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356
datasets available to search
ShareScore release 0.9.0
Dataset results
356 results for “Machine learning models”
Application of an Antimicrobial Stewardship Program in Brazilian ICUs Using Machine Learning Techniques and an Educational Model
ClinicalTrials.gov study NCT05312034. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Comparison of Six Different Machine Learning Methods With Traditional Model for Low Anterior Resection Syndrome After Minimally Invasive Surgery for Rectal Cancer -- Development and External Validatio
ClinicalTrials.gov study NCT07267767. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Machine Learning and 3D Image-based Modeling for Body Weight Estimation.
ClinicalTrials.gov study NCT06281938. IPD Sharing: YES. Countries: 0. Publications: 0.
Machine learning analysis of the orbitofrontal cortex transcriptome of human opioid users identifies Shisa7 as a translational target relevant for heroin-seeking leveraging a male rat model
GEO Series GSE280431. Rattus norvegicus; Homo sapiens. 100 samples. Type: Expression profiling by high throughput sequencing.
A machine learning breast cancer prediction model based on a panel from circulating exosomal miRNAs
GEO Series GSE197020. Homo sapiens. 96 samples. Type: Non-coding RNA profiling by high throughput sequencing.
The brain tumor classifications based on machine learning models trained with older version of methylation microarray chip compatible with the new EPIC v2 illumina’s chip.
GEO Series GSE229715. Homo sapiens. 32 samples. Type: Methylation profiling by genome tiling array.
Diversity-aware Fairness Testing of Machine Learning Models through Hashing-based Sampling
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Machine Learning Prediction Models for Mitral Valve Repairability and Mitral Regurgitation Recurrence in Patients Undergoing Surgical Mitral Valve Repair
<p>This record contains raw data related to the article "Machine Learning Prediction Models for Mitral Valve Repairability and Mitral Regurgitation Recurrence in Patients Undergoing Surgical Mitral Valve Repair"</p> <p>Abstract: Background: Mitral valve regurgitation (MR) is the most common valvular heart disease and current variables associated with MR recurrence are still controversial. We aim to develop a machine learning-based prognostic model to predict causes of mitral valve (MV) repair failure and MR recurrence. Methods: 1000 patients who underwent MV repair at our institution between 2008 and 2018 were enrolled. Patients were followed longitudinally for up to three years. Clinical and echocardiographic data were included in the analysis. Endpoints were MV repair surgical failure with consequent MV replacement or moderate/severe MR (>2+) recurrence at one-month and moderate/severe MR recurrence after three years. Results: 817 patients (DS1) had an echocardiographic examination at one-month while 295 (DS2) also had one at three years. Data were randomly divided into training (DS1: n = 654; DS2: n = 206) and validation (DS1: n = 164; DS2 n = 89) cohorts. For intra-operative or early MV repair failure assessment, the best area under the curve (AUC) was 0.75 and the complexity of mitral valve prolapse was the main predictor. In predicting moderate/severe recurrent MR at three years, the best AUC was 0.92 and residual MR at six months was the most<br> important predictor. Conclusions: Machine learning algorithms may improve prognosis after MV repair procedure, thus improving indications for correct candidate selection for MV surgical repair.</p>
Constructing a rat model of stress cardiomyopathy and screening for diagnostic markers using machine learning algorithms
GEO Series GSE223385. Rattus norvegicus. 20 samples. Type: Expression profiling by high throughput sequencing.
Database for Automatic Risk Tuning in Short-Term Electricity Market Models Using a Machine Learning Proxy
<p>Database (2014-2018) used in the paper entitled "Automatic Risk Tuning in Short-Term Electricity Market Models Using a Machine Learning Proxy".</p> <p>This database includes :</p> <ul> <li>the inputs and outputs of the probabilistic forecaster which predicts the Belgian system imbalance.</li> <li>the market data related to the construction of the balancing market.</li> </ul> <p>These data are obtained from the Belgian transmission system operator (Elia) and the European Network of Transmission system Operators (ENTSO-E).</p> <p>If you use these data, please refer to the following paper:</p> <p>J. Bottieau, K. Bruninx, A. Sanjab, Z. De Grève, F. Vallée and J-F. Toubeau, “Automatic Risk Tuning in Short-Term Electricity Market Models Using a Machine Learning Proxy,”.</p>
[OUTDATED] Data set [ref. paper "Predictive modeling of drivers' brake reaction time through machine learning methods"]
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Inactive-enriched machine-learning models exploiting patent data improve structure-based virtual screening for PDL1 dimerizers
<p>The 12 VS scenarios considered in this study employing six training-test data partitions<strong> </strong>(A-F). All training sets employ the same set of 371 actives (WO2015160641A2), but differ on the considered set of inactives and hence are uniquely identified by the latter (either TrueInactives, DeepCoys, RandomDecoys or ActivesOnly). Likewise, all test sets employ the same 297 actives (WO201503820A1), none of them also included in the training set, but different sets of inactives (TrueInactives or DeepCoys). </p> <p> </p> <table align="center"> <caption>Table 1. Six virtual screening scenarios corresponding to six pairs of training-test data for each type of SFs (classification or regression)</caption> <thead> <tr> <th scope="col">Partition ID</th> <th scope="col">Training set</th> <th scope="col">Test set</th> <th scope="col">Type</th> </tr> </thead> <tbody> <tr> <td>A</td> <td>DeepCoys</td> <td>TrueInactives</td> <td>Classification</td> </tr> <tr> <td>B</td> <td>RandomDecoys</td> <td>TrueInactives</td> <td>Classification</td> </tr> <tr> <td>C</td> <td>ActivesOnly</td> <td>TrueInactives</td> <td>Classification</td> </tr> <tr> <td>D</td> <td>TrueInactives</td> <td>DeepCoys</td> <td>Classification</td> </tr> <tr> <td>E</td> <td>RandomDecoys</td> <td>DeepCoys</td> <td>Classification</td> </tr> <tr> <td>F</td> <td>ActivesOnly</td> <td>DeepCoys</td> <td>Classification</td> </tr> <tr> <td>A</td> <td>DeepCoys</td> <td>TrueInactives</td> <td>Regression</td> </tr> <tr> <td>B</td> <td>RandomDecoys</td> <td>TrueInactives</td> <td>Regression</td> </tr> <tr> <td>C</td> <td>ActivesOnly</td> <td>TrueInactives</td> <td>Regression</td> </tr> <tr> <td>D</td> <td>TrueInactives</td> <td>DeepCoys</td> <td>Regression</td> </tr> <tr> <td>E</td> <td>RandomDecoys</td> <td>DeepCoys</td> <td>Regression</td> </tr> <tr> <td>F</td> <td>ActivesOnly</td> <td>DeepCoys</td> <td>Regression</td> </tr> </tbody> </table> <p> </p>
Raw data used to build models in SIMON Automated Machine Learning
<p>Here you can find all data and all information regarding each generated dataset.<br> For each dataset there are 4 files:</p> <p>json_info : This file contains, number of features with their names and number of subjects that are available for the same dataset<br> data_testing: data frame with data used to test trained model<br> data_training: data frame with data used to train models<br> results: direct unfiltered data from database</p> <p><br> Files are written in feather format.</p> <p><a href="https://gist.github.com/LogIN-/00d7628e0850f843ba84a678fac0a103">Here is an example</a> of data structure for each file in repository</p> <p> </p>
Supplementary Data and Models of Melt-based Thermo-barometer for paper "'No Free Lunch' in Tabular Geochemical Data: An example of Shallow versus Deep Machine Learning Algorithms for Geothermobarometry"
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Predicting the skin sensitization potential of small molecules with machine learning models trained on biologically meaningful descriptors
<p>In recent years a number of machine learning models for the prediction of the skin sensitization potential of small organic molecules have been reported and become available. These models generally perform well within their applicability domains but, as a result of the use of molecular fingerprints and other non-intuitive descriptors, the interpretability of the existing models is clearly limited. The aim of this work is to develop a strategy to replace the non-intuitive features by predicted outcomes of bioassays. We show that such replacement is indeed possible and that as few as ten interpretable, predicted bioactivities are sufficient to reach competitive performance. On a holdout data set of 257 compounds, the best model ("Skin Doctor CP:Bio") obtained an efficiency of 0.82 and an MCC of 0.52 (at the significance level of 0.20). Skin Doctor CP:Bio is available from the authors free of charge for academic research. The modeling strategies explored in this work are easily transferable and could be adopted for the development of more interpretable machine learning models for the prediction of the bioactivity and toxicity of small organic compounds.</p> <p>The corresponding research article has been published in <em>Pharmaceuticals</em> <strong>2021</strong>, <em>14</em>(8), 790, DOI: <a href="https://doi.org/10.3390/ph14080790">https://doi.org/10.3390/ph14080790</a></p>
Dataset related to article "Multicentric evaluation of a machine learning model to streamline the radiotherapy patient specific quality assurance process"
<p> This record contains raw data related to article “Multicentric evaluation of a machine learning model to streamline the radiotherapy patient specific quality assurance process"</p> <p><em>Purpose:</em> Patient-specific quality assurance (PSQA) is performed to ensure that modulated treatment plans can be delivered as intended, but constitutes a substantial workload that could slow down the radiotherapy process and delay the start of clinical treatments. In this study, we investigated a machine learning (ML) tree-based ensemble model to predict the gamma passing rate (GPR) for volumetric modulated arc therapy (VMAT) plans.</p> <p><em>Materials and Methods:</em> 5622 VMAT plans from multiple treatment sites were selected from a database of Institution 1 and the ML model trained using 19 metrics. PSQA analyses were performed automatically using criteria 3%/1 mm (global normalization, absolute dose, 10% threshold) and 95% action limit. Model’s performance was evaluated on an out-of-sample test set of Institution 1 and on two independent sets of measurements collected at Institution 2 and Institution 3. Mean absolute error (MAE), as well as the model’s sensitivity and specificity, were computed.</p> <p><em>Results:</em> The model obtained a MAE of 2.33%, 2.54% and 3.91% for the three Institutions, with a specificity of 0.90, 0.90 and 0.68, and a sensitivity of 0.61, 0.25, and 0.55, respectively. Small positive median values of the residuals (i.e., the difference between measurements and predictions) were observed for each Institution (0.95%, 1.66%, and 3.42%). Thus, the model’s predictions were, on average, close to the real values and provided a conservative estimation of the GPR.</p> <p><em>Conclusions</em>: ML models can be integrated into clinical practice to streamline the radiotherapy workflow, but they should be center-specific or thoroughly verified within centers before clinical use.</p> <p> </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.