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Dataset results
164 results for “prediction algorithms”
TECPR2 protein models predicted by three algorithms
<p>This ZIP-file contains the files used for TECPR2 protein modeling and resulting PDB (Protein Data Bank) files from three algorithms/pipelines (GalaxyWEB, trRosetta, SWISS-MODEL) used for clustering analysis and visualization in Neuser et al. ("Clinical, neuroimaging and molecular spectrum of <em>TECPR2-</em>associated hereditary sensory and autonomic neuropathy with intellectual disability").<br> We always used standard parameters and the respective top model ("model_1 | model01 | model1") for each algorithm/pipeline and/or downstream steps.</p>
High-resolution snow depth prediction using Random Forest algorithm with topographic parameters
Open the record for dataset details and reuse information.
Selected studies of the Artificial Intelligence Algorithms to Predict College Students academic performance: a Systematic Mapping Study
Open the record for dataset details and reuse information.
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>
A Feasibility Study to Improve Colorectal Cancer Screening Among Racially Diverse Zip Codes in a Persistent Poverty County Using Navigation and Machine Learning Predictive Algorithms
ClinicalTrials.gov study NCT05383976. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Effect of a Sepsis Prediction Algorithm on Clinical Outcomes
ClinicalTrials.gov study NCT03960203. IPD Sharing: Not stated. Countries: 0. Publications: 1.
Verification of Prediction Algorithm
ClinicalTrials.gov study NCT02863666. IPD Sharing: NO. Countries: 2. Publications: 0.
Algorithm for Predicting the Unfavorable Course of Sepsis in Children
ClinicalTrials.gov study NCT05908162. IPD Sharing: NO. Countries: 1. Publications: 0.
Development of quantitative direct prediction algorithm for the human target organ similarity of human pluripotent stem cell-derived organoids and cells
GEO Series GSE178858. Homo sapiens. 26 samples. Type: Expression profiling by high throughput sequencing.
tRForest: a novel random forest-based algorithm for tRNA-derived fragment target prediction
GEO Series GSE189510. Homo sapiens. 4 samples. Type: Expression profiling by high throughput sequencing.
A Random-Forest Based Algorithm for Prediction of Enhancers From Histone Modifications
GEO Series GSE37858. Homo sapiens. 3 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
DNase I hypersensitivity and algorithmic prediction of TF binding in early pancreatic mES directed differentiation
GEO Series GSE53776. Mus musculus. 6 samples. Type: Methylation profiling by high throughput sequencing.
Development of quantitative direct prediction algorithm for the human target organ similarity of human pluripotent stem cell-derived organoids and cells [RNA-seq]
GEO Series GSE178855. Homo sapiens. 24 samples. Type: Expression profiling by high throughput sequencing.
BayesAge: A Maximum Likelihood Algorithm To Predict Epigenetic Age
GEO Series GSE261769. Homo sapiens. 458 samples. Type: Methylation profiling by high throughput sequencing.
Accurate age prediction from blood using a small set of DNA methylation sites and a cohort-based machine learning algorithm
GEO Series GSE207605. Homo sapiens. 0 samples. Type: Methylation profiling by array; Third-party reanalysis.
Finite element data collected and Machine learning algorithms to predict the mechanical properties of innovative CLT
<p>This folder includes the data collected from the finite element simulations of the innovative CLT to compute its mechanical properties, the error of the closed-form solutions predicting the bending stiffness in the minor direction D22, the variation of the distance between the Reissner Mindlin and Bending Gradient theory in terms of spacing between lateral lamellas, the hyperparameters tuning of several ML algorithms (Regression Tree, Random Forest, Gradient Boosting and Artificial Neural Network), the ML evaluations, the saved artificial neural network algorithms to predict each mechanical property of innovative CLT, and the ML application to use it.</p>
Large-scale comparison of machine learning algorithms for target prediction of natural products
<p>Supplement Materials of the article named "Large-scale comparison of machine learning algorithms for target prediction of natural products".</p>
Open data for "Predicting dominant terrestrial biomes at a Global Scale: Assessments of machine learning algorithms, climate variables indexing, and extreme climate"
<p>______________________________________________________<br> This page contains public-domain data required to reconstruct simulation results in the manuscript "Predicting dominant terrestrial biomes at a Global Scale: Assessments of machine learning algorithms, climate variables indexing, and extreme climate," submitted by the following author.</p> <p>Author: Hisashi SATO (JAMSTEC) <br> email : hsatoscb_(at)_gmail.com</p> <p>______________________________________________________<br> 1. Folder "Code"<br> Detailed descriptions are available on the code. </p> <p>1-1. MachineLearningComparison.R<br> Machine learning programs using random forest (RF), naive Bayes classifier (NV), and support vector machine (SVM) algorithms.</p> <p>1-2. Analyse_MapSimilarity.R<br> Calculate coincidences of simulated potential natural vegetation (PNV) maps simulated by different models.</p> <p>1-3. Visualize_VCE.R<br> Generating VCE (Visualize Climate Image) for training CNN models.</p> <p>1-4. Visualize_Maps.R<br> Visualizing global PNV maps.</p> <p>1-5. Visualize_ClimateHistgrams.R<br> Visualizing histograms of climate datasets.</p> <p>______________________________________________________<br> 2. Folder "Input"</p> <p>2-1. Unified_BIOCLIM_WorldClim.csv<br> Input data for the current climate.<br> This file contains the following variables.<br> lon Longitude at the center of the grid<br> lat Latitude at the center of the grid<br> bio1~19 Average climate indices from BIOCLIM (AveI)<br> CDD~WSDI Extreme climate indices (CEI)<br> c1~c16 Fraction of PNV from MODIS data<br> tavg01~tavg12 Monthly mean air temperature from January to December (Ave)<br> prec01~prec12 Monthly precipitation from January to December (Ave)</p> <p>2-2. Unified_BIOCLIM_WorldClimFutureRCP85.csv<br> Input data for future climate (@RCP8.5)<br> Including variables are the same as Unified_BIOCLIM_WorldClim.csv</p> <p>2-3. BIOCLIM_RefNo.csv<br> This CSV file contains the following information for each grid.<br> lat: Latitude at the center of the grid<br> lon: Longitude at the center of the grid<br> latNo: Latitude number corresponding to the image file name<br> lonNo: Longitude number corresponding to the image file name<br> lineNo: No use. Don't mind.<br> vegNo: Most dominant PNV based on the Unified_BIOCLIM_WorldClim.csv</p> <p>______________________________________________________<br> 3. Folder "Output"</p> <p>3-1. PNV_sim<br> 3-2. PNV_sim_RCP85.csv<br> Current and future PNV maps from various models. These files are the main output files from the code MachineLearningComparison.R. For PNV maps from CNN models (m4p1~6) were supplemented. Detailed methods to build CNN models, please refer to the following manuscript.<br> Sato, H. & T. Ise (2022). "Predicting global terrestrial biomes with the LeNet convolutional neural network." Geoscientific Model Development 15(7): 3121-3132.</p> <p>Labels indicate combinations of machine-learning-algorithm and dataset for training the model. For example, In case of "m1p1", that column shows the simulation result of models trained with randomForest (RF) algorithm and Ave dataset.<br> m1: randomForest (RF)<br> m2: Support vector machine (SVM)<br> m3: Naive Bayes (NB)<br> m4: Convolutional Neural Network (CNN), which is NOT analysed in this code<br> p1: Ave<br> p2: Ave + CEI<br> p3: Ave + CEIpart<br> p4: AveI <br> p5: AveI + CEI<br> p6: AveI + CEIpart</p>
LinearCoFold and LinearCoPartition: Linear-Time Algorithms for Secondary Structure Prediction of Interacting RNA molecules
<p>LinearCoFold and LinearCoPartition</p>
Association Between Genetic Algorithm to Predict Hypertension Therapy and Response to Treatment
ClinicalTrials.gov study NCT03292900. IPD Sharing: NO. Countries: 1. Publications: 0.
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.