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82 results for “Predictive Simulations”
Simulated NGS read datasets for bacterial pathogenic potential prediction
<p>## Predicting pathogenic potentials from NGS reads: novel bacterial species</p> <p>This repository contains simulated Illumina read datasets for bacterial pathogenic potential prediction and associated metadata extracted from the IMG Database (https://img.jgi.doe.gov/). The reads are 250bp long and were simulated with Mason (https://www.seqan.de/apps/mason/) from genomes downloaded from NCBI. The training-validation-test split was done on the species level to ensure "novelty" of validation and test species. The training sets contain 10 million reads per class, validation sets - 1.25 million reads per class, and test sets - 1.25 million paired reads per class. Additional, imbalanced training sets contain 2.5 million "nonpathogenic" and 17.5 million "pathogenic" reads, keeping the mean covarage constant for all species. The temporal benchmark test set contains reads from 3 additional pathogenic species in the Pantoea genus.</p> <p>## Predicting pathogenic potentials from NGS reads: novel strains of known species</p> <p>The BacPaCS datasets contain reads simulated from the dataset compiled by Barash et al. (https://doi.org/10.1093/bioinformatics/bty928). It this case, the training-validation-test split was done on the strain level (so different strains of the same species may be present in all three sets).</p>
Predictive simulations of ionization energies of solvated halide ions with relativistic embedded Equation of Motion Coupled-Cluster Theory: Figures
<p>This entry contains the sources for the figures included in the body of the paper titled "Predictive simulations of ionization energies of solvated halide ions with relativistic embedded Equation of Motion Coupled-Cluster Theory", by Yassine Bouchafra, Avijit Shee, Florent Réal, Valérie Vallet and André Severo Pereira Gomes, as well as those found in the supplementary information.</p> <p>It accompanies the dataset found at the DOI: 10.5281/zenodo.1477004</p> <p> </p> <p> </p>
Vertical Profiles of Convection-Permitting Simulations for Predicting Thunderstorm Occurrence
<p>This repository contains datasets for training and evaluation of the machine learning (ML) models in K. Vahid Yousefnia et al., <em>Inferring Thunderstorm Occurrence from Vertical Profiles of Convection-Permitting Simulations: Physical Insights from a Physical Deep Learning Model</em>, 2024 (submitted to <em>Artificial Intelligence for the Earth Systems,</em> preprint available at https://arxiv.org/abs/2409.20087).</p>
Simulated NGS read datasets for prediction of novel fungal pathogens and multiple pathogen classes
<p>This repository contains simulated Illumina read datasets for novel fungal pathogen prediction and real-time detection of multiple pathogen classes. They were used to train the models hosted at <a href="https://zenodo.org/record/5711877">https://zenodo.org/record/5711877</a>.<br> The reads were simulated with Mason (<a href="https://www.seqan.de/apps/mason/">https://www.seqan.de/apps/mason/</a>) from genomes downloaded from NCBI, based on metadata stored in a manually curated database (<a href="https://zenodo.org/record/5846345">https://zenodo.org/record/5846345</a>).</p> <p>We provide the following:</p> <p>1) An rds file describing assignment of fungal species from the database (<a href="https://zenodo.org/record/5846345">https://zenodo.org/record/5846345</a>) to training, validation and test sets (TrainValTest_fungi.rds). A second rds file (TrainValTest_temporal.rds) includes species added within 12 weeks after the original datasets were compiled. Those species were used for a temporal benchmark.</p> <p>2) Fungal validation and test sets. Each contains 1.25 million, 250bp-long reads simulated from non-overlapping sets of human ("pathogenic") or non-human ("nonpathogenic") pathogens. The test set contains paired reads ("_1" and "_2" for the first and second mate). The number of reads per species is proportional to the respective genome length. An additional, temporal test set (*temporal*fasta.gz) includes 15 species added after 12 weeks from the consturction of the original datasets.</p> <p>3) Fungal training sets. They contain 250bp-long reads simulated from species not present in the validation or test sets. There are four variants:<br> 3a) "low-coverage, linear" - 20 million reads, number of reads per species proportional to genome length<br> 3b) "low-coverage, logarithmic" - 20 million reads, number of reads per species proportional to the logarithm of genome length ("log")<br> 3c) "high-coverage, linear" - 240 million reads, number of reads per species proportional to genome length ("24")<br> 3d) "high-coverage, logarithmic" - 240 million reads, number of reads per species proportional to the logarithm of genome length ("24log")</p> <p>4) Training, validation and test sets for the multiclass models. They should be used together with the "pathogenic" read sets hosted at <a href="https://zenodo.org/record/4456857">https://zenodo.org/record/4456857</a>. Here, we share sets for two of the four total classes:<br> 4a) The 'non-pathogen' class is a mixture of "nonpathogenic" biacterial and viral read sets, concatenated and downsampled to the original read number (20M for training, 1.25M for validation and test). The training and validation sets contain mixed-length (25-20bp) simulated subreads (original sets hosted here: <a href="https://zenodo.org/record/4456857">https://zenodo.org/record/4456857</a>). The test set contains 250bp long reads based on the test sets from here: <a href="https://zenodo.org/record/3678563">https://zenodo.org/record/3678563</a> and here: <a href="https://zenodo.org/record/4312525">https://zenodo.org/record/4312525</a>; it was also sorted by species.<br> 4b) Mixed-length versions of the "pathogenic" fungal training and validation sets, prepared by random shortening of the "low-coverage" read sets in the "linear" (_rn_) and "logarithmic" (_rn_*log_) flavours.</p> <p>See also the preprint: <a href="https://www.biorxiv.org/content/10.1101/2021.11.30.470625">https://www.biorxiv.org/content/10.1101/2021.11.30.470625</a></p>
data set to bioRxiv preprint 'Persistent cross-species SARS-CoV-2 variant infectivity predicted via comparative molecular dynamics simulation
<p>This is supporting data and software code for the following preprint in bioRxiv</p> <p><strong>Persistent cross-species SARS-CoV-2 variant infectivity predicted via comparative molecular dynamics simulation</strong></p> <p>https://www.biorxiv.org/content/10.1101/2022.04.18.488629v1</p>
Binding Affinity Prediction Workflow - Simulation Input Files and Absolute Binding Free Energies
<p>The Binding Affinity Prediction (BAP) workflow calculates absolute binding free energies for protein-ligand complexes by taking their crystal structures, converting them into input files for molecular dynamics (MD) simulations with GROMACS after they have passed extensive quality checks, and analysing the resulting trajectories with the Generalised Born model of implicit solvation as implemented in gmx_MMPBSA to obtain the free-energy estimates. The workflow was designed for soluble proteins without post-translational modifications, co-factors and non-standard amino acids, and it has limited support for coordinated ions.</p> <p>For the dataset published here, the BAP workflow was run on the PDBbind 2020 (http://www.pdbbind.org.cn/index.php) refined set. This entry contains the MD simulation input files (BAPSimulationInputFiles.tar.gz) and the ABFE estimates (BAPBindingFreeEnergyEstimates.csv) obtained from four 250 ns trajectories for each complex. The MD simulations for more than 4000 complexes were run on the Leonardo supercomputer while the implicit-solvent calculations were carried out on Galileo, both operated by Cineca (Italy). The MD trajectories will be stored at Cineca for approx. 1 year after publication of this entry; contact Cineca's user support if you are interested in the trajectories.</p> <p>The README file describes how to reproduce the MD trajectories and the subsequent implicit-solvent calculations yielding the free-energy estimates. The workflow scripts can be downloaded from GitHub (https://github.com/LigateProject/Binding-Affinity-Prediction-workflow). The MD simulations were run with GROMACS 2023.2 (https://manual.gromacs.org/2023.2/index.html), and the implicit-solvent calculations were carried out with gmx_MMPBSA 1.6.1 (https://valdes-tresanco-ms.github.io/gmx_MMPBSA/v1.6.1/).</p>
Evaluating the predictive character of the method of Constrained Geometries Simulate External Force with Density Functional Theory.
<p>## Abstract</p> <p>from [1]:</p> <p>Mechanochemistry is a fast-developing field of interdisciplinary research with a growing number of applications. Therefore, many theoretical methods have been developed to quickly predict the outcome of mechanically induced reactions. Constrained geometries simulate External Force (CoGEF) is one of the earlier methods in this field. It is easily implemented and can be conducted with most DFT codes. However, recently, we observed totally different predictions for model systems of epoxy resins in different conformations and with different density functionals. To better understand the conformational and functional dependence in typical CoGEF calculations we present a systematic evaluation of the CoGEF method for different model systems covering homolytic and heterolytic bond cleavage reactions, electrocyclic ring opening reactions and scission of non-covalent interactions in hydrogen-bond complexes. From our calculations we observe that many mechanochemical descriptors strongly depend on the functional used, however, a systematic trend exists for the relative maximum Force. In general, we observe that the CoGEF procedure is forcing the system to high energetic regions on the molecular potential energy profiles, which can lead to unexpected and uncorrelated predictions of mechanochemical reactions. This is questioning the true predictive character of the method.</p> <p> </p> <p>## Contact</p> <p>Christian R. Wick</p> <p>Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Faculty of Science, Department of Physics, PULS Group, Interdisciplinary Center for Nanostructured Films (IZNF), Cauerstrasse 3, 91058, Germany</p> <p> </p> <p>## License</p> <p>Creative Commons Attribution 4.0 International</p> <p> </p> <p>## Context</p> <p>Dataset to paper [1]</p> <p> </p> <p>## Contents</p> <ul> <li>All COGEF trajectories in xyz format.</li> <li>All CoGEF distances and DFT Energies in csv format.</li> </ul> <p>The following DFT levels of theory were investigated:</p> <ul> <li>B3LYP/6-31G(d)</li> <li>B3LYP-D3BJ/def2-SVP</li> <li>BP86-D3/def2-SVP</li> <li>PBE1PBE/def2-SVP</li> <li>M06-D3/def2-SVP</li> </ul> <p> </p> <p>## Folder structure</p> <ul> <li>- compound_X : data set for compound number X (numbering corresponds to the numbering scheme in [1]) <ul> <li>the xyz trajectories follow the following naming convention: "DFT_method"_"unrestricted/restricted".xyz</li> <li>the csv files follow the naming convention: "DFT_method"_"unrestricted/restricted".xyz.csv</li> </ul> </li> </ul> <p>## Software</p> <p>### COGEFF calculations: COGEF.py v1.8.0</p> <p>Zenodo release:</p> <p>https://doi.org/10.5281/zenodo.7079733</p> <p>### DFT calculations:</p> <p>Gaussian 16 Rev B [2]</p> <p> </p> <p>## Funding</p> <p>This research was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - 377472739/GRK 2423/1-2019 FRASCAL.</p> <p><br> ## References</p> <p>[1] C. R. Wick, E. Topraksal, D. M. Smith, A.-S. Smith, "Evaluating the predictive character of the method of Constrained Geometries Simulate External Force with Density Functional Theory.", Forces in Mechanics, 9, 100143; doi:10.1016/j.finmec.2022.100143</p> <p>[2] Frisch, M. J.; Trucks, G. W.; Schlegel, H. B.; Scuseria, G. E.; Robb, M. A.; Cheeseman, J. R.; Scalmani, G.; Barone, V.; Petersson, G. A.; Nakatsuji, H.; et al. Gaussian 16 Rev. B.01, 2016.</p>
Simulated NGS read datasets for novel human virus prediction
<p>This repository contains simulated Illumina read datasets for novel human virus prediction and associated metadata extracted from the Virus Host Database (<a href="https://www.genome.jp/virushostdb/">https://www.genome.jp/virushostdb/</a>). The reads are 250bp long and were simulated with Mason (<a href="https://www.seqan.de/apps/mason/">https://www.seqan.de/apps/mason/</a>) from genomes downloaded from NCBI. The training-validation-test split was done on whole viral sequences to ensure "novelty" of validation and test viruses. The training sets contain 10 million reads per class, validation sets - 1.25 million reads per class, and test sets - 1.25 million paired reads per class. The negative class sets contain reads simulated from chordate-infecting ("cho"), metazoan-infecting ("met"), eukariote-infecting ("euk") and all-nonhuman viruses. The positive class contains human-infecting viruses. The stratified dataset ("strat") contains an equal number of reads from "cho", "met but not cho", "euk but not met" and "all but not euk". </p> <p>Species-level datasets ("humspec", "allspec" and "chospec", with the corresponding fasta and *_species.rds files) are constructed analogously, but ensuring that all viruses of a given species were assigned to either training, val or test set. This is a stricter setting modelling a "novel viral species" scenario while reflecting within-species phenotype diversity.</p> <p>blast_hits.gz contains blast hits of human virome reads form Moustafa et al., 2017 (https://doi.org/10.1371/journal.ppat.1006292) blasted against our training database (see paper for details). In the second column you can find the matched label and the accession number of the matched reference. blast_labels_complete.gz contains extracted labels for all virome reads, including those without any matches. Note: one of the read headers (>3c8ac47039d32b11c8fe23f588e444e9) from Moustafa et al. is slightly corrupted with null characters. You can remove them with sed 's/\x0//g' or equivalent.</p> <p> </p>
Accompanying dataset for: Predicting Species Emergence in Simulated Complex Pre-Biotic Networks
<p>This is the accompanying data and code for the publication [Markovitch & Krasnogor: Predicting Species Emergence in Simulated Complex Pre-Biotic Networks] containing the full set of 10,000 lognormal networks studied, their network communities and the compotype species observed during simulations with the GARD model. Details are given in the aforementioned paper. Please see also: http://ico2s.org/</p> <p>This work was funded by the UK's Engineering and Physical Sciences Research Council (EPSRC) under projects (EP/J004111/2) "Towards a Universal Biological-Cell Operating System (AUdACiOuS)" and (EP/N031962/1) "Synthetic Portabolomics: Leading the way at the crossroads of the Digital and the Bio Economies"</p> <p> </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>
Simulated Herbarium data for testing the accuracy with which specimen data can predict the timing and duration of population-level flowering displays
<p>This dataset provides code and example data for simulating specimen collections of flowering plants across North America, and for developing phenological predictions of population-level flowering onset and termination for these data. It further presents code for assessing the accuracy of these predictions relaticve to known (simulated) population-level flowering dates at the location of each collection.</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>
Predictive simulations of core electron binding energies of halogenated species adsorbed on ice surfaces from relativistic quantum embedding calculations
<p>This dataset collects the unprocessed (= outputs from calculations) and processed (= plots, average values for orbital and ionization energies) results discussed in the paper titled "Predictive simulations of core electron binding energies of halogenated species adsorbed on ice surfaces from relativistic quantum embedding calculations" by Richard Asamoah Opoku, Céline Toubin, and André Severo Pereira Gomes.</p>
Microclimate simulation output: "Between vision and action: the predicted effects of co-designed green infrastructure solutions on environmental burdens"
<p>The following microclimate simulation dataset supports the paper "Between vision and action: the predicted effects of co-designed green infrastructure solutions on environmental burdens" by Mathias Schaefer, published in Urban Ecosystems (2022).</p> <p>"T0Simulation_11082020_output" contains data about the status quo simulation of the area of interest (500 m x 500 m x 60 m), whereas "T1Simulation_11082020_output" shows the results of the Green Infrastructure scenario described in the research article above. Please ensure enough memory space on your device, as both files have a size of approximately 25 GB (unzipped).</p> <p>The output files can be visualized with the ENVI-met Leonardo extension. The ENVI-met LITE-version is freely available and can be downloaded at the <a href="https://envi-met.info/doku.php?id=files:download">ENVI-met homepage</a>. Alternatively, the included .NETCDF files can be imported as a multidimensional raster dataset in ArcGIS Pro.</p> <p>Files in the folder "atmosphere" represent meteorological parameters such as potential air temperature [°C], relative humidity [%], or wind speed [m/s]. Air pollution calculations like particulate matter concentrations [µg/m³] can be found in the folder "pollutants". The folder "buildings" contains building data for 3D visualizations of surface temperatures [°C].</p>
Data used in a manuscript entitled "Large ensemble simulation for investigating predictability of precursor vortices of Typhoon Faxai in 2019 with a 14-km mesh global nonhydrostatic atmospheric model" submitted to Geophysical Research Letters
<p>This include a dataset used in a manuscript entitled “Large ensemble simulation for investigating predictability of precursor vortices of Typhoon Faxai in 2019 with a 14-km mesh global nonhydrostatic atmospheric model” by Yamada and co-authors, which is submitted to Geophysical Research Letters.</p> <p>Contact: Yohei Yamada (yoheiy@jamstec.go.jp)</p>
Data from: Integrating genomic data and simulations to evaluate alternative species distribution models and improve predictions of glacial refugia and future responses to climate change
<p>Climate change poses a threat to biodiversity, and it is unclear whether species can adapt to or tolerate new conditions, or migrate to areas with suitable habitats. Reconstructions of range shifts that occurred in response to environmental changes since the last glacial maximum from species distribution models (SDMs) can provide useful data to inform conservation efforts. However, different SDM algorithms and climate reconstructions often produce contrasting patterns, and validation methods typically focus on accuracy in recreating current distributions, limiting their relevance for assessing predictions to the past or future. We modeled historically suitable habitat for the threatened North American tree green ash (<em>Fraxinus pennsylvanica</em>) using 24 SDMs built using two climate models, three calibration regions, and four modeling algorithms. We evaluated the SDMs using contemporary data with spatial block cross-validation and compared the relative support for alternative models using a novel integrative method based on coupled demographic-genetic simulations. We simulated genomic datasets using habitat suitability of each of the 24 SDMs in a spatially-explicit model. Approximate Bayesian Computation (ABC) was then used to evaluate the support for alternative SDMs through comparisons to an empirical population genomic dataset. Models had very similar performance when assessed with contemporary occurrences using spatial cross-validation, but ABC model selection analyses consistently supported SDMs based on the CCSM climate model, an intermediate calibration extent, and the generalized linear modeling algorithm. Finally, we projected the future range of green ash under four climate change scenarios. Future projections using the SDMs selected via ABC suggest only minor shifts in suitable habitat for this species, while some of those that were rejected predicted dramatic changes. Our results highlight the different inferences that may result from the application of alternative distribution modeling algorithms and provide a novel approach for selecting among a set of competing SDMs with independent data.</p>
Predictive simulations of ionization energies of solvated halide ions with relativistic embedded Equation of Motion Coupled-Cluster Theory: Dataset
<p>This dataset collects the unprocessed (= outputs from calculations) and processed (= plots, average values for ionization energies) results discussed in the paper titled "Predictive simulations of ionization energies of solvated halide ions with relativistic embedded Equation of Motion Coupled-Cluster Theory", by Yassine Bouchafra, Avijit Shee, Florent Réal, Valérie Vallet and André Severo Pereira Gomes.</p> <p>In each archive file there is a README explaining how to use the bundled scripts to process the data.</p>
In silico design, docking simulation, and ANN-QSAR model for predicting the anticoagulant activity of thiourea isosteviol compounds as FXa inhibitors
<p>The present work combined molecular modeling and docking approach for searching and designing novel thiourea isosteviol-based compounds as potential FXa inhibitors. Elaborated regression model establishes the relationships between experimentally determined anticoagulant activity and molecular descriptors and enables the prediction of FXa inhibitory activity for novel compounds. The obtained results proved that the Artificial Neural Network algorithm facilitates the search for the most promising isosteviol derivatives incorporating thiourea fragments as FXa inhibitors. Moreover, docking simulation confirms the prominent binding of the newly in silico designed molecules with the active sites of the protein, which may be the lead molecules and can be further optimized for the efficient pharmacodynamic and pharmacokinetic profiles. The enclosed files are representations of molecular structures of thiourea isosteviol compounds with experimentally tested FXa inhibitory activity (i20-i39) geometrically optimized in hyperchem, newly in silico designed thiourea isosteviol compounds geometrically optimized in hyperchem (e1-e11), one file contains molecular descriptors for optimized structures calculated in Dragon and there is also a code for ANN QSAR model for predicting activity of novel thiourea isosteviol compounds. </p>
Focal-TSMP: Deep learning for vegetation health prediction and agricultural drought assessment from a regional climate simulation
<p>This is the preprocessed remote sensing dataset used in the paper<strong> "Focal-TSMP: Deep learning for vegetation health prediction and agricultural drought assessment from a regional climate simulation"</strong>. It contains the preprocessed NOAA data along with the additional files necessary for the TSMP simulation.</p>
Predicting the distribution of serotonergic axons: A supercomputing simulation of reflected fractional Brownian motion in a 3D-mouse brain model
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