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96 results for “Machine learning algorithms”
Dataset for: Sky pixel detection in outdoor imagery using an adaptive algorithm and machine learning.
<p>The data presented in this article is related to the research article entitled ``Sky pixel detection in outdoor imagery using an adaptive algorithm and machine learning." \citep{Nice2019UC}.</p> <p>The dataset consists of a trained Inception V3 neural network model as well as the configuration files to train the neural network and run the inferences. The dataset also contains two sets of outdoor imagery (from Skyfinder and Google Street View) used to train the neural network and validate the sky pixel detection system in the linked article. The original images are included as well as rescaled imagery used to train the neural network, and sky masks used for validation.</p>
Identification of Kidney Cell Types in scRNA-seq and snRNA-seq Data Using Machine Learning Algorithms
<p>Metadata files for our final analysis as part of the "<span><span>Identification of Kidney Cell Types in scRNA-seq and snRNA-seq Data Using Machine Learning Algorithms" study.</span></span></p>
Comparison and assessment of different object-based classifications using machine learning algorithms and UAVs multispectral imagery in the framework of precision agriculture
<p>Supplementary material of the paper</p>
Clinical Categorization Algorithm (Clical) and Machine-Learning Approach (Srf-clical) to Predict Clinical Benefit to Immunotherapy in Metastatic Melanoma Patients: Real-world Evidence from Istituto Nazionale Tumori Irccs Fondazione Pascale, Napoli, Italy.
<p>Raw-data related to a manuscript submitted to "Cancers" journal - MDPI - https://www.mdpi.com/journal/cancers</p> <p><strong>Manuscript Title</strong>: Clinical Categorization Algorithm (Clical) and Machine-Learning Approach (Srf-clical) to Predict Clinical Benefit to Immunotherapy in Metastatic Melanoma Patients: Real-world Evidence from Istituto Nazionale Tumori Irccs Fondazione Pascale, Napoli, Italy.</p> <p><strong>Authors:</strong> Gabriele Madonna1,#, Giuseppe V. Masucci2,3,#, Mariaelena Capone1, Domenico Mallardo1, Antonio Maria Grimaldi1, Ester Simeone1, Vito Vanella1, Lucia Festino1, Marco Palla1, Luigi Scarpato1, Marilena Tuffanelli1, Grazia D’angelo1, Lisa Villabona2, Isabelle Krakowski2,4, Hanna Eriksson2,3, Felipe Simao5, Rolf Lewensohn2,3, Paolo Antonio Ascierto1,+</p> <p><strong>Affiliations</strong>:</p> <p>1 Cancer Immunotherapy and Development Therapeutics Unit, Istituto Nazionale Tumori IRCCS Fondazione "G. Pascale", Napoli, Italy</p> <p>2 Theme Cancer, Karolinska University Hospital, Stockholm, Sweden</p> <p>3 Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden</p> <p>4 Theme Inflammation, Karolinska University Hospital Stockholm, Sweden</p> <p>5 Genevia technologies OY, Tampere, Finland</p> <p># these authors equally contributed</p> <p>+ Corresponding author</p> <p><strong>Abstract of submitted Manuscript:</strong> The real-life application of immune checkpoint inhibitors (ICI) may yield different outcomes compared to the benefit presented in clinical trials. For this reason, there is a need to define the group of patients that may benefit from treatment. We retrospectively investigated 578 metastatic melanoma patients treated with ICI at Istituto Nazionale Tumori IRCCS Fondazione “G. Pascale” of Napoli Italy (INT-NA). To compare patients’ clinical variables (age, Lactate Dehydrogenase (LDH), Neutrophil-Lymphocyte Ratio (NLR), eosinophil, BRAF status, previous treatment) and their predictive and prognostic power in a comprehensive non-hierarchical way, a Clinical Categorization Algorithm (CLICAL) was defined and validated by the application of machine learning, Survival Random Forest (SRF-CLICAL). The comprehensive analysis of the clinical parameters by log risk-based algorithms convened into predictive signatures that could identify groups of patients with great benefit or not, regardless of the ICI received. From a real-life retrospective analysis of metastatic melanoma patients, we generated and validated an algorithm based on machine learning that could assist with the clinical decision of whether or not to apply ICI therapy by defining five signatures of predictability with a 95% accuracy.</p> <p><strong>Funding: </strong>This research was funded by Italian Ministry of Health (IT-MOH) through “Ricerca Corrente”, grants number M2-2. Additional funding [N#184093) from the Stockholm Cancer Society and King Gustav V’s Jubilee foundation Stockholm.</p>
Screening of key risk SNPs for glioma based on machine learning algorithms
<p>Glioma is a common primary malignant brain tumor and is the most aggressive and lethal solid tumor, accounting for approximately 80% of all intracranial malignancies. Our aim was to screen key SNP by LASSO regression and random forest (a machine learning algorithm) and construct a model based on these SNP to predict the risk of glioma in Chinese Han population.</p>
Predicting species richness and diversity using satellite remote sensing and random forest machine learning algorithm
<p><strong>Aims</strong>: Remote sensing approaches could be beneficial for monitoring and compiling essential biodiversity data because it is cost-effective and allows for coverage of large areas over a short period. This study investigated the relationship between multispectral remote sensing data from Landsat 8 and Sentinel 2 and species richness and diversity in mountainous and protected grasslands.</p> <p><strong>Locations</strong>: Golden Gate Highlands National Park, Free State, South Africa. </p> <p><strong>Methods</strong>: In-situ data of plant species composition and cover from 142 plots with 16 releves each were distributed across the study site and used to calculate species richness and Shannon-wiener species diversity index (species diversity. We used a machine-learning random forest algorithm to optimise the prediction of species richness and diversity. The algorithm was used to identify the optimal spectral bands and vegetation indices for estimating species richness and diversity. Subsequently, the selected bands and vegetation indices were used to estimate species richness through random forest regression. </p> <p><strong>Results</strong>: This research found weak relationships between remote sensing vegetation indices and the diversity metrics, but significant relationships were found between some spectral bands and diversity metrics. Moreover, using machine learning random forest, the multispectral datasets exhibited strong predictive powers. In this investigation, for both sensors, near-infrared (NIR) seemed to be the most selected band to explain species diversity in mountainous grasslands.</p> <p><strong>Main</strong> <strong>conclusions</strong>: This finding further ascertains the efficiency of using NIR in vegetation mapping. This research shows that NIR, SAVI and EVI are the most adequate for predicting species richness and diversity in mountainous grasslands with relatively good accuracies.</p>
Data for training AMSR2-CNN and its corresponding machine learning algorithm
<p>Despite the availability of multiple decades of passive microwave measurements from satellite platforms, their utility for developing quantitative, spatially distributed estimates of snowpack is yet to be realized. A major bottleneck is the use of simple conceptual retrieval model formulations that are ineffective in representing the significant heterogeneity and complexity of snow evolution, particularly over areas with complex topography and forest regions. Here we demonstrate a physics-constrained and interpretable Convolutional Neural Network (CNN) to learn the functional relationship utilizing multi-channel passive microwave brightness temperature measurements from the Advanced Microwave Scanning Radiometer 2 (AMSR2) and in-situ snow depth observations. The machine learning approach with CNN generates vastly improved snow depth estimates relative to the standard AMSR2 estimates. Compared to independent in-situ measurements of snow depth over the Continental United States, the domain averaged Pearson correlation measure is three times higher than that of the standard AMSR2 estimates (R<sup>2</sup>: 0.68 versus 0.21), while the systematic errors are reduced by approximately fourfold. Further, the CNN-based snow depth estimates also exhibit notable enhancements in regions with forests, deep snow, and melting snow, thereby alleviating the limitations faced by traditional algorithms in retrieving accurate snow depths. The interpretation of the CNN framework further indicates that the machine learning approach dynamically leverages both volume scattering and emission components from a suite of measured passive microwave signals to generate more accurate snow depth retrievals. The results of this study provide an important benchmark of high-quality snow retrievals from passive microwave satellite measurements by maximizing their information content.</p>
Machine-Learning Algorithm for Prediction of Blood Pressure, Glycated Haemoglobin and Estimated Glomerular Filtration Rate
ClinicalTrials.gov study NCT04814680. IPD Sharing: YES. Countries: 1. Publications: 0.
A Novel Machine Learning Algorithm to Predict the Lewy Body Dementias
ClinicalTrials.gov study NCT04448340. IPD Sharing: NO. Countries: 1. Publications: 1.
Development and Pre-validation of a Machine Learning-based Prediction Algorithm for Early Functional Recovery in Patients Undergoing Hip and Knee Replacement Surgery
ClinicalTrials.gov study NCT07333560. IPD Sharing: NO. Countries: 1. Publications: 6.
Patient-Ventilator Dyssynchrony Detection With a Machine Learning Algorithm
ClinicalTrials.gov study NCT06506123. IPD Sharing: NO. Countries: 1. Publications: 6.
Towards a Wearable Alcohol Biosensor: Examining the Accuracy of BAC Estimates From New-Generation Transdermal Technology Using Large-Scale Human Testing and Machine Learning Algorithms
ClinicalTrials.gov study NCT05692830. IPD Sharing: UNDECIDED. Countries: 1. Publications: 27.
Clinical Prediction Models for Pediatric In-Hospital Death Risk in Congolese Severe Malaria Children Using Machine Learning Based-Algorithms
ClinicalTrials.gov study NCT07355426. IPD Sharing: UNDECIDED. Countries: 1. Publications: 4.
Impact of Machine Learning-based Clinician Decision Support Algorithms in Perioperative Care
ClinicalTrials.gov study NCT05809232. IPD Sharing: NO. Countries: 1. Publications: 1.
To Construct a Prognosis Prediction Model for ECMO Patients Based on Machine Learning Algorithms
ClinicalTrials.gov study NCT06654388. IPD Sharing: NO. Countries: 1. Publications: 2.
Predicting species richness and diversity using satellite remote sensing and random forest machine learning algorithm
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Data from: QTG-Finder2: a generalized machine-learning algorithm for prioritizing QTL causal genes in plants
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Data for training AMSR2-CNN and its corresponding machine learning algorithm
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data for "Validation of Rapid and Low-Cost Approach for the Delineation of Zone Management Based on Machine Learning Algorithms"
<p>These data were used in article "Validation of Rapid and Low-Cost Approach for the Delineation of Zone Management Based on Machine Learning Algorithms" (https://doi.org/10.3390/agronomy12010183)</p>
Online machine learning algorithms to predict link quality in community wireless mesh networks
<p>FunkFeuer raw topology data from 15-01-2016 to 28-01-2016 retrieved on 17-02-2016 from http://opendata.confine-project.eu/dataset/funkfeuer-topology-data. This dataset was employed in "Miguel L. Bote-Lorenzo, Eduardo Gómez-Sánchez, Carlos Mediavilla-Pastor, Juan I. Asensio-Pérez, Online machine learning algorithms to predict link quality in community wireless mesh networks, Computer Networks, Volume 132, 2018, Pages 68-80, https://doi.org/10.1016/j.comnet.2018.01.005."</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.