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1,773 results for “Predictive model”
Development and validation of metabolic models for predicting survival and immune status of hepatocellular carcinoma patients
<p>Supplementary materials for the article titled “Development and validation of metabolic models for predicting survival and immune status of hepatocellular carcinoma patients”</p>
Freebase Datasets for Robust Evaluation of Knowledge Graph Link Prediction Models
<p><strong>Freebase</strong> is amongst the largest public cross-domain knowledge graphs. It possesses three main data modeling idiosyncrasies. It has a strong <strong>type system</strong>; its properties are purposefully represented in <strong>reverse pairs</strong>; and it uses <strong>mediator objects</strong> to represent multiary relationships. These design choices are important in modeling the real-world. But they also pose nontrivial challenges in research of embedding models for knowledge graph completion, especially when models are developed and evaluated agnostically of these idiosyncrasies. We make available several variants of the Freebase dataset by inclusion and exclusion of these data modeling idiosyncrasies. This is the first-ever publicly available <strong>full-scale</strong> Freebase dataset that has gone through <strong>proper preparation</strong>. </p><p> </p><p>Dataset Details</p><p>The dataset consists of the four variants of Freebase dataset as well as related mapping/support files. For each variant, we made three kinds of files available:</p><ul><li>Subject matter triples file<ul><li><i>fb+/-CVT+/-REV</i> One folder for each variant. In each folder there are 5 files: train.txt, valid.txt, test.txt, entity2id.txt, relation2id.txt Subject matter triples are the triples belong to subject matters domains—domains describing real-world facts.<ul><li>Example of a row in train.txt, valid.txt, and test.txt: <ul><li>2, 192, 0</li></ul></li><li>Example of a row in entity2id.txt:<ul><li>/g/112yfy2xr, 2</li></ul></li><li>Example of a row in relation2id.txt:<ul><li>/music/album/release_type, 192</li></ul></li><li>Explaination<ul><li>"/g/112yfy2xr" and "/m/02lx2r" are the MID of the subject entity and object entity, respectively. "/music/album/release_type" is the realtionship between the two entities. 2, 192, and 0 are the IDs assigned by the authors to the objects.</li></ul></li></ul></li></ul></li><li>Type system file<ul><li><i>freebase_endtypes</i>: Each row maps an edge type to its required subject type and object type.<ul><li>Example<ul><li>92, 47178872, 90</li></ul></li><li>Explanation<ul><li>"92" and "90" are the type id of the subject and object which has the relationship id "47178872".</li></ul></li></ul></li></ul></li><li>Metadata files<ul><li><i>object_types</i>: Each row maps the MID of a Freebase object to a type it belongs to.<ul><li>Example<ul><li>/g/11b41c22g, /type/object/type, /people/person</li></ul></li><li>Explanation<ul><li>The entity with MID "/g/11b41c22g" has a type "/people/person"</li></ul></li></ul></li><li><i>object_names</i>: Each row maps the MID of a Freebase object to its textual label.<ul><li>Example<ul><li>/g/11b78qtr5m, /type/object/name, "Viroliano Tries Jazz"@en</li></ul></li><li>Explanation<ul><li>The entity with MID "/g/11b78qtr5m" has name "Viroliano Tries Jazz" in English.</li></ul></li></ul></li><li><i>object_ids</i>: Each row maps the MID of a Freebase object to its user-friendly identifier.<ul><li>Example<ul><li>/m/05v3y9r, /type/object/id, "/music/live_album/concert"</li></ul></li><li>Explanation<ul><li>The entity with MID "/m/05v3y9r" can be interpreted by human as a music concert live album.</li></ul></li></ul></li><li><i>domains_id_label</i>: Each row maps the MID of a Freebase domain to its label.<ul><li>Example<ul><li>/m/05v4pmy, geology, 77</li></ul></li><li>Explanation<ul><li>The object with MID "/m/05v4pmy" in Freebase is the domain "geology", and has id "77" in our dataset.</li></ul></li></ul></li><li><i>types_id_label</i>: Each row maps the MID of a Freebase type to its label.<ul><li>Example<ul><li>/m/01xljxh, /government/political_party, 147</li></ul></li><li>Explanation<ul><li>The object with MID "/m/01xljxh" in Freebase is the type "/government/political_party", and has id "147" in our dataset.</li></ul></li></ul></li><li><i>entities_id_label</i>: Each row maps the MID of a Freebase entity to its label.<ul><li>Example<ul><li>/g/11b78qtr5m, Viroliano Tries Jazz, 2234</li></ul></li><li>Explanation<ul><li>The entity with MID "/g/11b78qtr5m" in Freebase is "Viroliano Tries Jazz", and has id "2234" in our dataset.</li></ul></li><li><i>properties_id_label</i>: Each row maps the MID of a Freebase property to its label.<ul><li>Example<ul><li>/m/010h8tp2, /comedy/comedy_group/members, 47178867</li></ul></li><li>Explanation<ul><li>The object with MID "/m/010h8tp2" in Freebase is a property(relation/edge), it has label "/comedy/comedy_group/members" and has id "47178867" in our dataset.</li></ul></li></ul></li><li><i>uri_original2simplified</i> and <i>uri_simplified2original</i>: The mapping between original URI and simplified URI and the mapping between simplified URI and original URI repectively.<ul><li>Example<ul><li><i>uri_original2simplified</i><ul><li>"<a href="http://rdf.freebase.com/ns/type.property.unique">http://rdf.freebase.com/ns/type.property.unique</a>": "/type/property/unique"</li></ul></li><li><i>uri_simplified2original</i><ul><li>"/type/property/unique": "<a href="http://rdf.freebase.com/ns/type.property.unique">http://rdf.freebase.com/ns/type.property.unique</a>"</li></ul></li></ul></li><li>Explanation<ul><li>The URI "<a href="http://rdf.freebase.com/ns/type.property.unique">http://rdf.freebase.com/ns/type.property.unique</a>" in the original Freebase RDF dataset is simplified into "/type/property/unique" in our dataset.</li><li>The identifier "/type/property/unique" in our dataset has URI <a href="http://rdf.freebase.com/ns/type.property.unique">http://rdf.freebase.com/ns/type.property.unique</a> in the original Freebase RDF dataset.</li></ul></li></ul></li></ul></li></ul></li></ul>
Development of Distress Index Prediction Models for Rehabilitation Treatments in Louisiana Using Advanced Machine Learning Techniques
<p>Performance prediction models are used by state agencies to predict future trends in distress indices, hence, determining the required maintenance and/or rehabilitation treatment as well as the deterioration rate and remaining pavement service life. However, most of these models are based on a limited number of parameters and cannot predict the performance distress indices reliably. Such limitation resulted in having, most of the time, a maximum prediction period of five years. As a solution and coping with the ever-increasing size of pavement data, machine learning techniques have become a promising alternative. The objective of this study was to develop a machine-learning-based framework for states with a hot and humid climate that can predict the long-term field performance (for 11 years) of their asphalt (AC) overlays based on their key project conditions. Two machine learning algorithms were examined, namely Random Forest (RF) and CatBoost, and the one yielding a higher accuracy was considered. In this study, the well-known pavement condition index (PCI) was used as the pavement performance indicator. A total of 892 log miles of AC overlay data were obtained from the Louisiana Department of Transportation and Development (LaDOTD) Pavement Management System (PMS) database. Based on the collected data, six models were trained (for each algorithm) and validated to predict the future PCI of AC overlays for up to 11 years. Results indicated that the RF algorithm yielded higher accuracy than the CatBoost Algorithm and thus the RF-based models were considered in the proposed decision-making framework.</p>
Development of a Predictive Model for In-Hospital Mortality in COVID-19 Patients Using CAR, IL-6, IL-6/LY, and NLR: A Single-Center Study in Indonesia
<p>Figure 1. ROC Curve of CAR, IL-6, IL-6/LY, and NLR</p> <p> </p> <p>Figure 2. Kaplan Meier curve of (a) CAR (b) IL-6 (c) IL-6/LY (d) NLR blue line represents group above cut off and green one represents group below cut-off</p> <p> </p>
A structured evaluation of regression models for predicting CO2 concentration from plasma emission spectra, dataset
<p>Dataset for publication: <a href="https://doi.org/10.1016/j.sab.2022.106467">https://doi.org/10.1016/j.sab.2022.106467</a>.</p> <p>The recorded spectra are stored as comma separated values, the set includes a meta data-file (.mat-file), and a column descriptions (columns.pdf).</p>
Predicting Mathematics Anxiety and Achievement: Unveiling the Significance of Student and Teacher Attributes through Hierarchical Linear Modeling
<p>This study aimed to determine the predictive power of student and teacher characteristics on students' math anxiety and achievement.</p>
Data sets and machine learning models for: Predicting critical properties and acentric factor of fluids using multi-task machine learning
<p>The experimental data sets, data splits, additional features, QM calculations, model predictions, and final machine learning models for the manuscript "Predicting Critical Properties and Acentric Factor of Fluids Using Multi-Task Machine Learning". <strong>Citation should refer directly to the manuscript:</strong></p> <ul> <li> <p>Biswas, S.; Chung, Y.; Ramirez, J.; Wu, H.; Green, W. H. Predicting Critical Properties and Acentric Factors of Fluids Using Multitask Machine Learning. <em>Journal of Chemical Information and Modeling.</em> <strong>2023</strong> <em>63</em> (15), 4574-4588. DOI: <a href="https://doi.org/10.1021/acs.jcim.3c00546">10.1021/acs.jcim.3c00546</a></p> </li> </ul> <p>To use the machine learning models, please refer to the sample files and instructions on <a href="https://github.com/yunsiechung/chemprop/tree/crit_prop">https://github.com/yunsiechung/chemprop/tree/crit_prop</a>. </p> <p>Detailed information can be found in README.md file.</p> <p> </p> <p><strong>Details on the properties considered</strong></p> <p>The data set includes the following 8 properties:</p> <ul> <li>Tc: critical temperature, in K</li> <li>Pc: critical pressure, in bar</li> <li>rhoc: critical density, in mol/L</li> <li>omega: acentric factor, unitless</li> <li>Tb: boiling point, in K</li> <li>Tm: melting point, in K</li> <li>dHvap: enthalpy of vaporization at boiling point, in kJ/mol</li> <li>dHfus: enthalpy of fusion at melting point, in kJ/mol</li> </ul> <p><strong>Details on the files</strong></p> <p>1. Data sets under CritProp_v1.1.0:</p> <ul> <li>all_data: includes the data sets used in this work. All data points are listed for each chemical compound as well as its corresponding data source. The details of the data sources can be found in the README.md file. The distribution of the data set is included in each folder. <ul> <li>estimated_data_for_pretraining: contains the estimated data from Yaws' handbook that are used to pre-train our machine learning (ML) model.</li> <li>experimental_data: contains the experimental data (references 1 - 15) used to fine-tune our final ML model.</li> </ul> </li> <li>additional_features: includes the additional features tested for the ML model. The Abraham features are generated for all data (references 1 - 15) while the acsf, qm, and rdkit features are only generated for the data from references 1 - 9. <ul> <li>abraham: Abraham solute parameters (E, S, A, B, L). Molecular features.</li> <li>acsf: ACSF (atom-centered symmetry functions). Atomic features that are coverted from the 3D coordinates of the compound</li> <li>qm_atom: QM (quantum chemical) atomic feature. </li> <li>qm_mol: QM molecular feature.</li> <li>rdkit: Selected RDKit 2D molecular features.</li> </ul> </li> <li>data_splits_and_model_predictions: contains the training and test sets used to evaluate the model. It also contains the predicted values from our final ML model for each test set. <ul> <li>random and scaffold splits: training and test sets that include the data from references 1 - 9.</li> <li>external test set: a test set that includes the data from only references 10 - 15.</li> </ul> </li> </ul> <p>2. Machine learning (ML) model files:</p> <ul> <li>CritProp_ML_model_files_with_abraham_feat.zip: contains the Chemprop ML model files that are trained using Abraham features as additional molecular features. This gives the best results.</li> <li>CritProp_ML_model_files_without_additional_feat.zip: contains the Chemprop ML model files that are trained without any additional features. This gives the second best results.</li> </ul> <p>To use these ML models, please refer to the sample files and instructions on <a href="https://github.com/yunsiechung/chemprop/tree/crit_prop">https://github.com/yunsiechung/chemprop/tree/crit_prop</a></p> <p>3. QM (quantum chemical) calculations:</p> <ul> <li>QM_calculations.zip: contains the results of the QM calculations that are performed to compute QM features.</li> </ul> <p> </p> <p> </p>
Monitoring of postpartum body condition at the cow and herd levels: assessing explanatory and predictive power of disease risk models
<p>Objectives</p> <p>1- To define the herd threshold for cows with poor body condition based on its predictive capacity for disease risk at the herd level, and</p> <p>2- to estimate the impact measures on disease rates due to body condition indicators in transition period.</p> <p>Two commercial grazing dairy herds (Herd A=5.034 and herd B=7.965 lactations) from Argentinean Pampa region were used to perform a longitudinal retrospective study during a 4-year period (2014 –2017).Health, reproductive and body condition score (BCS) records were gathered. The BCS (5-point scale) was performed at calving and at the time of reproductive release. The difference between both measures of BCS was used to assess the body condition loss (∆BCS). All the cows not bred by 70 DIM were checked for anestrus.Calving cohorts of 21-day were defined at each herd and parity group through the entire study period. The frequency of cows with BCS<3 or ∆BC>-0.5 at each cohort were calculated and used to define quartiles through whole study period. Quartiles were used, one at a time, as threshold to dichotomize the cohorts to predict the risk that a cohort has a frequency of anestrus over the median.The higher AUC was used as selection criterium to determine the herd level threshold at each HERD and PARITY level. The population attributable fraction (AFP) of anestrus rate to body condition indicators at each cohort was calculated, for every HERD and PARITY level. </p>
PIGNet2: A versatile deep learning-based protein-ligand interaction prediction model for accurate binding affinity scoring and virtual screening
<p>Training and test datasets of the paper "Improving the versatility of deep learning-based protein-ligand interaction prediction for accurate binding affinity scoring and virtual screening".</p>
Data and codes: Speech-recognition in landlide predictive modelling
<p>This is the data and codes for the manuscript "Speech-recognition in landlide predictive modelling"</p>
Geospatial based model for malaria risk prediction in Kilombero Valley, south-eastern Tanzania
<div> <p><strong>Background</strong>: Malaria continues to pose a major public health challenge in tropical regions. Despite significant efforts to control malaria in Tanzania, there are still residual transmission cases. Unfortunately, little is known about where these residual malaria transmission cases occur and how they spread. In Tanzania, for example, the transmission is heterogeneously distributed. In order to effectively control and prevent the spread of malaria, it is essential to understand the spatial distribution and transmission patterns of the disease. This study seeks to predict areas that are at high risk of malaria transmission so that intervention measures can be developed to accelerate malaria elimination efforts.</p> </div> <p><strong>Methods</strong>: This study employs a geospatial-based model to predict and map out malaria risk area in Kilombero Valley. Environmental factors related to malaria transmission were considered and assigned valuable weights in the Analytic Hierarchy Process (AHP), an online system using a pairwise comparison technique. The malaria hazard map was generated by a weighted overlay of the altitude, slope, curvature, aspect, rainfall distribution, and distance to streams in Geographic Information Systems (GIS). Finally, the risk map was created by overlaying components of malaria risk including hazards, elements at risk, and vulnerability.</p> <p><strong>Results</strong>: The study demonstrates that the majority of the study area falls under the moderate-risk level (61%), followed by the low-risk level (31%), while the high-malaria risk area covers a small area, which occupies only 8% of the total area.</p> <p><strong>Conclusion</strong>: The findings of this study are crucial for developing spatially targeted interventions against malaria transmission in residual transmission settings. Predicted areas prone to malaria risk provide information that will inform decision-makers and policymakers for proper planning, monitoring, and deployment of interventions.</p>
Alphafold predicted structures of VPS13 proteins from model organisms
<p>This upload contains AlphaFold-predicted structures of VPS13 proteins from a variety of organisms. Given the large size of these proteins, only partial sequences were predicted with AlphaFold(1) and the resulting structures were aligned in PyMOL(2). A summary of the structures uploaded here is presented as a collection of domain cartoons in the "VPS13 domain organization across eukaryotic evolution.pdf" file. </p> <p>The structures were generated with AlphaFold v2.029 on the Yale High Performance Cluster. Each *.zip file contains the best ranked predictions (out of five) for each sequence (*.pdb files) and the PyMOL assembled full structure (*.pse file). In a few cases, where a good alignment was not possible due to long disordered regions in the C-terminal portions (mostly in proteins from <em>D. discoideum</em> and <em>A. thaliana</em>), the full structures were aligned manually in PyMOL based on the continuity of the lipid transfer groove. The structures in PyMOL can be colour-coded by the confidence value of AlphaFold predictions using the following prompt:</p> <p>set_color n0, [0.051, 0.341, 0.827]<br> set_color n1, [0.416, 0.796, 0.945]<br> set_color n2, [0.996, 0.851, 0.212]<br> set_color n3, [0.992, 0.490, 0.302]<br> color n0, b < 100; color n1, b < 90<br> color n2, b < 70; color n3, b < 50</p> <p>Considering that full length structures were assembled by aligning different protein fragments and in view of the presence of flexible loops with low prediction confidence scores, the relative positions of different folded domains are not necessarily correct.</p> <p> </p> <p><strong>References</strong></p> <p>1. J. Jumper, <em>et al.</em>, Highly accurate protein structure prediction with AlphaFold. <em>Nature</em> 596, 583–589 (2021).</p> <p>2. The PyMOL Molecular Graphics System, Version 2.0. Schrödinger LLC.</p>
A multi-model ensemble of baseline and process-based models improves the predictive skill of near-term lake forecasts: data, forecasts, and scores
<p>This data publication contains zipped parquet from the Falling Creek Reservoir multi-model ensemble (MME) forecasting work using the FLARE (Forecasting Lake And Reservoir Ecosystems) system and baseline models: drivers.zip contains NOAA driver forecast files, targets.zip contains in-situ water temperature observations, forecasts.zip contains forecast parquet files generated from the MME workflow (FLARE & baseline models), and scores.zip contains forecast skill metrics required for analysis.</p>
Deep model predictive control of gene expression in thousands of single cells
<p>Experimental data, training datasets, and trained models for our study on deep model predictive control of gene expression in bacteria. This data can be used to reproduce our results and figures.</p> <p>See our preprint here: <a href="https://www.biorxiv.org/content/10.1101/2022.10.28.514305">biorxiv.org/content/10.1101/2022.10.28.514305</a></p> <p>And our code repository here: <a href="https://gitlab.com/dunloplab/deepcellcontrol">gitlab.com/dunloplab/deepcellcontrol</a></p> <p><strong>Contents:</strong></p> <ul> <li><em>datasets.zip</em>: Formatted experimental data used to train and validate fluorescence forecasting models.</li> <li><em>experiments.zip:</em> Processed data for all control experiments.</li> <li><em>misc.zip</em>: Files necessary to reproduce some figures or to exactly reproduce some of our results.</li> <li><em>models.zip</em>: Trained neural network models and associated files.</li> </ul>
Is my model fit for purpose? Validating a population model for predicting freshwater fish responses to flow management
<p>Models based on ecological processes ("process-explicit models") are often used to predict ecosystem responses to environmental changes or management scenarios. However, models are imperfect and need to be validated, ideally by testing their assumptions and outputs against independent empirical data sets. Examples of validation of process-explicit models are rare. Recently, stochastic population models have been developed to predict the likely responses (over 10-120 years) of a riverine fish (golden perch, Macquaria ambigua) to flow management in the Murray-Darling Basin (MDB) in eastern Australia, one of the world's most regulated river basins. Declines of golden perch (and other species) are a direct consequence of altered hydrology, and managers require information to predict how fish will respond to possible future hydrological conditions to guide the substantial investments in flow management. Here, we use two independent field data sets to validate our population model. We compared model predictions to observed trends to ask: (1) how do predicted population sizes and growth rates compare to observed data? (2) does the correlation between predicted and observed population sizes and growth rates vary among populations? (3) does the correlation between predicted and observed population sizes and growth rates vary across observed hydrological conditions? and (4) how do modelled and observed fish movement rates compare? We found reasonable correlations between fish population sizes and growth rates as predicted by the model and observed in independent data sets for several populations (Aim 1) but the strength of these correlations varied among populations (Aim 2) and hydrological conditions (Aim 3). Predicted and observed fish movement rates were strongly correlated (Aim 4). Population models are frequently used in conservation decision-making but are rarely validated. We demonstrate that: (1) validation can identify model strengths and weaknesses; (2) observed data sets often have inherent limitations that can preclude robust validations; (3) validation is likely be more common if appropriate observed data sets are available; and (4) validation should consider the purpose of modelling. Wider consideration of these messages would contribute to more critical examinations of models so they can be most appropriately used in conservation decision-making.</p>
Data from: The contributions of flower strips to wild bee conservation in agricultural landscapes can be predicted using pollinator habitat suitability models
<p>Sowing flower strips along field edges is a widely adopted method for conserving pollinating insects in agricultural landscapes. To maximize the effect of flower strips given limited resources, we need spatially explicit tools that can prioritize their placement, and for identifying plant species to include in seed mixtures.</p> <p>We sampled bees and plant species as well as their interactions in a semi-controlled field experiment with roadside/field edge pairs with/without a sown flower strip at 31 sites in Norway and used a regional spatial model of solitary bee species richness to test if the effect of flower strips on bee species richness was predictable from the modelled solitary bee species richness.</p> <p>We found that sites with flower strips were more bee species rich compared to sites without flower strips and that this effect was greatest in areas that the regional solitary bee species richness model had identified to be particularly important for bees. Spatial models revealed that even within small landscapes there were pronounced differences between field edges in the predicted effect of sowing flower strips.</p> <p>Of the plant species that attracted the most bee species, the majority mainly attracted bumblebees and only few species also attracted solitary bees. Considering both the taxonomic diversity of bees and the species richness of bees attracted by plants we suggest that seed mixes containing <em>Hieracium </em>spp. such as <em>Hieracium umbellatum </em>and <em>Pilosella officinarum</em>; <em>Taraxacum</em> spp; <em>Trifolium repens</em>;<em> Lotus corniculatus</em>; S<em>tellaria graminea</em>; and <em>Achillea millefolium</em> would provide resources for diverse bee communities in our region.</p> <p>Spatial prediction models of bee diversity can be used to identify locations where flower strips are likely to have the largest effect and can thereby provide managers with an important tool for prioritizing how funding for agri-environmental schemes such as flower strips should be allocated. Such flower strips should contain plant species that are attractive to both solitary and bumblebees, and do not need to be particularly plant species rich as long as the selected plants complement each other.</p>
Models and Data associated with: Single-cell gene expression prediction from DNA sequence at large contexts
<p>This archive holds trained models and associated data for the <a href="https://www.biorxiv.org/content/10.1101/2023.07.26.550634v1">manuscript</a>:<br> "Single-cell gene expression prediction from DNA sequence at large contexts"</p> <p>Structure:</p> <ul> <li>configs - example configs for the workflows to produce publication data </li> <li>data_* - pre-processed single cell data used for publication</li> <li>models_* - model checkpoints, hyperparameters and training progress in tensorboard logs</li> <li>preprocessing - additional data required to reproduce the pre-processing workflow</li> </ul> <p> </p> <p>"Copyright 2023 GlaxoSmithKline Research & Development Limited. All rights reserved."</p>
Ensemble BLUP, Machine Learning, and Deep Learning Models Predict Maize Yield Better Than Each Model Alone.
<p>Data and scripts exploring ensembling strategies using the models developed in <a href="https://academic.oup.com/g3journal/advance-article/doi/10.1093/g3journal/jkad006/6982634">Kick et al., 2023</a> (see also <a href="https://zenodo.org/record/7401113">1</a>, <a href="https://zenodo.org/record/6916775">2</a>). Download all files to a single directory then run setup.sh or manually unzip using tar.</p> <p> </p> <table> <tbody> <tr> <td><strong>Filename</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>setup.sh</td> <td>Simple script that unzips zipped directories</td> </tr> <tr> <td>ext_data</td> <td>Reduced data from Kick et al. 2023</td> </tr> <tr> <td>ext_data_notebooks</td> <td>Contains python notebooks containing analysis and R markdown file containing visualization of results. Python and R data objects are written to allow results to be read in instead of re-generated.</td> </tr> <tr> <td>output</td> <td>Folder containing a placeholder file.</td> </tr> </tbody> </table> <p> </p> <p>This research used resources provided by the United States Department of Agriculture’s Agricultural Research Service (project number 5070-21000-041-000-D). The SCINet project of the USDA Agricultural Research Service (project number 0500-00093-001-00-D) was instrumental in the training of the models used in this work. In addition, we would like to acknowledge those presently and historically involved in generating data for the Genomes to Fields Initiative.</p> <p> </p> <p> </p> <p> </p>
Data Set for Enhanced Performance Prediction of ATL Model Transformations
<p>Model transformation languages are domain-specific languages, which are designed to comfortably define transformations. With the increasing use of transformations in various domains, the complexity and size of input models are also increasing. However, developers often lack suitable models for performance testing. We have therefore conducted experiments in which we predict the performance of model transformations based on characteristics of input models using machine learning approaches. In particular, we focused on how to predict the performance of transformations that also transform attributes whose values can have arbitrary size. This dataset contains our raw and processed input data, the scripts necessary to repeat our experiments, and the results we obtained.</p> <p>Our input data consists of the time measurements for six different transformations defined in the Atlas Transformation Language (ATL), as well as the collected characteristics of the real-world input models we used. In this data set, we provide the script that implements our experiments. We predict the execution time of ATL transformations using the machine learning approaches linear regression, random forests and support vector regression using a radial basis function kernel. We also investigate different sets of characteristics of input models as input for the machine learning approaches. These are described in detail in the provided documentation.pdf. The results of the experiments are provided as raw data in individual cvs files. Furthermore, we provide our Eclipse plugin, which collects the characteristics for a set of given models.</p> <p>A detailed documentation is available in documentaion.pdf.</p>
Data from Genome scale metabolic network modelling for metabolic profile predictions
<p>Data used to produce figures 4, 5 and 6 in the paper Genome scale metabolic network modelling for metabolic profile predictions.</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.