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13,064 results for “predictions”
Predicting subjective liking of bitter and sweet liquid solutions using facial electromyography: mixed model dataset
<p>Hedonic responses to foods are often measured using subjective liking ratings scales. This project investigated the potential use of electromyography as a means to predict subjective liking ratings using affective facial muscle activity recorded at different phases of oral processing while tasting liquids. Using linear mixed models, muscle activity recorded while emptying into the mouth, swirling, and thinking about the taste of bitter and sweet liquid solutions was used to predict subjective liking ratings. During different phases of the tasting, these mixed models demonstrate that zygomaticus major activity predicted increased liking and that corrugator supercilii and levator labii superioris predicted decreased liking. The change in liking ratings predicted by each muscle varied depending on whether participants were emptying, swirling, or thinking about the taste.</p>
Dataset and source code for ICSME2017 paper "Supervised vs Unsupervised Models: A Holistic Look at Effort-Aware Just-in-Time Defect Prediction"
<p>Dataset and source code for ICSME2017 paper “Supervised vs Unsupervised Models: A Holistic Look at Effort-Aware Just-in-Time Defect Prediction”</p> <p>There are four different models in the paper (i.e., EALR, LT, CBS and OneWay). Each model was implemented in a single Java file in the model package. To reproduce the experiment results of each model in the paper, just run the main method in the corresponding Java file. </p> <p> </p>
pLMMoRF: A web server that accurately predicts membrane-interacting molecular recognition features by employing a protein language model
<p>pLMMMoRF predictor scrips and MemMoRF prediction of the human proteome.</p>
Data and script pipeline for: Common to rare transfer learning (CORAL) enables inference and prediction for a quarter million rare Malagasy arthropods
<p>The scripts and the data provided in this depository demonstrate how to apply the approach described in the paper "<strong>Common to rare transfer learning (CORAL) enables inference and prediction for a quarter million rare Malagasy arthropods</strong>" by Ovaskainen et al. Here we summarize how to use the software with a small, simulated dataset, with running time less than a minute in a typical laptop (Demo 1); (2) how to apply the analyses presented in the paper for a small subset of the data, with running time of ca. one hour in a powerful laptop (Demo 2); how to reproduce the full analyses presented in the paper, with running time up to several days, depending on the computational resources (Demo 3). The Demos 1 and 2 are aimed to be user-friendly starting points for understanding and testing how to implement CORAL. The Demo 3 is included mainly for reproducibility.</p> <p> </p> <p><strong><u>System requirements</u></strong></p> <p> </p> <p>· The software can be used in any operating system where R can be installed.</p> <p>· We have developed and tested the software in a windows environment with R version 4.3.1.</p> <p>· Demo 1 requires the R-packages phytools (2.1-1), MASS (7.3-60), Hmsc (3.3-3), pROC (1.18.5) and MCMCpack (1.7-0).</p> <p>· Demo 2 requires the R-packages phytools (2.1-1), MASS (7.3-60), Hmsc (3.3-3), pROC (1.18.5) and MCMCpack (1.7-0).</p> <p>· Demo 3 requires the R-packages phytools (2.1-1), MASS (7.3-60), Hmsc (3.3-3), pROC (1.18.5) and MCMCpack (1.7-0), jsonify (1.2.2), buildmer (2.11), colorspace (2.1-0), matlib (0.9.6), vioplot (0.4.0), MLmetrics (1.1.3) and ggplot2 (3.5.0).</p> <p>· The use of the software does not require any non-standard hardware.</p> <p> </p> <p><strong><u>Installation guide</u></strong></p> <p> </p> <p>· The CORAL functions are implemented in Hmsc (3.3-3). The software that applies the is presented as a R-pipeline and thus it does not require any installation other than installation of R.</p> <p> </p> <p><strong><u>Demo 1: Software demo with simulated data</u></strong></p> <p><strong> </strong></p> <p>The software demonstration consists of two R-markdown files:</p> <p> </p> <p>· <strong>D01_software_demo_simulate_data.</strong> This script creates a simulated dataset of 100 species on 200 sampling units. The species occurrences are simulated with a probit model that assumes phylogenetically structured responses to two environmental predictors. The pipeline saves all the data needed to data analysis in the file allDataDemo.RData: XData (the first predictor; the second one is not provided in the dataset as it is assumed to remain unknown for the user), Y (species occurrence data), phy (phylogenetic tree), studyDesign (list of sampling units). Additionally, true values used for data generation are save in the file trueValuesDemo.RData: LF (the second environmental predictor that will be estimated through a latent factor approach), and beta (species responses to environmental predictors).</p> <p>· <strong>D02_software_demo_apply_CORAL</strong>. This script loads the data generated by the script D01 and applies the CORAL approach to it. The script demonstrates the informativeness of the CORAL priors, the higher predictive power of CORAL models than baseline models, and the ability of CORAL to estimate the true values used for data generation.</p> <p> </p> <p>Both markdown files provide more detailed information and illustrations. The provided html file shows the expected output. The running time of the demonstration is very short, from few seconds to at most one minute.</p> <p> </p> <p><strong><u>Demo 2: Software demo with a small subset of the data used in the paper</u></strong></p> <p><strong> </strong></p> <p>The software demonstration consists of one R-markdown file:</p> <p> </p> <p><strong>MA_small_demo.</strong> This script uses the CORAL functions in HMSC to analyze a small subset of the Malagasy arthropod data. In this demo, we define rare species as those with prevalence at least 40 and less than 50, and common species as those with prevalence at least 200. This leaves 51 species to the backbone model and 460 rare species modelled through the CORAL approach. The script assess model fit for CORAL priors, CORAL posteriors, and null models. It further visualizes the responses of both the common and the rare species to the included predictors.</p> <p> </p> <p><strong><u>Scripts and data for reproducing the results presented in the paper (Demo 3)</u></strong></p> <p> </p> <p>The input data for the script pipeline is the file “allData.RData”. This file includes the metadata (meta), the response matrix (Y), and the taxonomical information (taxonomy). Each file in the pipeline below depends on the outputs of previous files: they must be run in order. The first six files are used for fitting the backbone HMSC model and calculating parameters for the CORAL prior:</p> <p> </p> <p>· <strong>S01_define_Hmsc_model</strong> - defines the initial HMSC model with fixed effects and sample- and site-level random effects.</p> <p>· <strong>S02_export_Hmsc_model</strong> - prepares the initial model for HPC sampling for fitting with Hmsc-HPC. Fitting of the model can be then done in an HPC environment with the bash file generated by the script. Computationally intensive.</p> <p>· <strong>S03_import_posterior</strong> – imports the posterior distributions sampled by the initial model.</p> <p>· <strong>S04_define_second_stage_Hmsc_model</strong> - extracts latent factors from the initial model and defines the backbone model. This is then sampled using the same S02 export + S03 import scripts. Computationally intensive.</p> <p>· <strong>S05_visualize_backbone_model</strong> – check backbone model quality with visual/numerical summaries. Generates Fig. 2 of the paper.</p> <p>· <strong>S06_construct_coral_priors</strong> – calculate CORAL prior parameters.</p> <p> </p> <p>The remaining scripts evaluate the model:</p> <p>· <strong>S07_evaluate_prior_predictionss </strong>– use the CORAL prior to predict rare species presence/absences and evaluate the predictions in terms of AUC. Generates Fig. 3 of the paper.</p> <p>· <strong>S08_make_training_test_split</strong><em> </em>– generate train/test splits for cross-validation ensuring at least 40% of positive samples are in each partition.</p> <p>· <strong>S09_cross-validate</strong><em> </em>– fit CORAL and the baseline model to the train/test splits and calculate performance summaries. Note: we ran this once with the initial train/test split and then again with on the inverse split (i.e., <em>training = ! training</em> in the code, see comment). The paper presents the average results across these two splits. Computationally intensive.</p> <p>· <strong>S10_show_cross-validation_results</strong><em> </em>– Make plots visualizing AUC/Tjur’s R<sup>2</sup> produced by cross-validation. Generates Fig. 4 of the paper.</p> <p>· <strong>S11a_fit_coral_models</strong><em> </em>– Fit the CORAL model to all 250k rare species. Computationally intensive.</p> <p>· <strong>S11b_fit_baseline_models</strong><em> </em>– Fit the baseline model to all 250k rare species. Computationally intensive.</p> <p>· <strong>S12_compare_posterior_inference</strong><em> ­­</em>– compare posterior climate predictions using CORAL and baseline models on selected species, as well as variance reduction for all species. Generates Fig. 5 of the paper.</p> <p> </p> <p>Pre-processing scripts:</p> <p>· <strong>P01_preprocess_sequence_data.R </strong>– Reads in the outputs of the bioinformatics pipeline and converts them into R-objects.</p> <p>· <strong>P02_download_climatic_data.R </strong>– Downloads the climatic data from "sis-biodiversity-era5-global” and adds that to metadata.</p> <p>· <strong>P03_construct_Y_matrix.R </strong>– Converts the response matrix from a sparse data format to regular matrix. Saves “allData.RData”, which includes the metadata (meta), the response matrix (Y), and the taxonomical information (taxonomy).</p> <p> </p> <p>Computationally intensive files had runtimes of 5-24 hours on high-performance machines. Preliminary testing suggests runtimes of over 100 hours on a standard laptop.</p> <p><em> </em></p> <p><strong><u>ENA Accession numbers</u></strong></p> <p>All raw sequence data are archived on mBRAVE and are publicly available in the European Nucleotide Archive (ENA; https://www.ebi.ac.uk/ena; project accession number PRJEB86111; run accession numbers ERR15018787-ERR15009869; sample IDs for each accession and download URLs are provided in the file ENA_read_accessions.tsv).</p>
SynProtX: A Large-Scale Proteomics-Based Deep Learning Model for Predicting Synergistic Anticancer Drug Combinations
<h2>SynProtX: A Large-Scale Proteomics-Based Deep Learning Model for Predicting Synergistic Anticancer Drug Combinations</h2> <p>SynProtX is a deep learning model that integrates large-scale proteomics data, molecular graphs, and chemical fingerprints to predict synergistic effects of anticancer drug combinations. It provides robust performance across tissue-specific and study-specific datasets, enhancing reproducibility and biological relevance in drug synergy prediction.</p> <p>This Zenodo repository includes a <code>.tar.gz</code> archive containing all essential components to reproduce the experiments described in the study. This archive is designed to work seamlessly with the coding pipeline available at: <a href="https://github.com/manbaritone/SynProtX" target="_blank" rel="noopener">https://github.com/manbaritone/SynProtX</a>.</p> <h3>License:</h3> <p>Creative Commons Zero v1.0 Universal (CC0)<br>This work is released under CC0, dedicating it to the public domain. You are free to use, modify, and distribute it without restriction.</p> <h3>Archive Contents:</h3> <p>This compressed file includes:</p> <ul> <li>Datasets<br>- Tissue Datasets: <code>ALMANAC-Breast</code>, <code>ALMANAC-Lung</code>, <code>ALMANAC-Ovary</code>, <code>ALMANAC-Skin</code><br>- Study Datasets: <code>FRIEDMAN</code>, <code>ONEIL</code></li> <li>Supporting Files<br>- Raw and preprocessed data<br>- Feature dictionaries<br>- Hyperparameter configurations<br>- Trained model weights</li> </ul> <h3>Folder Structure:</h3> <blockquote> <p><code>SynProtX/</code><br><code>├── data/ # Raw and preprocessed data</code><br><code>│ ├── export/ # Processed protein/gene expression & drug combinations</code><br><code>│ ├── nps/ # Numpy arrays for all datasets</code><br><code>│ ├── nps_intersected/ # Dataset-specific numpy arrays</code><br><code>│ └── raw/ # Original data from DrugComb, CCLE, COSMIC, ChEMBL V31, ProCan-DepMapSanger</code><br><code>├── feature_dicts/ # Feature dictionaries for drug combinations</code><br><code>├── hyperparams/ # Hyperparameter configs for SynProtX-GATFP</code><br><code>│ ├── classification/ # For classification tasks</code><br><code>│ └── regression/ # For regression tasks</code><br><code>├── state_dict/ # Trained model weights</code><br><code>│ ├── classification/ # PyTorch checkpoints for classification</code><br><code>│ └── regression/ # PyTorch checkpoints for regression</code><br><code>└── README_Zenodo.md # This file</code></p> </blockquote> <h3>For more information, please visit:</h3> <p><strong>GitHub:</strong> <a href="https://github.com/manbaritone/SynProtX" target="_blank" rel="noopener">https://github.com/manbaritone/SynProtX</a></p>
Exceptional multi-year prediction skill of the Kuroshio Extension in the high-resolution CESM decadal prediction system
The Kuroshio Extension (KE) has far-reaching influences on climate as well as on local marine ecosystems. Thus, skillful multi-year to decadal prediction of the KE state and understanding sources of skill are valuable. Retrospective forecasts using the high-resolution CESM show exceptional skill in predicting KE variability up to lead year 4, substantially higher than the skill found in a similarly configured low-resolution CESM. The higher skill is attained because the high-resolution system can more realistically simulate the westward Rossby wave propagation of initialized ocean anomalies in the central North Pacific and their expression within the sharp KE front, and does not suffer from spurious variability near Japan present in the low-resolution CESM that interferes with the incoming wave propagation. These results argue for the use of high-resolution models for future studies that aim to predict changes in western boundary current systems and associated biological fields.
The dataset used in the article "Evidential Deep Learning for Guided Drug-Target Interaction Prediction"
<p>The file consists of two parts, the first part is the feature file used for model training and the second part is the saved model.</p> <p>bond_angle file is the feature file for the drugbank dataset, KIBA file is the feature file for the kiba dataset, davis is the feature file for the davis dataset. case_drugbank is the dungbank feature file for the independent test set and case_len_drug is the feature file for the patent validation dataset in the TKIs feature file.len_vs is the feature file for TKIs virtual screening dataset.</p> <p>In the runs folder, the models saved by random division on the Drugbank, KIBA, and Davis datasets are stored.</p> <p>The <a href="https://zenodo.org/api/records/14056305/draft/files/bert_weights_encoderMedium_10.h5/content" target="_blank" rel="noopener noreferrer">bert_weights_encoderMedium_10.h5</a> is the parameter for the small molecule pre-training model, MG-BERT.</p>
Dataset used in "Catchment landforms predict groundwater-dependent wetland sensitivity to recharge changes".
<p>Dataset in the form of text files, all 60 presented catchments are identified by the HydroATLAS database ID in each file. Catchment topography figures are available following their HydroATLAS ID. For the seepage distribution, one text file is presented for each catchment named with the corresponding catchment ID. Code to generate desaturation parameters (lambda and n) and ML models are saved in a common directory (catchment_desaturation_estimator), code is commented to facilitate reproducibility. </p>
Data and Code for Yeager et al., 2022: Enhanced Skill and Signal-to-noise in an Eddy-Resolving Decadal Prediction System
The sensitivity of decadal prediction system performance to model resolution is examined by comparing results from low- and high-resolution (LR and HR) predictions conducted with the Community Earth System Model (CESM). The primary difference between the two systems is the horizontal grid spacing of the ocean and atmosphere models (1° for both in LR; 0.1° and 0.25°, respectively, in HR), permitting a first direct comparison of how skill and signal-to-noise characteristics change when moving to the ocean eddy-resolved modeling regime. HR exhibits significantly increased skill and enhanced signal-to-noise for atmospheric fields compared to LR. This result suggests that mesoscale atmosphere-ocean interaction, which is present in HR but absent in LR, is a key mechanism involved in the transmission of predictable signals from the ocean to the atmosphere. Climate predictions can potentially be improved (and the signal-to-noise paradox alleviated) through explicit representation of ocean eddies and their interactions with the atmosphere.
Dataset for "ConfSolv: Prediction of solute conformer free energies across a range of solvents"
<p>This dataset contains three archives. The first archive, full_dataset.zip, contains geometries and free energies for nearly 44,000 solute molecules with almost 9 million conformers, in 42 different solvents. The geometries and gas phase free energies are computed using density functional theory (DFT). The solvation free energy for each conformer is computed using COSMO-RS and the solution free energies are computed using the sum of the gas phase free energies and the solvation free energies. The geometries for each solute conformer are provided as ASE_atoms_objects within a pandas DataFrame, found in the compressed file dft coords.pkl.gz within full_dataset.zip. The gas-phase energies, solvation free energies, and solution free energies are also provided as a pandas DataFrame in the compressed file free_energy.pkl.gz within full_dataset.zip. Ten example data splits for both random and scaffold split types are also provided in the ZIP archive for training models. Scaffold split index 0 is used to generate results in the corresponding publication. </p><p>The second archive, refined_conf_search.zip, contains geometries and free energies for a representative sample of 28 solute molecules from the full dataset that were subject to a refined conformer search and thus had more conformers located. The format of the data is identical to full_dataset.zip.</p><p>The third archive contains one folder for each solvent for which we have provided free energies in full_dataset.zip. Each folder contains the .cosmo file for every solvent conformer used in the COSMOtherm calculations, a dummy input file for the COSMOtherm calculations, and a CSV file that contains the electronic energy of each solvent conformer that needs to be substituted for "EH_Line" in the dummy input file.</p>
Supplementary data on learning about German farmers' willingness to cooperate from public goods games and expert predictions
<p>Supplemental files on learning about German farmers' willingness to cooperate from public goods games and expert predictions. </p>
Agent-based model predicts that layered structure and 3D movement work synergistically to reduce bacterial load in 3D in vitro models of tuberculosis granuloma - Location Data
<p>This dataset is meant to be used with "Agent-based model predicts that layered structure and 3D movement work synergistically to reduce bacterial load in 3D in vitro models of tuberculosis granuloma - Results and Data". It provides spatial output data for 4 different setups (spheroid, traditional, 3d gravity, and traditional floating) of an agent-based model of <i>in vitro </i>tuberculosis infection models. </p>
Data for "Strong El Niño events lead to robust multi-year ENSO predictability"
<p>The full raw data, intermediate steps, and final results for all three experiments used in Lenssen et al. (202X), "Strong El Niño events lead to robust multi-year ENSO predictability," as submitted to GRL. Each zip file contains the raw data, intermediate steps, and final results for model analog forecast issued to predict that model or observational record. All data has been gridded to a common 2x2 grid using nco following the include `mygrid_2x2` file.</p><p>Archived codebase for this data available at <a href="https://zenodo.org/doi/10.5281/zenodo.10045615">https://zenodo.org/doi/10.5281/zenodo.10045615</a><br>Live codebase at <a href="https://github.com/nlenssen/LongLeadENSO/">https://github.com/nlenssen/LongLeadENSO/</a></p>
Dataset Analysis of the Predictions of Coherent vs. Fragmented Knowledge
<p>The database presents participant scores of ontological commitment to the causal principles of emergent processes and direct processes. The following list indicates which principles belong to inter-level direct processes, micro-level direct processes, inter-level emergent processes, and micro-level emergent processes.</p><p><strong>Micro-level Direct </strong></p><p>Special status of the organisms</p><p>Restricted interactions </p><p>Dependent interactions </p><p>Sequential interactions </p><p>Interactions stop when equilibrium is reached</p><p><strong>Inter-level Direct </strong></p><p><strong> </strong>Progressive change</p><p>Centralization</p><p>Teleology</p><p>Level correspondence </p><p><strong>Micro-level Emergent </strong></p><p>Equal status of the organisms</p><p>Independent interactions </p><p>Simultaneous interactions</p><p>Random interactions </p><p>Interactions in continuous equilibration </p><p><strong>Inter-level Emergent </strong></p><p>Proportional change</p><p>Decentralization</p><p>Non teleology</p><p>Separation of levels</p><p>In the database, the first row indicates the name of each principle and is accompanied by a number that denotes the session in which the measurement was taken. For example, "Special status of the organisms 1" and "Special status of the organisms 2" represent the estimates of ontological commitment to the micro-level direct principle "Special status of the organisms" in sessions one and two, respectively.</p>
Learning to resist distraction by spatially predictable luminance transients and color singletons: same or different mechanisms?
<p>The file contains the dataset related to the paper entitled "Learning to resist distraction by spatially predictable luminance transients and color singletons: same or different mechanisms?" to appear in Visual Cognition.</p><p>The folder contains a text file named README.txt that contains the explanation of the folder and file structure.</p>
Predicted genome-wide chromatin contact differences among 71 bonobos and chimpanzees
<p>This file contains predicted chromatin contact differences in HFF cells using Akita among pairs of 71 bonobos and chimpanzees at 4,420 ~ 1 Mb genomic windows in the panTro6 genome. Each entry corresponds to a pairwise comparison at a given window. Data per comparison includes the individual IDs in the pairwise comparison, lineages represented, chromosome, position, window ID, mean squared error, Spearman correlation, divergence (1 - Spearman correlation), and the number of nucleotide differences for the pair at the given window.</p>
Predicting medicinal phytochemicals of Moringa oleifera using hyperspectral reflectance of tree canopies
<p>Research article: <a href="https://doi.org/10.1080/01431161.2021.1887541">https://doi.org/10.1080/01431161.2021.1887541</a></p><p>New technique for processing hyperspectral data: 1)https://www.researchgate.net/publication/349663204_Computer_vision_and_hyperspectral_imagery_in_orchards_and_in_fields_Data_processing_and_analysis_methods</p><p>2)https://www.researchgate.net/profile/Vjacheslav-Fisenko/publication/349663204/figure/fig13/AS:1139984849485825@1648804976658/3-D-visualization-of-reflectance-spectra-of-four-medicinal-plant-genotypesPredicting_W640.jpg</p>
Pix2pix training dataset for predicting nuclei form brightfield images
<p>Dataset for training pix2pix model to predict nuclei from brightfield images of MDA-MB-231. </p><p>This dataset contains 1600 brightfield and 1600 nuclei images. </p><p>The nuclei were labeled using sir-DNA (Cytoskeleton). Cells were imaged live (37 °C, 5% CO2) using a Nikon Eclipse Ti2-E microscope (Nikon) equipped with an sCMOS Orca Flash4.0 camera (Hamamatsu) and controlled by the NIS-Elements software (Nikon, v 5.11.01). MDA-MB-231 cells were imaged using a 20× Nikon CFI Plan Apo Lambda objective (NA 0.75), either 1 frame per minute for 2 hours or 1 frame every 5 minutes for 17 hours. In these experiments, a camera binning of 2 × 2 was used. </p>
Example data for the paper "A deep learning system for predicting time to progression of diabetic retinopathy"
<p>Example data for the paper "A deep learning system for predicting time to progression of diabetic retinopathy"</p>
FunMap prediction scores for all gene pairs
<p>This gzipped TSV (Tab-Separated Values) file contains prediction scores generated by a predictive model trained on different types of feature set. The file structure is as follows:</p><p><strong>Column 1 (Gene Pair):</strong> This column represents pairs of gene ids. Each entry in this column consists of two gene ids, potentially denoting associations between genes.</p><p><strong>Column 2 (RNA Data Prediction):</strong> The values in this column represent prediction scores generated by the model using RNA features as input. </p><p><strong>Column 3 (Protein Data Prediction):</strong> This column contains prediction scores produced by the model when trained on protein features. </p><p><strong>Column 4 (Combined RNA and Protein Data Prediction):</strong> The values in this column represent prediction scores resulting from the model trained with both RNA and protein features.</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.