Articles with "prediction accuracy" as a keyword



Photo from wikipedia

Investigation of prediction accuracy and the impact of sample size, ancestry, and tissue in transcriptome‐wide association studies

Sign Up to like & get
recommendations!
Published in 2020 at "Genetic Epidemiology"

DOI: 10.1002/gepi.22290

Abstract: In transcriptome‐wide association studies (TWAS), gene expression values are predicted using genotype data and tested for association with a phenotype. The power of this approach to detect associations relies, at least in part, on the… read more here.

Keywords: accuracy; tissue; association; prediction accuracy ... See more keywords
Photo by ggfujyoj from unsplash

The Limitations of Existing Approaches in Improving MicroRNA Target Prediction Accuracy.

Sign Up to like & get
recommendations!
Published in 2017 at "Methods in molecular biology"

DOI: 10.1007/978-1-4939-7046-9_10

Abstract: MicroRNAs (miRNAs) are small (18-24 nt) endogenous RNAs found across diverse phyla involved in posttranscriptional regulation, primarily downregulation of mRNAs. Experimentally determining miRNA-mRNA interactions can be expensive and time-consuming, making the accurate computational prediction of… read more here.

Keywords: mirna mrna; target; prediction accuracy; limitations existing ... See more keywords
Photo from wikipedia

A Machine Learning-Based Method to Identify Bipolar Disorder Patients

Sign Up to like & get
recommendations!
Published in 2022 at "Circuits, Systems, and Signal Processing"

DOI: 10.1007/s00034-021-01889-1

Abstract: Bipolar disorder is a serious psychiatric disorder characterized by periodic episodes of manic and depressive symptomatology. Due to the high percentage of people suffering from severe bipolar and depressive disorders, the modelling, characterisation, classification and… read more here.

Keywords: prediction accuracy; disorder; method; machine learning ... See more keywords
Photo by campaign_creators from unsplash

Gramian matrix data collection-based random forest classification for predictive analytics with big data

Sign Up to like & get
recommendations!
Published in 2019 at "Soft Computing"

DOI: 10.1007/s00500-019-04014-2

Abstract: Prediction is the process of analyzing the current and past events to identify future events. The prediction of the subsequent future conditions is still a revealing stage in many applications to minimize the risk level.… read more here.

Keywords: prediction accuracy; collection based; data collection; prediction ... See more keywords
Photo from wikipedia

Energy demand forecasting using a novel remnant GM(1,1) model

Sign Up to like & get
recommendations!
Published in 2020 at "Soft Computing"

DOI: 10.1007/s00500-020-04765-3

Abstract: Grey prediction models play a significant role in forecasting energy demand, particularly the GM(1,1) model. To increase the prediction accuracy of the original GM(1,1) model, the corresponding residual GM(1,1) model is often recommended. However, the… read more here.

Keywords: model; prediction accuracy; prediction; energy demand ... See more keywords
Photo from wikipedia

The power load’s signal analysis and short-term prediction based on wavelet decomposition

Sign Up to like & get
recommendations!
Published in 2017 at "Cluster Computing"

DOI: 10.1007/s10586-017-1316-3

Abstract: The complex signal represented by power load is affected by many factors, so the signal components are very complicated. So that, it is difficult to obtain satisfactory prediction accuracy by using a single model for… read more here.

Keywords: model; prediction accuracy; prediction; power load ... See more keywords
Photo from wikipedia

Impact of the complexity of genotype by environment and dominance modeling on the predictive accuracy of maize hybrids in multi-environment prediction models

Sign Up to like & get
recommendations!
Published in 2021 at "Euphytica"

DOI: 10.1007/s10681-021-02779-y

Abstract: The prediction accuracy of multi-environment prediction models can be affected by the complexity of the genotype by environment interaction (G×E). Moreover, depending on the trait genetic architecture, accounting for non-additive effects, such as dominance effects,… read more here.

Keywords: multi environment; prediction accuracy; prediction; complexity ... See more keywords
Photo from wikipedia

Gaussian Processes for improving orbit prediction accuracy

Sign Up to like & get
recommendations!
Published in 2019 at "Acta Astronautica"

DOI: 10.1016/j.actaastro.2019.05.014

Abstract: Abstract A machine learning (ML) approach has been recently proposed to improve the orbit prediction accuracy of resident space objects (RSOs) through learning from historical data. Previous results have shown that the ML approach can… read more here.

Keywords: orbit prediction; gaussian processes; prediction accuracy;
Photo from wikipedia

Study on influencing factors of prediction accuracy of support vector machine (SVM) model for NOx emission of a hydrogen enriched compressed natural gas engine

Sign Up to like & get
recommendations!
Published in 2018 at "Fuel"

DOI: 10.1016/j.fuel.2018.07.009

Abstract: Abstract In recent years, support vector machine (SVM) method has been rapidly developed because of its great advantage in solving small sample regression problems. Based on the prediction accuracy of NO x emission, the SVM… read more here.

Keywords: svm; regression; model; prediction accuracy ... See more keywords
Photo from wikipedia

A trustworthiness indicator to select sample points for the individual predictive soil mapping method (iPSM)

Sign Up to like & get
recommendations!
Published in 2020 at "Geoderma"

DOI: 10.1016/j.geoderma.2020.114440

Abstract: Abstract Observations of soil at georeferenced sample points are the indispensable input to digital soil mapping (DSM) models that relate soil properties and types to the soil-forming environment. Many existing DSM methods require soil samples… read more here.

Keywords: sample; method; prediction accuracy; sample points ... See more keywords
Photo from wikipedia

Balancing prediction accuracy and generalization ability: A hybrid framework for modelling the annual dynamics of satellite-derived land surface temperatures

Sign Up to like & get
recommendations!
Published in 2019 at "ISPRS Journal of Photogrammetry and Remote Sensing"

DOI: 10.1016/j.isprsjprs.2019.03.013

Abstract: Abstract Annual temperature cycle (ATC) models enable the multi-timescale analysis of land surface temperature (LST) dynamics and are therefore valuable for various applications. However, the currently available ATC models focus either on prediction accuracy or… read more here.

Keywords: generalization ability; surface; accuracy; prediction accuracy ... See more keywords