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356
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ShareScore release 0.9.0
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356 results for “Machine learning models”
Data from: Machine learning biogeographic processes from biotic patterns: a new trait-dependent dispersal and diversification model with model choice by simulation-trained discriminant analysis
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Data from: Machine learning methods for locating re-entrant drivers from electrograms in a model of atrial fibrillation
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Data from: Prediction model for aerodynamic coefficients of iced quad bundle conductors based on machine learning method
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Data from: Development of machine learning models for diagnosis of glaucoma
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Model-to-crop conserved NUE Regulons enhance machine learning predictions of nitrogen use efficiency
GEO Series GSE280344. Zea mays. 57 samples. Type: Expression profiling by high throughput sequencing.
Machine-learning model based on glycosyltransferase expression predicts multiple cancer types, subtypes and survival probability
GEO Series GSE254461. Homo sapiens. 57 samples. Type: Expression profiling by high throughput sequencing.
A Multi-Omics Interpretable Machine Learning Model Reveals Modes of Action of Small Molecules
GEO Series GSE129144. Mus musculus. 54 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
Model-to-crop conserved NUE Regulons enhance machine learning predictions of nitrogen use efficiency
GEO Series GSE280343. Zea mays. 2 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Global transcriptional profiles of hiPSC-derived hypothalamus-pituitary organoids predicted to fail or succeed by machine learning model
GEO Series GSE301692. Homo sapiens. 16 samples. Type: Expression profiling by high throughput sequencing.
Exon Level Machine Learning Analyses Elucidate Novel Candidate miRNA Targets in an Avian Model of Fetal Alcohol Spectrum Disorder
GEO Series GSE115383. Gallus gallus. 4 samples. Type: Expression profiling by high throughput sequencing.
A Multi-Omics Interpretable Machine Learning Model Reveals Modes of Action of Small Molecules (RNA-Seq)
GEO Series GSE129143. Mus musculus. 48 samples. Type: Expression profiling by high throughput sequencing.
Modeling CRISPR-Cas13d on-target and off-target effects using machine learning approaches
GEO Series GSE183256. Homo sapiens. 8 samples. Type: Other.
Model-to-crop conserved NUE Regulons enhance machine learning predictions of nitrogen use efficiency
GEO Series GSE280346. Zea mays. 229 samples. Type: Expression profiling by high throughput sequencing.
Model-to-crop conserved NUE Regulons enhance machine learning predictions of nitrogen use efficiency
GEO Series GSE280342. Arabidopsis thaliana. 8 samples. Type: Expression profiling by high throughput sequencing.
A Multi-Omics Interpretable Machine Learning Model Reveals Modes of Action of Small Molecules (ChIP-Seq)
GEO Series GSE129141. Mus musculus. 6 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Establishment of interpretable cytotoxicity prediction models using machine learning analysis of transcriptome features
GEO Series GSE252529. Homo sapiens. 15 samples. Type: Expression profiling by high throughput sequencing.
Long-term trends of ambient nitrate (NO3-) concentrations across China based on ensemble machine-learning models
<p>The data is the monthly NO3- concentrations across China during 2005-2015. These data was obtained using a novel ensemble model combining random forest (RF), gradient boosting decision tree (GBDT), and extreme gradient boosting (XGBoost) algorithms based on satellite data, assimilated meteorology, and other geographical covariates.</p> <p>In the datasets, XX-YY denote the XX month in YY year.<br> For instance, January-05 denotes the January in 2005.<br> NaN in the data denote the missing values.</p>
Development, evaluation, and validation of machine learning models for COVID-19 detection based on routine blood tests
<p>The .xlsx dataset includes all patients used for training, internal-external and external validation: these can be distinguished by looking at the ID (first column) in the dataset: those in format Axxxx-<Date> are the data used for the training, those in the format 20xx are the data used for the internal-external validation, while the remaining data were used for external validation.</p> <p>As regards the features: for the Target feature the value 1 stands for "Positive to COVID-19" while the value 0 stands for "Negative to COVID-19"; while for the Sex feature the value 1 stands for "Male" while the value 0 stands for "Female".</p> <p>The full article is available at: https://www.degruyter.com/view/journals/cclm/ahead-of-print/article-10.1515-cclm-2020-1294/article-10.1515-cclm-2020-1294.xml.</p> <p>A pre-print version of the article is also available on MedrXiv: https://www.medrxiv.org/content/10.1101/2020.10.02.20205070v1</p> <p><strong>ABSTRACT</strong></p> <p><strong>Background</strong> The rRT-PCR test, the current gold standard for the detection of coronavirus disease (COVID-19), presents with known shortcomings, such as long turnaround time, potential shortage of reagents, false-negative rates around 15–20%, and expensive equipment. The hematochemical values of routine blood exams could represent a faster and less expensive alternative. </p> <p><strong>Methods</strong> Three different training data set of hematochemical values from 1,624 patients (52% COVID-19 positive), admitted at San Raphael Hospital (OSR) from February to May 2020, were used for developing machine learning (ML) models: the complete OSR dataset (72 features: complete blood count (CBC), biochemical, coagulation, hemogasanalysis and CO-Oxymetry values, age, sex and specific symptoms at triage) and two sub datasets (COVID-specific and CBC dataset, 32 and 21 features respectively). 58 cases (50% COVID-19 positive) from another hospital, and 54 negative patients collected in 2018 at OSR, were used for internal-external and external validation.</p> <p><strong>Results</strong> We developed five ML models: for the complete OSR dataset, the area under the receiver operating characteristic curve (AUC) for the algorithms ranged from 0.83 to 0.90; for the COVID-specific dataset from 0.83 15 to 0.87; and for the CBC dataset from 0.74 to 0.86. The validations also achieved good results: respectively, AUC 16 from 0.75 to 0.78; and specificity from 0.92 to 0.96. </p> <p><strong>Conclusions</strong> ML can be applied to blood tests as both an adjunct and alternative method to rRT-PCR for the fast and cost-effective identification of COVID-19-positive patients. This is especially useful in developing countries, or in countries facing an increase in contagions.</p>
The best performing landslide susceptibility maps using ensemble machine learning models and precipitation data on basin and regional level in Lombardy, Italy
<p>A selection of landslide susceptibility maps computed through ensemble machine learning models with included precipitation data for the basin of Valchiavenna, and the Lombardy region in Italy.</p> <p>A list of the used base machine learning methods:</p> <ul> <li>Neural Networks.</li> </ul> <p>A list of the precipitation data included in the models:</p> <ul> <li>Average hourly precipitation for the year of 2020,</li> <li>90<sup>th</sup> percentile for the hourly precipitation for the year of 2020 ,</li> <li>Averaged + 90<sup>th</sup> percentile for the hourly precipitation for the year of 2020.</li> </ul> <p>A full list of the model combinations can be found in the "Case Studies" document.</p> <p>The maps are in WGS 84/ UTM zone 32N (EPSG:32632).</p> <p>The map production process details are discussed in Xu et al. 2024. If you use the dataset, please, cite also the paper:</p> <p><em>Qiongjie Xu, Vasil Yordanov, Lorenzo Amici & Maria Antonia Brovelli (2024) Landslide susceptibility mapping using ensemble machine learning methods: a case</em><br><em>study in Lombardy, Northern Italy, International Journal of Digital Earth, 17:1, 2346263, DOI:10.1080/17538947.2024.2346263</em></p> <p>The maps are produced as part of the "Geoinformatics and Earth Observation for Landslide Monitoring" Italy-Vietnam.</p> <p>The work is partially funded by the Italian Ministry of Foreign Affairs and International Cooperation within the project “Geoinformatics and Earth Observation for Landslide Monitoring” CUP D19C21000480001.</p> <p> </p>
Landslide susceptibility maps using base machine learning models on basin and regional level in Lombardy, Italy
<p>A selection of landslide susceptibility maps computed through base machine learning models for the basins of Val Tartano, Upper Valtellina and Valchiavenna, and on a regional level for the Lombardy region in Italy.</p> <p>A list of the used machine learning methods:</p> <ul> <li>Bagging,</li> <li>Random Forest,</li> <li>AdaBoost,</li> <li>Gradient Tree Boosting,</li> <li>Neural Networks.</li> </ul> <p>A full list of the model combinations can be found in the "Case Studies" document.</p> <p>The maps are in WGS 84/ UTM zone 32N (EPSG:32632).</p> <p>The map production process details are discussed in Xu et al. 2024. If you use the dataset, please, cite also the paper:</p> <p><em>Qiongjie Xu, Vasil Yordanov, Lorenzo Amici & Maria Antonia Brovelli (2024) Landslide susceptibility mapping using ensemble machine learning methods: a case</em><br><em>study in Lombardy, Northern Italy, International Journal of Digital Earth, 17:1, 2346263, DOI:10.1080/17538947.2024.2346263</em></p> <p>The maps are produced as part of the "Geoinformatics and Earth Observation for Landslide Monitoring" Italy-Vietnam.</p> <p>The work is partially funded by the Italian Ministry of Foreign Affairs and International Cooperation within the project “Geoinformatics and Earth Observation for Landslide Monitoring” CUP D19C21000480001.</p> <p> </p>
ScienceDex guides
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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.