. Блок-схема прогноЗирования сроков установки коллекторов и оЖидаемого количества спата [Белогрудов, 1980]. Fig. 9. The block diagram of prediction timing for installation of collectors and the expected number of spat [Belogrudov, 1980].
Supplementary Data for 'Transformers Significantly Improve Splice Site Prediction'
<p><strong>Description:</strong></p> <p>This repository contains supplementary data accompanying the manuscript <strong>"Transformers Significantly Improve Splice Site Prediction"</strong>. The data includes annotations used for training our splice site prediction models and the predictions made by our model and SpliceAI 10k. These datasets are provided to facilitate replication of our results and to support further research in RNA splicing and machine learning applications in genomics.</p> <p><strong>Contents:</strong></p> <ol> <li> <p><strong>Annotations Used for Model Training:</strong></p> <ul> <li> <p><strong>clinvar_splice_variants.tsv</strong></p> <ul> <li><em>Description:</em> Contains detailed information about ClinVar splice variants used in our study.</li> <li><em>Contents Include:</em> Variant identifiers, genomic coordinates, associated clinical significance, and relevant annotations.</li> </ul> </li> <li> <p><strong>splice_site_annotation_gtex.tsv</strong></p> <ul> <li><em>Description:</em> Splice site annotations derived from all tissues in GTEx V8.</li> <li><em>Contents Include:</em> Coordinates of splice sites and transcript information.</li> </ul> </li> <li> <p><strong>splice_site_annotation_icelandic_whole_blood_plus_gtex.tsv</strong></p> <ul> <li><em>Description:</em> Splice site annotations derived from a combination of Icelandic whole blood samples and samples from all tissues in GTEx V8.</li> <li><em>Contents Include:</em> Coordinates of splice sites and transcript information.</li> </ul> </li> </ul> </li> <li> <p><strong>Model Predictions:</strong></p> <p><strong>a. SpliceAI 10k Model Predictions:</strong></p> <ul> <li> <p><strong>spliceai_10k_clinvar_delta.vcf</strong></p> <ul> <li><em>Description:</em> SpliceAI 10k delta scores for ClinVar splice variants.</li> <li><em>Contents Include:</em> Variant Call Format (VCF) file containing delta scores that indicate the predicted impact on splicing for ClinVar variants.</li> </ul> </li> <li> <p><strong>spliceai_10k_no_sqtl_delta.vcf</strong></p> <ul> <li><em>Description:</em> SpliceAI 10k delta scores for variants unlikely to be splicing quantitative trait loci (sQTLs) in Icelandic whole blood.</li> <li><em>Contents Include:</em> VCF file with delta scores for variants not associated with sQTLs.</li> </ul> </li> <li> <p><strong>spliceai_10k_sqtl_delta.vcf</strong></p> <ul> <li><em>Description:</em> SpliceAI 10k delta scores for sQTLs detected in Icelandic whole blood.</li> <li><em>Contents Include:</em> VCF file with delta scores for variants identified as sQTLs.</li> </ul> </li> </ul> <p><strong>b. Transformer 45k Model Predictions:</strong></p> <ul> <li> <p><strong>transformer_45k_clinvar_delta.vcf</strong></p> <ul> <li><em>Description:</em> Transformer 45k delta scores for ClinVar splice variants.</li> <li><em>Contents Include:</em> VCF file with delta scores from our Transformer model, indicating the predicted impact on splicing.</li> </ul> </li> <li> <p><strong>transformer_45k_no_sqtl_delta.vcf</strong></p> <ul> <li><em>Description:</em> Transformer 45k delta scores for variants unlikely to be sQTLs in Icelandic whole blood.</li> <li><em>Contents Include:</em> VCF file with delta scores for variants not associated with sQTLs, as predicted by our model.</li> </ul> </li> <li> <p><strong>transformer_45k_sqtl_delta.vcf</strong></p> <ul> <li><em>Description:</em> Transformer 45k delta scores for sQTLs detected in Icelandic whole blood.</li> <li><em>Contents Include:</em> VCF file with delta scores for variants identified as sQTLs, based on our Transformer model predictions.</li> </ul> </li> </ul> </li> </ol> <p><strong>Additional Information:</strong></p> <ul> <li> <p><strong>Delta Scores and Their Interpretation:</strong></p> <ul> <li>Each variant is assessed for its potential impact on splicing through four delta scores: <ul> <li><strong>Acceptor Site Creation (<code>top_a_creation_delta</code>):</strong> Predicts the likelihood of creating a new acceptor site.</li> <li><strong>Acceptor Site Disruption (<code>top_a_disruption_delta</code>):</strong> Predicts the likelihood of disrupting an existing acceptor site.</li> <li><strong>Donor Site Creation (<code>top_d_creation_delta</code>):</strong> Predicts the likelihood of creating a new donor site.</li> <li><strong>Donor Site Disruption (<code>top_d_disruption_delta</code>):</strong> Predicts the likelihood of disrupting an existing donor site.</li> </ul> </li> <li><strong>Final Delta Score Calculation:</strong> <ul> <li>The overall impact of a variant is determined by taking the maximum of these four delta scores: <div> <div><code>final_delta_score = <span>max</span>(top_a_creation_delta, top_a_disruption_delta, top_d_creation_delta, top_d_disruption_delta) </code></div> </div> </li> <li>A higher final delta score indicates a greater predicted impact on splicing.</li> </ul> </li> <li><strong>Positions:</strong> <ul> <li>The positions (<code>*_pos</code>) indicate the genomic coordinates where the predicted splicing events occur, providing insight into the specific locations affected by the variant.</li> </ul> </li> </ul> </li> </ul> <ul> <li> <ul> <li><strong>Interpreting Delta Scores:</strong> <ul> <li>Delta scores range from 0 to 1.</li> <li>Scores closer to 1 suggest a higher probability of the variant affecting splicing.</li> </ul> </li> </ul> </li> <li> <p><strong>Purpose:</strong></p> <ul> <li>These datasets support the findings reported in our manuscript by providing the raw data used for model training and evaluation.</li> <li>Researchers can use these datasets to replicate our experiments, compare model performances, or conduct further studies on splice site prediction.</li> </ul> </li> </ul> <p> </p> <p><strong>Citation:</strong></p> <p>Please cite this dataset as:</p> <blockquote> <p>B.A. Jónsson, G.H. Halldórsson, S. Árdal, S. Rögnvaldsson, E. Einarsson, P. Sulem, D.F. Guðbjartsson, P. Melsted, K. Stefánsson, M.Ö. Úlfarsson (2023). Supplementary Data for "Transformers Significantly Improve Splice Site Prediction". Zenodo. <a target="_new" rel="noopener">https://doi.org/10.5281/zenodo.14109868</a></p> </blockquote> <p> </p> <p><strong>Contact Information:</strong></p> <p>For questions or further information, please contact:</p> <ul> <li><strong>Benedikt A. Jónsson</strong></li> <li><strong>Affiliation:</strong> deCODE Genetics/Amgen, Inc., Reykjavik, Iceland</li> <li><strong>Email: </strong>benediktj@decode.is</li> </ul>
Dataset of the manuscript: Predictive design of plasmonic color
<p>This data publication is based on the metadata and datasets underlying the manuscripts "Predictive design of plasmonic color"</p> <p>Folder "Figure 1" contains:</p> <ul> <li>Simulated Extinction spectra of 120 nm silica particle coated with 20 nm gold shell</li> <li>Simulated Transmission spectra of 120 nm silica particle coated with 20 nm gold shell scaled for different concentrations + Lab and RGB color coordinates</li> <li>3D plots of accessible color gamut for Ag, Au, SiO2@Au and SiO2@Ag nanoparticles</li> </ul> <p>Figure "Figure 2" contains:</p> <ul> <li>Simulated colors of targeted for varying concentrations</li> <li>Color-map for color of the year 2022</li> <li>Color coordinates (Lab and RGB) of structure in spot 1 and spot 4 scaled for different concentrations</li> </ul> <p>Figure "Figure 3" contains:</p> <ul> <li>Photographs of realized particle dispersion (spot1: 0@52, spot2: 113@46, spot3: 137@24, spot 4: 398@37) at high and low concentrations, approx. concentrations in number of particles per ml</li> <li>TEM images of realized particles (for core-shell particle: after seeding (x@NP) and after shell growth)</li> <li>UV-Vis:</li> <ul> <li>Measured absorbance spectra and scaled transmission spectra of realized particles spot</li> <li>Color coordinates of simulated, experimentally determined and target color</li> </ul> </ul> <p>Figure "Figure 4" contains:</p> <ul> <li>Photographs of realized particle dispersion (CRC1411yellow: Ag0@3-24, CRC1411red: Au0@13, CRC1411blue: Au113@35), approx. concentrations in number of particles per ml</li> <li>TEM images of realized particles (for core-shell particle: after seeding (x@NP) and after shell growth)</li> <li>UV-Vis:</li> <ul> <li>Measured absorbance spectra and scaled transmission spectra of realized particles</li> <li>Color coordinates of simulated, experimentally determined and target color</li> </ul> <li>color maps for CRC1411 target colors</li> </ul> <p> </p>
PERFORMANCE OF MACHINE LEARNING ALGORITHMS FOR LUNG CANCER PREDICTION: A COMPARATIVE STUDY
<p>This study compares the performance of five machine learning algorithms—logistic regression, support vector machines, random forests, gradient boosting, and neural networks—for lung cancer prediction using demographic, lifestyle, and medical data from the UCI Machine Learning Repository. Gradient boosting and random forests achieved the highest accuracy (89% and 87%, respectively) and AUC-ROC scores (0.93 and 0.92), while neural networks reached 90% accuracy but presented interpretability limitations. Key predictors included smoking history, chronic disease, and respiratory symptoms, aligning with established risk factors. Ensemble methods, particularly gradient boosting and random forests, provided an optimal balance of accuracy and interpretability, highlighting their potential for clinical applications in early lung cancer detection.</p>
Tumor and Blood B Cell Abundance Outperforms Established ICB Response Prediction Signatures in Head and Neck Cancer
<div> <div> <div> <div> <p>This dataset contains processed flow cytometry data and clinical information for deidentified patients from Cohort 11, as well as deconvoluted cell abundances and clinical data for deidentified patients from Cohort 10, associated with the study titled <em>"Tumor and Blood B Cell Abundance Outperforms Established Immune Checkpoint Blockade Response Prediction Signatures in Head and Neck Cancer"</em> published in <strong>Annals of Oncology (2024)</strong>. <a href="https://doi.org/10.1016/j.annonc.2024.11.008" target="_new" rel="noopener">DOI: https://doi.org/10.1016/j.annonc.2024.11.008</a>.</p> </div> </div> </div> </div> <div> <div> <div> </div> </div> </div>
CrysAtom: Distributed Representation of Atoms for Crystal Property Prediction
<div> <div> <p>Application of artificial intelligence (AI) has been ubiquitous in the growth of research in the areas of basic sciences. Frequent use of machine learning (ML) and deep learning (DL) based methodologies by researchers has resulted in significant advancements in the last decade. These techniques led to notable performance enhancements in different tasks such as protein structure prediction, drug-target binding affinity prediction, and molecular property prediction. In material science literature, it is well-known that crystalline materials exhibit topological structures. Such topological structures may be represented as graphs and utilization of graph neural network (GNN) based approaches could help encoding them into an augmented representation space. Primarily, such frameworks adopt supervised learning techniques targeted towards downstream property prediction tasks on the basis of electronic properties (formation energy, bandgap, total energy, etc.) and crystalline structures. Generally, such type of frameworks rely highly on the handcrafted atom feature representations along with the structural representations. In this paper, we propose an unsupervised framework namely, CrysAtom, using untagged crystal data to generate dense vector representation of atoms, which can be utilized in existing GNN-based property predictor models to accurately predict important properties of crystals. Empirical results show that our dense representation embeds chemical properties of atoms and enhance the performance of the baseline property predictor models significantly.</p> </div> </div>
Making predictions for geoscience and climate policy workshop results
<p>This provides the data files and basic plotting and interpretation for results for predictions made in a series of workshops run in 2024, with predictions made for 2024, 2050, 2100 and 5o years after net zero. The workshop results are found in the files "EGU_Prediction-making workshop questions cleaned.csv" and "RGS_...", the code expects these to be in a folder denoted "data". More details can be found in the README.txt. Updates to this codebase can be found in <a href="https://github.com/Rlamboll/ClimatePredictionsWorkshop">https://github.com/Rlamboll/ClimatePredictionsWorkshop.</a></p> <p> </p>
Dataset and code for submission of "Aesthetic values predict bird trade, but the association varies across product types and trade regions"
<p>Data and code used for analyses in the manuscript titled "Aesthetic values predict bird trade, but the association varies across product types and trade regions". The raw data files are given as .xlsx files for the trade data ("birdtrade_data_clean.xlsx" & "EU_birdtrade_data_clean.xlsx"), as a .csv file ("iratebirds_data_151122.csv") for the aeshtetic value data. and all final merged datasets used in the analysis and figure codes are given as .RData -files. All code is given as .R files.<br><br>The data descriptor is currently given in the submitted manuscript and it's supplements, and will be added here too in more detail upon acceptance of the manuscript.</p>
Dataset for "Coating thickness prediction for a viscous film on a rough plate"
<p>This dataset supports the publication 'Coating thickness prediction for a viscous film on a rough plate' by Lebo Molefe, Giuseppe A. Zampogna, John M. Kolinski, and François Gallaire, <em>Journal of Fluid Mechanics,</em> <strong>1001</strong>(A59), 2024. The data are film thicknesses measured for silicone oil films coated on rough plates. Please refer to the publication and its supplemental material for details.</p> <p><a href="https://doi.org/10.1017/jfm.2024.1015">https://doi.org/10.1017/jfm.2024.1015</a></p> <p><strong>Contents</strong></p> <p>Three folders containing the code and data required to produce the figures in the journal article and supplemental material.</p> <p><strong>Readme</strong></p> <p>A .txt file describing contents of code and datasets.</p> <p><strong>Code</strong></p> <p>Contains:</p> <ul> <li>Python code to produce Figures 5-10 and 12 in the main text, as well as supplementary Figures 1-4.</li> <li>Source code: COMSOL files for solving microscopic problem for effective parameters (L, Kitf) describing the rough surface; Python files for solving the macroscopic model equations once effective parameters are known.</li> </ul> <p><strong>Data</strong></p> <p>Contains data needed to plot the figures mentioned above, as well as supporting data.</p> <p>The data includes density, surface tension, and viscosity measurements for silicone oil, with rheometry measurements performed on an Anton Paar MCR 302 rheometer, as detailed in the Supplementary Material.</p> <p><strong>Plots</strong></p> <p>Contains output of plotting code corresponding to the figures mentioned above.</p>
Realistic Ab Initio Predictions of Excimer Behavior under Collective Light-Matter Strong Coupling - Dataset
<p>Dataset complement to "Realistic Ab Initio Predictions of Excimer Behavior under Collective Light-Matter Strong Coupling" - includes outputs obtained using the <a href="https://etprogram.org/development-website/">eT program</a>, an open-source electronic (and molecular-polaritonic) structure program, and VIBROT (OpenMolcas).</p> <p> </p> <p>See the paper at <a href="https://journals.aps.org/prx/abstract/10.1103/PhysRevX.15.021040">https://journals.aps.org/prx/abstract/10.1103/PhysRevX.15.021040</a></p>
JSON files containing parameters of training gene models for ab-initio prediction software
<p>These are the JSON files containing parameters of training gene models for ab-initio prediction software. These training datasets are Phytophthora specific and can be further utilized for the gene prediction and annotation of other related Phytophthora strains.</p>
Our predicted Crinkler (CRN) family effector proteins and the corresponding GFF3 files across 128 Phytophthora isolates
<p>These files contain predicted Crinkler (CRN) family effector proteins and the corresponding GFF3 files across 128 Phytophthora isolates.</p>
DeepBacs – E. coli SIM prediction dataset and CARE model
<p>Training and test images of live, membrane-labeled <em>E. coli </em>cells for prediction of SIM super-resolution images from widefield images, as well as a trained CARE model.</p> <p>Additional information can be found on this <a href="https://github.com/HenriquesLab/DeepBacs/wiki">github wiki</a>.</p> <p>The example image shows a widefield fluorescence image and SIM reconstruction of FM5-95 labelled, live <em>E. coli </em>cells.</p> <p> </p> <p><strong>Training and test dataset</strong></p> <p><strong>Data type</strong>: Paired microscopy images (fluorescence) of low (widefield) and high resolution (SIM)</p> <p><strong>Microscopy data type</strong>: Fluorescence microscopy (FM5-95)</p> <p><strong>Microscope</strong>: GE HealthCare Deltavision OMX system (with temperature and humidity control, 37°C) equipped with an Olympus 60x 1.42NA Oil immersion objective and 2 PCO Edge 5.5 sCMOS cameras (one for DIC, one for fluorescence)</p> <p><strong>Cell type</strong>: <em>E. coli </em>DH5α grown under agarose pads</p> <p><strong>File format</strong>: .tif (16-bit for widefield images and 32-bit for SIM reconstructions)</p> <p><strong>Image size</strong>: 1024 x 1024 px² (40 nm/px)<br> <strong>Image preprocessing</strong>: <em>E. coli</em> widefield images were scaled with a factor of 2 to match the SIM reconstruction pixel size. </p> <p> </p> <p><strong>CARE model</strong></p> <p>The CARE 2D model was generated using the ZeroCostDL4Mic platform (Chamier et al., 2021). It was trained from scratch for 300 epochs on 5500 paired image patches (image dimensions: (1024 x 1024 px²), patch size: (80 x 80 px²), 100 patches/image) with a batch size of 8 and a laplace loss function, using the CARE 2D ZeroCostDL4Mic notebook (v 1.12). Key python packages used include tensorflow (v 0.1.12), Keras (v2.3.1), csbdeep (v 0.6.1), numpy (v1.19.5), cuda (v 10.1.243). The training was accelerated using a Tesla P100GPU and data was augmented by a factor of 4 using rotation and flipping.</p> <p>Model weights can be used with the ZeroCostDL4Mic CARE 2D notebook or the CSBDeep Fiji plugin.</p> <p> </p> <p><strong>Author(s)</strong>: Pedro Matos Pereira<sup>1,2</sup>, Mariana Pinho<sup>1,3</sup></p> <p><strong>Contact email</strong>: <a href="mailto:pmatos@itqb.unl.pt">pmatos@itqb.unl.pt</a> and <a href="mailto:mgpinho@itqb.unl.pt">mgpinho@itqb.unl.pt</a></p> <p> </p> <p><strong>Affiliation</strong>: </p> <p>1) Bacterial Cell Biology, Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Oeiras, Portugal</p> <p>2) ORCID: https://orcid.org/0000-0002-1426-9540</p> <p>3) ORCID: https://orcid.org/0000-0002-7132-8842</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.