Moving beyond regression techniques in cardiovascular risk prediction: applying machine learning to address analytic challenges
Risk prediction plays an important role in clinical cardiology research. Traditionally, most risk
models have been based on regression models. While useful and robust, these statistical …
models have been based on regression models. While useful and robust, these statistical …
A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases
Autoimmune diseases are chronic, multifactorial conditions. Through machine learning (ML),
a branch of the wider field of artificial intelligence, it is possible to extract patterns within …
a branch of the wider field of artificial intelligence, it is possible to extract patterns within …
Using recursive feature elimination in random forest to account for correlated variables in high dimensional data
Background Random forest (RF) is a machine-learning method that generally works well
with high-dimensional problems and allows for nonlinear relationships between predictors; …
with high-dimensional problems and allows for nonlinear relationships between predictors; …
[책][B] Random forests
The general principle of random forests is to aggregate a collection of random decision
trees. The goal is, instead of seeking to optimize a predictor “at once” as for a CART tree, to …
trees. The goal is, instead of seeking to optimize a predictor “at once” as for a CART tree, to …
Comparing methods for detecting multilocus adaptation with multivariate genotype–environment associations
Identifying adaptive loci can provide insight into the mechanisms underlying local
adaptation. Genotype–environment association (GEA) methods, which identify these loci …
adaptation. Genotype–environment association (GEA) methods, which identify these loci …
[HTML][HTML] Genome-wide modeling of polygenic risk score in colorectal cancer risk
Accurate colorectal cancer (CRC) risk prediction models are critical for identifying
individuals at low and high risk of develo** CRC, as they can then be offered targeted …
individuals at low and high risk of develo** CRC, as they can then be offered targeted …
How many trees in a random forest?
TM Oshiro, PS Perez, JA Baranauskas - … 2012, Berlin, Germany, July 13-20 …, 2012 - Springer
Random Forest is a computationally efficient technique that can operate quickly over large
datasets. It has been used in many recent research projects and real-world applications in …
datasets. It has been used in many recent research projects and real-world applications in …
Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics
The random forest (RF) algorithm by Leo Breiman has become a standard data analysis tool
in bioinformatics. It has shown excellent performance in settings where the number of …
in bioinformatics. It has shown excellent performance in settings where the number of …
Random forests
Random Forests were introduced by Leo Breiman [6] who was inspired by earlier work by
Amit and Geman [2]. Although not obvious from the description in [6], Random Forests are …
Amit and Geman [2]. Although not obvious from the description in [6], Random Forests are …
Oxidative stress and metabolic perturbations in wooden breast disorder in chickens
B Abasht, MF Mutryn, RD Michalek, WR Lee - PloS one, 2016 - journals.plos.org
This study was conducted to characterize metabolic features of the breast muscle (pectoralis
major) in chickens affected with the Wooden Breast myopathy. Live birds from two purebred …
major) in chickens affected with the Wooden Breast myopathy. Live birds from two purebred …