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356 results for “Machine learning models”
Supplementary datasets for the manuscript "Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states" - Part 1
<p><strong>Supplementary files containing datasets needed to reproduce the results of the manuscript "Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states" by S. Choudhury et al (https://doi.org/10.1101/2023.02.21.529387).</strong></p> <p>The code to use with these data and reproduce the manuscript results is available at https://github.com/EPFL-LCSB/renaissance and https://gitlab.com/EPFL-LCSB/renaissance. The execution of parts of this code is dependent on the SkimPy toolbox (https://github.com/EPFL-LCSB/skimpy). Refer to the readme files on the RENAISSANCE code repositories for more details.</p> <p>The dataset contains the following files:</p> <p>1. models.zip - contains thermodynamically curated steady-state and nonlinear kinetic models of <em>E. coli </em>metabolism used in this study. Also contains the samples of steady-state metabolite concentrations and metabolic fluxes used in the study presented in Figure 3 (steady-state samples used for preparing Figures 2 and 4).</p> <p>2. renaissance_incidence_results.zip - self-explanatory (Figure 2a and 2b)</p> <p>3. ODE_solutions.zip - self-explanatory (Figure 2c)</p> <p>4. bioreactor_simulations1-3.zip - self-explanatory (Figure 2d)</p> <p>5. steady_state_analysis.zip - RENAISSANCE results obtained for each of the steady states (Figure 3a)</p> <p>6. subspace_analysis.zip - RENAISSANCE results presented in Figure 3b-g</p> <p><strong>The remaining datasets are published in the following links</strong></p> <p><em> - https://doi.org/10.5281/zenodo.7930084</em></p> <p><em> - https://doi.org/10.5281/zenodo.10391802</em></p>
A global flood risk modeling framework built with climate models and machine learning - Submission - Data Supplement
<p>This contribution contains data, fitted statistical models, and an analysis script for the submitted manuscript "A global flood risk modeling framework built with climate models and machine learning" by David A. Carozza and Mathieu Boudreault.</p>
XIS: A daily spatiotemporal machine-learning model for environmental exposures in the contiguous United States
<p>These Parquet files contain the outputs used for many analyses and plots in the linked papers. For temperature and humidity, the full sets of observations for cross-validation aren't included because we used restricted-use MADIS data.</p>
ecDNA machine learning modeling
<h3><strong>1. Today (2024-06-27), we discovered an issue with the labeling of sample groups in one of the supplementary figures (Supplementary Figure 14c) in our published article. We have corrected the figure and present it here, and we extend our apologies to all readers for any confusion this may have caused (although no report received).</strong></h3> <h3><strong>2. The source data of supplementary figure 13 in the accompanying article table has been found to have issues, which were identified as a result of improper Excel operation. Here, we have uploaded the correct data table</strong></h3> <p>--------------------------------------------------</p> <p> </p> <p>1. ecDNA_cargo_gene_modeling_data.csv.gz</p> <p>The dataset contains features from 386 TCGA tumors for modeling ecDNA cargo gene prediction. It was converted from R data format with the following code. NOTE: columns 'sample' and 'gene_id' are not used for actual modeling but for identifying, and sampling purposes.</p> <p>library(data.table)</p> <p>data = readRDS("~/../Downloads/ecDNA_cargo_gene_modeling_data.rds")</p> <p>colnames(data)[3] = "total_cn"</p> <p>data.table::fwrite(data, file = "~/../Downloads/ecDNA_cargo_gene_modeling_data.csv.gz", sep = ",")</p> <p> </p> <p>2. gcap_pcawg_WGS_result.tar.gz</p> <p>GCAP analysis results for PCAWG allele-specific copy number profiles derived from WGS.</p> <p> </p> <p>3. gcap_tcga_snp6_result.tar.gz</p> <p>GCAP analysis results for TCGA allele-specific copy number profiles derived from SNP6 array.</p> <p> </p> <p>4. gcap_Changkang_WES_result.tar.gz</p> <p>GCAP analysis results for SYSUCC Changkang allele-specific copy number profiles derived from tumor-normal paired WES.</p> <p> </p> <p>5. tcga_overlap_gene_wgs.rds, tcga_overlap_gene_snp.rds and tcga_overlap_gene_wes.rds</p> <p>These datasets contain TCGA gene-level copy number results in R data format from overlapping samples (dataset above). WGS from PCAWG, SNP array, and WES from GDC portal.</p> <p> </p> <p>6. cellline-batch1.zip & cellline-batch1.zip</p> <p> </p> <p>GCAP results of cell line batch 1 and batch 2.</p> <p> </p> <p>7. AA_cellline_wgs.zip</p> <p>AA software results for cell line batch 1.</p> <p> </p> <p>8. Batch2_AA_summary.xlsx</p> <p>AA software results for cell line batch 2.</p> <p> </p> <p>9. FISH-for-supp-file.zip</p> <p>Extended raw FISH images from 12 CRC samples.</p> <p> </p> <p>10. SNU216.zip</p> <p>Extended AA and GCAP analysis on SNU216.</p> <p> </p> <p>11. aa_ffpe.zip and AA_summary_table_of_6_erbb2_ffpe_samples.xlsx</p> <p>Extended AA running files (all results) and result summary data for 6 GCAP predicted ERBB2 amp clinical samples.</p> <p> </p> <p>12. source data of fig.4</p> <p> </p> <p>13. source data of supp fig.2 subplots</p> <p> </p> <p>13. source data of supp fig.15</p> <p> </p> <p>14. GCAP result data objects for three ICB cohorts. Both gene-level and sample-level data included.</p> <p> </p> <p>15. PDX-P68: processed (AA and CNV) data of P68 from WGS and WES data.</p> <p> </p> <p>16. source data of supp fig.13</p> <p> </p> <p>17. updated supplementary figure 14</p>
Supplementary material for the publication: "Efficient Surrogate Models for Materials Science Simulations: Machine Learning-based Prediction of Microstructure Properties"
<p><span><span><span>This dataset contains supplementary code, images and models for the publication „Efficient Surrogate Models for Materials Science Simulations: Machine Learning-based Prediction of Microstructure Properties“.</span></span></span></p> <p> </p> <p><span><span><span>The content will be updated and additionally linked to the corresponding git repositories.</span></span></span></p>
Machine Learning-Based Bridge Maintenance Optimization Model for Maximizing Performance within Available Annual Budgets
<p>Effective maintenance planning for bridges is crucial for maintaining their performance, safety, and minimizing maintenance costs. Timely implementation of interventions can improve the performance of bridges and avoid the need for costly interventions. However, bridge maintenance is often delayed due to inadequate planning and budget allocation, as well as resource constraints such as funding. With availability of historical condition data of bridges in databases such as the National Bridge Inventory (NBI) and National Bridge Elements (NBE), there is an opportunity to use data-driven methods to predict deterioration of bridge elements and optimize their maintenance interventions to maximize performance of bridges. This paper presents the development of a novel system that uses Machine Learning (ML) techniques to predict condition of concrete bridge elements and binary linear programming optimization method to identify the optimal selection of maintenance interventions and their timing to maximize the performance of bridges while complying with available annual budgets. Four ML methods are explored: decision tree, random forest, gradient boosting, and support vector machines. The results of the ML evaluation show that, while the values of the predictive performance metrics varied for different elements, random forest method had the best performance for all elements. A case study of a concrete bridge is analyzed to evaluate the performance of the system and demonstrate its new capabilities. The case study results show that the developed model identifies optimal maintenance interventions for various annual budgets over a 50-year study period. The primary contributions of this research to the body of knowledge are: (1) development of a novel system that integrates machine learning techniques and linear programming for predicting bridge element conditions and optimizing maintenance interventions; (2) modeling and predicting the deterioration of bridge elements based on health index metric; and (3) generating long-term maintenance plans for each of bridge elements to maximize the performance of bridges within available annual budgets. The present system is expected to support decision makers, such as highway agencies, in allocating limited financial resources for bridge maintenance more efficiently and cost-effectively.</p>
Data from: A cost-effective blood DNA methylation-based age estimation method in domestic cats, Tsushima leopard cats (Prionailurus bengalensis euptilurus), and Panthera species, using targeted bisulfite sequencing and machine learning models
<p><span>Knowledge of individual age can help both in-situ and ex-situ conservation programs to design more efficient and suitable management plans for targeted wildlife species. DNA methylation is one of the epigenetic aging markers that has emerged as a promising tool that can estimate age with high accuracy using only a tiny amount of biological material, which can be collected in a minimally invasive way. Here, we sequenced five targeted genetic regions and used </span><span>8–23</span><span> selected CpG sites to build age estimation models with machine learning methods </span><span>with about only $3–7 per sample</span><span>, using blood samples of seven Felidae species—ranging from small to big, and domestic to endangered species: domestic cats (<em>Felis catus</em>, 139 samples), Tsushima leopard cats (<em>Prionailurus bengalensis euptilurus</em>, 84 samples), and five<em> Panthera </em>species (96 samples). </span><span>The models built achieved satisfactory accuracy—the mean absolute error of the best models was 1.966, 1.348, and 1.552 years in domestic cats, Tsushima leopard cats, and <em>Panthera</em> spp., respectively.</span><span> Our models in domestic cats and Tsushima leopard cats were applicable to individuals regardless of health conditions, indicating the high applicability of our models to samples collected from diverse situations, e.g., rescued individuals in the context of conservation. We also showed the possibility of developing universal age estimation models for the five<em> Panthera</em> spp. using two of the five genetic regions, suggesting an even lower cost to use our models for future applications.</span></p>
Calibration-free reaction yield quantification by HPLC with a machine-learning model of extinction coefficients
<p>This repository contains all the data and code associated with the manuscript "Calibration-free reaction yield quantification by HPLC with a machine-learning model of extinction coefficients"</p> <p>Mass spec and absorption chromatogram data are in reaction_set_1.zip, reaction_set_2.zip, and simulated_reaction_set.zip. The chemprop model trained on the Deep4Chem dataset is in Deep4Chem_chemprop.zip.</p>
TimeSpec4LULC: A Smart-Global Dataset of Multi-Spectral Time Series of MODIS Terra-Aqua from 2000 to 2021 for Training Machine Learning models to perform LULC Mapping
<p>TimeSpec4LULC is a smart open-source global dataset of multi-spectral time series for 29 Land Use and Land Cover (LULC) classes ready to train machine learning models. It was built based on the seven spectral bands of the MODIS sensors at 500 m resolution from 2000 to 2021 (262 observations in each time series). Then, was annotated using spatial-temporal agreement across the 15 global LULC products available in Google Earth Engine (GEE).</p> <p>TimeSpec4LULC contains two datasets: the original dataset distributed over 6,076,531 pixels, and the balanced subset of the original dataset distributed over 29000 pixels.</p> <p>The original dataset contains 30 folders, namely "Metadata", and 29 folders corresponding to the 29 LULC classes. The folder "Metadata" holds 29 different CSV files describing the metadata of the 29 LULC classes. The remaining 29 folders contain the time series data for the 29 LULC classes. Each folder holds 262 CSV files corresponding to the 262 months. Inside each CSV file, we provide the seven values of the spectral bands as well as the coordinates for all the LULC class-related pixels.</p> <p>The balanced subset of the original dataset contains the metadata and the time series data for 1000 pixels per class representative of the globe. It holds 29 different JSON files following the names of the 29 LULC classes.</p> <p>The features of the dataset are:</p> <p>- ".geo": the geometry and coordinates (longitude and latitude) of the pixel center.</p> <p>- "ADM0_Code": the GAUL country code.</p> <p>- "ADM1_Code": the GAUL first-level administrative unit code.</p> <p>- GHM_Index": the average of the global human modification index.</p> <p>- "Products_Agreement_Percentage": the agreement percentage over the 15 global LULC products available in GEE.</p> <p>- "Temporal_Availability_Percentage": the percentage of non-missing values in each band.</p> <p>- "Pixel_TS": the time series values of the seven spectral bands.</p>
Sticky Pi -- Machine Learning Data, Configuration and Models
<p><strong>Dataset for the Machine Learning section of the Sticky Pi project (https://doc.sticky-pi.com/)</strong></p> <p>Contains the dataset for the three algorithms described in the publication: Universal Insect Detector, Siamese Insect Matcher and Insect Tuboid Classifier.</p> <p><strong>Universal Insect Detector:</strong></p> <p>`universal_insect_detector/` contains training/validation data, configuration files to train the model, and the model as trained and used for publication.</p> <ul> <li>`data/` – A set of svg images that contain the embedded jpg raw image, and a set of non-intersecting polygon around the labelled insects</li> <li>`output/` <ul> <li>`model_final.pth` – the model as trained for the publication</li> </ul> </li> <li>`config/` <ul> <li>`config.yaml `– The configuration file defining the hyperparameters to train the model</li> <li>`mask_rcnn_R_101_C4_3x.yaml` – the base configuration file from which config is derived</li> </ul> </li> </ul> <p> </p> <p><strong>Siamese Insect Matcher</strong></p> <p>`siamese_insect_matcher/` contains training/validation data, configuration files to train the model, and the model as trained and used for publication.</p> <ul> <li>`data/` – a set of svg images that contain two embedded jpg raw images vertically stacked corresponding to two frames in a series. Each predicted insect is labelled as a polygon. Insects that are labelled as the same instance, between the two frames, are grouped (i.e. SVG group). The filename of each image is `<device>.<datetime_frame_1>.<datetime_frame_2>.svg`</li> <li>`output/` <ul> <li>`model_final.pth` – the model as trained for the publication</li> </ul> </li> <li>`config/` <ul> <li>`config.yaml` – The configuration file defining the hyperparameters to train</li> </ul> </li> </ul> <p><strong>Insect Tuboid Classifier:</strong></p> <p>`insect_tuboid_classifier/` contains images of insect tuboid, a database file describing their taxonomy, a configuration file to train the model, and the model as trained and used for publication.</p> <ul> <li>`data/` <ul> <li>`database.db`: a sqlite file with a single table `ANNOTATIONS`. The table maps a unique identifier of each tuboid (tuboid_id) to a set of manually annotated taxonomic variables.</li> <li>A directory tree of the form: `<series_id>/<tuboid_id>/`. Each terminal directory contains: <ul> <li> <ul> <li>`tuboid.jpg` – a jpeg image made of 224 x 224 tiles representing all the shots in a tuboid, left to right, top to bottom – might be padded with empty images</li> <li>`metadata.txt` – a csv text file with columns: <ul> <li> <ul> <li>parrent_image_id – <device>.<UTC_datetime></li> <li>X – the X coordinates of the object centroid</li> <li>Y – the Y coordinates of the object centroid</li> </ul> </li> </ul> </li> <li>scale – The scaling factor applied between the original and image and the 224 x 224 tile (>1 => image was enlarged)</li> <li>`context.jpg` – a representation of the first whole image of a series, with a box around the first tuboid shot (this is for debugging/labelling purposes)</li> </ul> </li> </ul> </li> </ul> </li> <li>`output/` <ul> <li>`model_final.pth` – the model as trained for the publication</li> </ul> </li> <li>config/ <ul> <li>`config.yaml` – The configuration file defining the hyperparameters to train the model as well as the taxonomic labels</li> </ul> </li> </ul>
Development of a machine learning model to predict non- durable response to anti-TNF therapy in Crohn's disease using transcriptome imputed from genotypes
<p>This is the expression value predicted using PrediXcan version 7 to find a gene feature that can distinguish between patients with and without effect on infliximab.</p> <p>Among the various tissue models provided by PrediXcan v7, three models were selected and used: whole blood, Colon transverse, and terminal ileum of small intestine, and the predicted gene counts of each model were 6,294, 5,612 and 3,107.</p> <p>For each of the three models, predicted gene expression values and phenotype information per sample were submitted.</p>
Molecular similarity perception based on machine-learning models
<p>Molecular similarity is an particularly important notion for chemical legislation, specifically in the evaluation process for orphan drugs (i.e., drugs for rare diseases). A new molecule needs to be dissimilar from any other existing drug for a given disease to be assigned the financially advantageous status of orphan drug. Currently, there are many ways to define whether two molecules are similar or dissimilar. Thus far, the European Medicines Agency has used experts majority voting on discretional judgments of similarity when assessing new drugs for rare diseases. The decision of individual expert whether two compounds are similar is inherently subjective, depending on factors such as gender, age, state of mind, and previous experiences. It is therefore desirable, in this context, to benefit from an objective measure of similarity. To answer this need, we report a new dataset of molecular similarity assessments, that includes complex and difficult similarity scenarios. As a result, we propose new and improved models for similarity-prediction procedures, including 3D properties. These models are publicly available: <a href="https://chematlas.chimie.unistra.fr/ReadySim/">https://chematlas.chimie.unistra.fr/ReadySim/</a>.</p> <p>Software, 3D structures and pictures are available in the git related to this deposit: <a href="https://github.com/enricogandini/paper_similarity_prediction.git">https://github.com/enricogandini/paper_similarity_prediction.git</a></p> <p>The deposit contains two files.</p> <ul> <li>original_training_set.csv: this is one of the dataset published initially in [doi: 10.1186/1758-2946-6-5].</li> <li>new_dataset.csv: result from a new survey organized in 2020</li> </ul> <p>The columns are the following:</p> <ul> <li>id_pair: unique identifier of the compound pair</li> <li>curated_smiles_molecule_a: first compound of the pair</li> <li>curated_smiles_molecule_b: second compound of the pair</li> <li>tanimoto_cdk_Extended: ECFP similarity measure</li> <li>TanimotoCombo: ComboScore similarity measure</li> <li>pchembl_distance: difference of activity of the compound pair</li> <li>target_name: protein to which the compound pair is binding</li> <li>simil_2D: similar based on ECFP (0 or 1)</li> <li>simil_3D: similar based on ComboScore (0 or 1)</li> <li>dissimil_2D: dissimilar based on ECFP (0 or 1)</li> <li>dissimil_3D: dissimilar based on ComboScore (0 or 1)</li> <li>pair_type: pairs are classified based on ECFP and ComboScore as similar or dissimilar in 2D and 3D - Sim2DSim3D, Sim2DDis3D, Dis2D,Sim3D, Dis2DSim3D</li> <li>n_answers: number of answers from experts</li> <li>n_similar: number of answers labeling the pair as similar compounds</li> <li>frac_similar: n_similar/n_answers</li> </ul>
Global input datasets for use in constraints on global seafloor biogenic methane production from deterministic and machine learning modeling
<p>This dataset includes 9 grids used as model input for manuscript "Constraints on global seafloor biogenic methane production from deterministic and machine learning modeling". Additionally, there are four grids (heat flow, total organic carbon, porosity, and crust age) for which variable uncertainty was given.</p> <p>Grids here are available in xyz (longitude in decimal degrees, latitude in decimal degrees, and variable) ascii file format. Each reference is below is the grids native reference. For more information on the creation of these grids please visit the main manuscript.</p> <p>Below are respective file names and variable name/units:</p> <p>Dataset 1: Elevation in Meters (+ indicates above sea level, - below sea level)</p> <p>Tozer, B., Sandwell, D. T., Smith, W. H. F., Olson, C., Beale, J. R., & Wessel, P. (2019). Global bathymetry and topography at 15 arc sec: SRTM15+. <em>Earth and Space Science</em>, 6. https://doi.org/10.1029/ 2019EA000658</p> <p>Dataset 2: Seawater Density in Kilograms per Cubic Meter</p> <p>Boyer, T. P., Antonov, J. I., Baranova, O. K., Garcia, H. E., Johnson, D. R., Mishonov, A. V., … Grodsky, A. (2013). World Ocean Database 2013. In S. Levitus, A. Mishonov (Ed.), Technical Ed.; <em>NOAA Atlas NESDIS</em> 72 (pp. 209).</p> <p>Dataset 3: Seawater Temperature in Degrees Celcius </p> <p>Boyer, T. P., Antonov, J. I., Baranova, O. K., Garcia, H. E., Johnson, D. R., Mishonov, A. V., … Grodsky, A. (2013). World Ocean Database 2013. In S. Levitus, A. Mishonov (Ed.), Technical Ed.; <em>NOAA Atlas NESDIS</em> 72 (pp. 209).</p> <p>Dataset 4: Seawater Salinity in Percent Salinity Units</p> <p>Boyer, T. P., Antonov, J. I., Baranova, O. K., Garcia, H. E., Johnson, D. R., Mishonov, A. V., … Grodsky, A. (2013). World Ocean Database 2013. In S. Levitus, A. Mishonov (Ed.), Technical Ed.; <em>NOAA Atlas NESDIS</em> 72 (pp. 209).</p> <p>Dataset 5: Heat Flow in Milliwatts per Square Meter</p> <p>Global Heat Flow Compilation Group (2013). Component parts of the World Heat Flow Data Collection. <em>PANGAEA</em>, https://doi.org/10.1594/PANGAEA.810104</p> <p>Hornbach, M. J., Harris, R. N. & Phrampus, B. J. (2020). Heat flow on the U.S. Beaufort Margin, Arctic Ocean: Implications for ocean warming, methane hydrate stability, and regional tectonics. <em>Geochemistry, Geophysics, Geosystems</em>, 21(5). e2020GC008933. https://doi.org/10.1029/2020GC008933</p> <p>Dataset 6: Sediment Thickness in Meters</p> <p>Straume, E. O., Gaina, C., Medvedev, S., Hochmuth, K., Gohl, K., Whittaker, J. M., … Hopper, J. R. (2019). GlobSed: updated total sediment thickness in the world’s oceans. <em>Geochemistry, Geophysics, Geosystems</em>, 20(4), 1756–1772.</p> <p>Dataset 7: Seafloor Porosity in Fraction</p> <p>Martin, K. M., Wood, W. T., & Becker, J. J. (2015). A global prediction of seafloor sediment porosity using machine learning. <em>Geophysical Research Letters</em>, 42(24), 2015GL065279. https://doi.org/10.1002/2015GL065279</p> <p>Dataset 8: Seafloor Total Organic Carbon in Percent Dry Weight</p> <p>Lee, T.R., Wood, W.T., & Phrampus, B.J. (2019). A machine learning (kNN) approach to predicting global seafloor total organic carbon. <em>Global Biogeochemical Cycles</em>. 33, 37–46, doi:10.1029/2018GB005992.</p> <p>Dataset 9: Crust Age in Million Years</p> <p>Müller, R. D., Sdrolias, M., Gaina, C., & Roest, W. R. (2008). Age, spreading rates, and spreading asymmetry of the world’s ocean crust. <em>Geochemistry, Geophysics, Geosystems</em>, 9, Q04006. https://doi.org/10.1029/2007GC001743</p> <p>Dataset 10: Seafloor Porosity Uncertainty in Fraction</p> <p>Dataset 11: Seafloor Total Organic Carbon Uncertainty in Percent Dry Weight</p> <p>Lee, T.R., Wood, W.T., & Phrampus, B.J. (2019). A machine learning (kNN) approach to predicting global seafloor total organic carbon. <em>Global Biogeochemical Cycles</em>. 33, 37–46, doi:10.1029/2018GB005992.</p> <p>Dataset 12: Heat Flow Uncertainty in Milliwatts per Square Meter</p> <p>Dataset 13: Crust Age Uncertainty in Million Years</p> <p>Müller, R. D., Sdrolias, M., Gaina, C., & Roest, W. R. (2008). Age, spreading rates, and spreading asymmetry of the world’s ocean crust. <em>Geochemistry, Geophysics, Geosystems</em>, 9, Q04006. https://doi.org/10.1029/2007GC001743</p>
Machine Learning Models and New Computational Tool for the Discovery of Insect Repellents that Interfere with Olfaction
<ul> <li><strong>SI1_Supporting Information</strong> file (docx) brings together detailed information on the outstanding models obtained for each dataset analyzed in this study such as statistical and training parameters and outliers. There can be found the responses in spikes/s of the mosquito <em>Culex quinquefasciatus </em>to the 50 IRs. Besides, there is presented a full table of the up-to-date studies related to QSAR and insect repellency.</li> <li><strong>SI2_EXP1_50IRs from Liu et al (2013)</strong> SDF file presents the structures of each of the 50 IRs analyzed.</li> <li><strong>SI3_EXP2_Datasets</strong> gathers the four datasets as SDF files from Oliferenko <em>et al.</em> (2013), Gaudin<em> et al. </em>(2008), Omolo <em>et al.</em> (2004), and Paluch <em>et al.</em> (2009) used for the repellency modeling in <strong>EXP2</strong>.</li> <li><strong>SI4_EXP3_Prospective analysis </strong>provides Malaria Box Library (400 compounds) as an SDF file, which were analyzed in our virtual screening to prospect potential virtual hits.</li> <li><strong>SI5_QuBiLS-MIDAS MDs lists</strong> contain three TXT lists of 3D molecular descriptors used in QuBiLS-MIDAS to describe the molecules used in the present study.</li> <li><strong>SI6_EXP1_Sensillar Modeling</strong> comprises two subfolders: Classification and Regression models for each of the six sensilla. Models built to predict the physiological interaction experimentally obtained from Liu <em>et al.</em> (2013). All of the models are implemented in the software SiLiS-PAPACS. Every single folder compiles a DOCX file with the detailed description of the model, an XLSX file with the output obtained from the training in Weka 3.9.4, an ARFF, and CSV files with the MDs for each molecule, and the SDF of the study dataset.</li> <li><strong>SI7_EXP2_Repellency Modeling </strong>encompasses the four datasets in the study: Oliferenko <em>et al.</em> (2013), Gaudin<em> et al. </em>(2008), Omolo <em>et al.</em> (2004), and Paluch <em>et al.</em> (2009). Inside the subfolders, there are three models per type of MDs (duplex, triple, generic, and mix) selected that best predict each dataset. As well as the SI6 folder, each model includes six files: DOCX, XLSX, ARFF, CSV, and an SDF.</li> <li><strong>SI8_Virtual Hits </strong>includes the cluster analysis results and physico-chemical properties of new IR virtual leads.</li> </ul>
Dataset for Integrated hydrodynamic and machine learning models
<p>The dataset is the supplement to our publication in <a href="https://www.nonlinear-processes-in-geophysics.net/">Nonlinear Processes in Geophysics</a> (https://doi.org/10.5194/npg-2021-36). To use this data, please give us credit by citing our article.</p>
Data from: Modeling the drivers of eutrophication in Finland with a machine learning approach
<p>The dataset contains data on characteristics of 1547 Finnish EU Water Framework Directive monitored lakes and their catchments from years 2016-2019.</p> <p> </p> <p><strong>Usage notes</strong></p> <p>The zip-file contains catchment and lake characteristics data to support the main analysis code in Github (link) (‘Analyses and visualization’) which produces results, figures and tables presented in the study ‘Modeling the drivers of eutrophication in Finland with a machine learning approach’. Detailed information about the data can be found in the ‘README.docx’ file.</p>
On the Application of Machine Learning Models to Assess and Predict Software Reusability
<p>This is the dataset, results and notebook for the submission into Maltesque 2022 conference.</p>
Assessing predictive performance of supervised machine learning algorithms for a diamond pricing model
<p>The diamond is 58 times harder than any other mineral in the world, and its elegance as a jewel has long been appreciated. Forecasting diamond prices is challenging due to nonlinearity in important features such as carat, cut, clarity, table, and depth. Against this backdrop, the study conducted a comparative analysis of the performance of multiple supervised machine learning models (regressors and classifiers) in predicting diamond prices. Eight supervised machine learning algorithms were evaluated in this work including Multiple Linear Regression, Linear Discriminant Analysis, eXtreme Gradient Boosting, Random Forest, k-Nearest Neighbors, Support Vector Machines, Boosted Regression and Classification Trees, and Multi-Layer Perceptron. The analysis is based on data preprocessing, exploratory data analysis (EDA), training the aforementioned models, assessing their accuracy, and interpreting their results. Based on the performance metrics values and analysis, it was discovered that eXtreme Gradient Boosting was the most optimal algorithm in both classification and regression, with a R<sup>2</sup> score of 97.45% and an Accuracy value of 74.28%. As a result, eXtreme Gradient Boosting was recommended as the optimal regressor and classifier for forecasting the price of a diamond specimen.</p>
Supplementary data: A machine learning approach for dynamical modelling of Al distributions in zeolites via 23Na/27Al solid-state NMR
<p><strong>Content:</strong></p> <p>This dataset provides supplementary data to "A machine learning approach for dynamical modelling of Al distributions in zeolites via 23Na/27Al solid-state NMR". It contains trained Neural Network Potentials (NNP), energy and force data used for accuracy evaluation of the NNPs. Energy and forces are stored as ASE trajectory files (traj), readable by the <a href="https://wiki.fysik.dtu.dk/ase/index.html">Atomic Simulation Environment </a>(ASE). In addition, this repository contains the generated training database with DFT (SCAN+D3(BJ)) energies and forces as SchNetPack1.0 database (SiAlOHNa.db) file readable by ASE and <a href="https://github.com/atomistic-machine-learning/schnetpack/tree/schnetpack1.0">SchNetPack version 1.0</a>. Also, the structure files used to calculate NMR properties are involved.</p> <ul> <li>"nnps.zip" - (pytorch) NNP model files (compatible with <a href="https://github.com/atomistic-machine-learning/schnetpack/tree/schnetpack1.0">SchNetPack version 1.0</a>)</li> <li>"SiAlOHNa.db" - DFT (SCAN+D3(BJ)) training database as SchNetPack1.0 database file readable by ASE and <a href="https://github.com/atomistic-machine-learning/schnetpack/tree/schnetpack1.0">SchNetPack version 1.0</a></li> <li>"error_stats.zip" - traj files storing energies/forces at the DFT (SCAN+D3(BJ)) and NNP level for all test simulations to calcuate energy/force errors</li> <li>"Structures_CHA17.zip" - the structures files of CHA(17). </li> </ul>
Cloud to Thing Continuum based Sports Monitoring System using Machine Learning and Deep Learning Model
<p><span>Sports monitoring and analysis have seen significant advancements with the integration of cloud computing and continuum paradigms, facilitated by machine learning and deep learning techniques. In this study, we present a novel approach for sports monitoring that seamlessly transitions from traditional cloud-based architectures to a continuum paradigm, enabling real-time analysis and insights into player performance and team dynamics. Leveraging machine learning and deep learning algorithms, our framework offers enhanced capabilities for player tracking, action recognition, and performance evaluation in various sports scenarios. This research proposes a Cloud-to-Thing Continuum based Sports Monitoring System utilizing Machine Learning (ML) and Deep Learning (DL) models. The system integrates data acquisition, preprocessing, feature extraction, cloud-based processing, continuum paradigm integration, and decision-making stages. It leverages innovative techniques such as Improved Mask R-CNN for pose estimation, hybrid metaheuristic algorithms with Generative Adversarial Network (GAN) for classification, and fuzzy decision-making Based on the integrated analysis, decisions are made regarding player performance, team strategies, and tactical adjustments. The continuum approach ensures a balance between centralized cloud processing and distributed edge processing, optimizing resource utilization and reducing latency. Through this system, real-time analysis of sports events is achieved, enabling immediate feedback for time-sensitive applications.</span></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.