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Logistic Regression
With the rise of Big Data in sports performance analysis based on data and a wide variety of possibilities have opened. In the last, 10 years...
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penalizedclr: an R package for penalized conditional logistic regression for integration of multiple omics layers
BackgroundThe matched case–control design, up until recently mostly pertinent to epidemiological studies, is becoming customary in biomedical...
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Logistic regression versus XGBoost for detecting burned areas using satellite images
Classical statistical methods prove advantageous for small datasets, whereas machine learning algorithms can excel with larger datasets. Our paper...
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Identification of miRNA biomarkers for breast cancer by combining ensemble regularized multinomial logistic regression and Cox regression
BackgroundBreast cancer is one of the most common cancers in women. It is necessary to classify breast cancer subtypes because different subtypes...
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Regression Analysis
Besides stratified analysis, regression analysisRegression analysis is a way to control for confounding in the analysis of a study’s data. The way we...
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Application of the class-balancing strategies with bootstrap** for fitting logistic regression models for post-fire tree mortality in South Korea
We aimed to tackle a common problem in post-fire tree mortality where the number of trees that survived surpasses the number of dead trees. Here, we...
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Improved Regularized Multi-class Logistic Regression for Gene Classification with Optimal Kernel PCA and HC Algorithm
A significant challenge in high-dimensional and big data analysis is related to the classification and prediction of the variables of interest. The...
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Detecting narwhal foraging behaviour from accelerometer and depth data using mixed-effects logistic regression
BackgroundDue to their Arctic habitat and elusive nature, little is known about the narwhal ( Monodon monoceros ) and its foraging behaviour....
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Cellular clarity: a logistic regression approach to identify root epidermal regulators of iron deficiency response
BackgroundPlants respond to stress through highly tuned regulatory networks. While prior works identified master regulators of iron deficiency...
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Risk Factor Prediction by Naive Bayes Classifier, Logistic Regression Models, Various Classification and Regression Machine Learning Techniques
Cardiovascular disease (CVD) is globally considered as the leading cause of death. As per the World Health Organization, cardiovascular diseases...
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A machine learning one-class logistic regression model to predict stemness for single cell transcriptomics and spatial omics
Cell annotation is a crucial methodological component to interpreting single cell and spatial omics data. These approaches were developed for single...
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Regression
Regression analysis is a set of statistical methods for estimating relationships between a dependent variable and one or more independent variables....
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Establishment and validation of a logistic regression model for prediction of septic shock severity in children
BackgroundSeptic shock is the most severe complication of sepsis, and is a major cause of childhood mortality, constituting a heavy public health...
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Statistical Analysis and Logistic Regression to Assess How COVID-19 Has Changed Department of General Medicine Patients’ Management: A Bicentric Study
The purpose of the present work was to assess the impact of the Covid-19 epidemic on the activity of the Department of General Medicine in the...
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Toward a logistic model of dynamic mold growth on wood
This paper deals with mathematical modeling of mold growth on wood. The logistic growth equation is used to model mold growth phenomena in changing...
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RNA Coding Potential Prediction Using Alignment-Free Logistic Regression Model
CPAT (Coding-Potential Assessment Tool) is a logistic regression model–based classifier that can accurately and quickly distinguish protein-coding...
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Mixed logistic regression in genome-wide association studies
BackgroundMixed linear models (MLM) have been widely used to account for population structure in case-control genome-wide association studies, the...
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Effect of Laboratory Predictors on Septic Mortality, 200 Patients, Traditional Regression vs Kernel Ridge Regression
In a 200 patient data file of patients with sepsis the effect of laboratory predictors on survival/septic death was assessed. Traditional regression...
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Prediction of Distribution of Dry Matter and Leaf Area of Faba Bean (Vicia faba) Using Nonlinear Regression Models
Growth analysis is a valuable method for quantitatively investigating the growth and development of products. To analyze plant growth during the...
