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356 results for “Machine learning models”
Machine Learning Models for Surface Wave Dispersion Curve Inversion using Mixture Density Networks
<p>Machine learning (ML) approach for dispersion curve inversion using mixture density networks (MDN) based on Keil and Wassermann (2023).</p> <p>The ML approach presented here allows the simultaneous estimation of layer numbers, layer depth and a complete probability distribution of the S-wave velocity structure in the upper 100 m. This is achieved by a two-step ML approach, where 1) a regular NN classifies the number of layers within the upper 100 m of the subsurface and 2) individual trained mixture density networks output the depth estimates together with a fully probabilistic solution of the S-wave velocity structure. We trained the model to distinguish structures with 2 - 7 subsurface layers.</p> <p>The trained classification NN and the individual MDNs are located in the folder ./trained_models.<br> With the jupyter notebook Prediction.ipynb the dispersion curve inversion can be performed using the already trained ML models.<br> With the jupyter notebooks Training-MDN.ipynb and Training-classification.ipynb the models can be trained on new data.<br> The code for the set-up of the MDN is based on Earp et al. (2020).</p> <p> </p> <p>More details and updates on the code can be found on: <a href="https://github.com/SabrinaKeil/MDN_Inversion">https://github.com/SabrinaKeil/MDN_Inversion</a> </p>
Machine learning can be as good as maximum likelihood when reconstructing phylogenetic trees and determining the best evolutionary model on four taxon alignments
<p><span>Machine learning can be as good as maximum likelihood when reconstructing phylogenetic topologies and determining the best evolutionary model on four taxon alignments.</span></p> <p><span>Phylogenetic tree reconstruction with molecular data is important in many fields of life science research. The gold standard in this discipline is the Maximum Likelihood tree reconstruction method. Here we show that for quartet trees, Machine Learning using neural networks can be as good as the Maximum Likelihood method to infer the best tree topology and the best model of sequence evolution for nucleotide as well as amino acid sequences. For this purpose we simulated data sets for a wide range of branch lengths, evolutionary models and model parameters and compared the topologies and inferred models obtained with Machine learning with those obtained with the Maximum Likelihood and the Neighbour Joining method. Our results show that neural networks are a promising avenue for determining relatedness between taxa, which is likely to accelerate the construction of phylogenetic trees in the future, while maintaining a high accuracy.</span></p>
Towards Generalization of Machine Learning Models: An Arabic Sentiment Analysis Dataset
<p>This data set consists of approximately 1.64 Million Arabic tweets (shared by their IDs) posted from 2009 to 2020, and their corresponding sentiment using a three-point classification system of Positive, Negative and Neutral/Mixed. No specific locations and/or keywords were specified throughout the data collection to obtain variation in the dialects and topics represented within the dataset. It is important to note that any biases in the proposed dataset in relation to the dialects and/or topics discussed were unintentional.</p> <p><strong>Please use the following citation if you use this data in a paper:</strong></p> <blockquote> <p>Abdaljalil, S., Hassanein, S., Mubarak, H., & Abdelali, A. (2023). Towards Generalization of Machine Learning Models: A Case Study of Arabic Sentiment Analysis. <strong>Proceedings of the International AAAI Conference on Web and Social Media, 17(1), 971-980.</strong></p> </blockquote> <p> </p> <p> </p>
BRCA1-specific machine learning model predicts variant pathogenicity with high accuracy - Supplementary material
<p>Figure S1: Distribution of the reviewed 141 <em>BRCA1</em> missense variants; Figure S2: The Shapely values for the <em>BRCA1</em> XGBoost models; Figure S3: The Shapely values of the <em>BRCA1</em> XGBoost model used to predict the functional assays’ results for variants of uncertain significance; Table S1: The receiver operating characteristic (ROC) curve analysis for the different in silico predictions; Table S2: Cross validation of the BRCA1 model in 5 different random training and test samples; Table S3: Pathogenicity prediction and prioritization of the 31,058 unreviewed BRCA1 variants from the BRCA Exchange database.</p>
Data for: ToadFishFinder classifier model v4: A catalog of oyster toadfish (Opsanus tau) calls for machine learning
<p>This data repository contains labeled passive underwater acoustic data used to train and test the machine-learning model of Bohnenstiehl (in prep – 2023), <span>Automated cataloging of oyster toadfish (<em>Opsanus</em> <em>tau</em>) calls using template matching and machine learning</span>. The software accompanying this paper is known as ToadFishFinder, and the classifier model presented in the paper is v4. It consists of more than 10000 labeled toadfish and 10000 labeled other signals. Labeled spectrogram images are provided, along with pressure-corrected waveforms (micro-Pascals) sampled at 24 kHz. Each waveform sample is 1350 ms long. The center 850 ms of these waveform segments represent the portion of the signal used in training and testing the classifier model. Waveform data are provided in multiple formats: 1) MATLAB (.mat) files containing the 'boatwhistle' and 'other' waveforms stored in column format, and 2) individual .wav files, each containing a labeled waveform example. Codes are provided to demonstrate how these .wav files can be read into MATLAB and PYTHON. These labeled data can be used to re-train the ToadFishFinder model or develop alternative classifiers. </p>
Data for DRExM³L: Drug REpurposing using eXplainable Machine Learning and Mechanistic Models of signal transduction
<p>(DREM³L) Drug REpurposing using Mechanistic Models of signal transduction and Machine Learning </p>
Soil chemistry dataset from the work "Modelling and prediction of major soil chemical properties with Random Forest: machine learning as tool to understand soil-environment relationships in Antarctica"
<p>Bases sum, H+Al (potential acidity), pH, phosphorous, remaining P (P-rem), sodium and total organic carbon distribution in Antarctic soils modeled and predicted through Machine Learning approaches, legacy soil data and environmental covariates. The quantile and prediction interval data represent the spatial uncertainty of the predictions.</p> <p>As soon as the work "Modelling and prediction of major soil chemical properties with Random Forest: machine learning as tool to understand soil-environment relationships in Antarctica" is published, the paper will be cited here. </p> <p>The .zip file contains the following folders:</p> <p>1) soil_chemistry_antarctica: data containing the soil chemical attributes distribution</p> <p>2) soil_chemistry_prediction_interval: uncertainty from the prediction interval 90% (Q95% - Q5%) of the soil attributes prediction</p> <p>4) soil_texture_quantile05: quantile 5% of the soil attributes prediction</p> <p>5) soil_texture_quantile95: quantile 95% of the soil attributes prediction</p>
Data from: A cost-effective blood DNA methylation-based age estimation method in domestic cats, Tsushima leopard cats (Prionailurus bengalensis euptilurus), and Panthera species, using targeted bisulfite sequencing and machine learning models
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Data from: Performance of unmarked abundance models with data from machine-learning classification of passive acoustic recordings
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Data for: PerchPicker classifier model v7: A catalog of American silver perch (Bairdiella chrysoura) calls for machine learning
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Data for: ToadFishFinder classifier model v4: A catalog of oyster toadfish (Opsanus tau) calls for machine learning
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Machine learning can be as good as maximum likelihood when reconstructing phylogenetic trees and determining the best evolutionary model on four taxon alignments
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Isotope mixing scenarios and machine learning model in: To what extent are the source mixing models accurate: evaluation of the model accuracy and guidelines for the site-specific model selection
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Data from: TC-GEN: Data-driven tropical cyclone downscaling using machine learning-based high-resolution weather model
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Vocalization data and scripts to model reindeer rut activity using on-animal acoustic recorders and machine learning
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Assessing predictive performance of supervised machine learning algorithms for a diamond pricing model
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A Machine-Learning-Based Global Atmospheric Forecast Model
<p>Data used in "A Machine-Learning-Based Global Atmospheric Forecast Model" 2020. Included in this dataset is the machine learning predictions and the truth for the year's worth of simulated forecast. </p>
A machine learning framework for computationally expensive transient models
<p>The following dataset contains DEM simulation data on multiple material properties and operational parameters and their impact on uniform mixing time. Unifrom mixing time is calculated when segregation index reaches 1.1. Segregation index is defined based on the paper: Marigo, M., Cairns, D. L., Davies, M., Ingram, A. & Stitt, E. H. A numerical comparison of mixing efficiencies of solids in a cylindrical vessel subject to a range of motions. <em>Powder Technol.</em> <strong>217</strong>, 540–547 (2012). </p> <p>The dataset was used for training ML model as described in paper: <a href="https://arxiv.org/abs/1907.05928">https://arxiv.org/abs/1907.05928</a></p>
Data and code for training and evaluating machine learning models for thunderstorm prediction from reanalysis data
<p>FIXED Data and Python code for training and evaluating machine learning models for predicting thunderstorms, associated with the paper:</p> <p>"Evaluation of machine learning classifiers for predicting deep convection"</p> <p>by Peter Ukkonen and Antti Mäkelä (to appear in JAMES)</p> <p>The data (preprocessed inputs and outputs) is stored as netCDF files and .mat files which can be loaded with Python. </p>
Data and code for paper "A gray-box model for a probabilistic estimate of regional ground magnetic perturbations: Enhancing the NOAA operational Geospace model with machine learning"
<p>Simulation results from the NOAA/SWPC Geospace model used in the paper Camporeale et al. (2020) "A gray-box model for a probabilistic estimate of regional ground magnetic perturbations: Enhancing the NOAA operational Geospace model with machine learning" published in J. Geophys. Res. (2020)</p> <p>MATLAB code is provided to train process the data and train the machine learning model and plot results.</p> <p>Manuscript available on <a href="https://arxiv.org/abs/1912.01038">https://arxiv.org/abs/1912.01038</a></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.