Results 41 to 50 of about 338,729 (282)
Random Forests and Networks Analysis
, 2017 D. Wilson~\cite{[Wi]} in the 1990's described a simple and efficient algorithm based on loop-erased random walks to sample uniform spanning trees and more generally weighted trees or forests spanning a given graph. This algorithm provides a powerful tool
Avena, L. +3 more
core +3 more sources
A simple and effective approach to quantitatively characterize structural complexity
Scientific Reports, 2021 This study brings insight into interpreting forest structural diversity and explore the classification of individuals according to the distribution of the neighbours in natural forests. Natural forest communities with different latitudes and distribution
Gongqiao Zhang +3 more
doaj +1 more source
Risk bounds for purely uniformly random forests [PDF]
, 2010 Random forests, introduced by Leo Breiman in 2001, are a very effective statistical method. The complex mechanism of the method makes theoretical analysis difficult.
Genuer, Robin
core +3 more sources
FEBS Letters, EarlyView.In this study, we found that human cervical‐derived adipocytes maintain intracellular iron level by regulating the expression of iron transport‐related proteins during adrenergic stimulation. Melanotransferrin is predicted to interact with transferrin receptor 1 based on in silico analysis.
Rahaf Alrifai +9 more
wiley +1 more source
, 2023 The wealth of data being gathered about humans and their surroundings drives new machine learning applications in various fields. Consequently, more and more often, classifiers are trained using not only numerical data but also complex data objects. For example, multi-omics analyses attempt to combine numerical descriptions with distributions, time ...
Maciej Piernik +2 more
openaire +2 more sources
The human gut microbiome across the life course
FEBS Letters, EarlyView.Despite significant individual variation and continuous change throughout life, the human gut microbiome follows some life stage‐specific trends. This article provides a brief overview of how gut microbiome composition shifts across different phases of life. Created in BioRender. Özkurt, E. (2026) https://BioRender.com/8q4nrnc.
Alise J. Ponsero +4 more
wiley +1 more source
Block Forests: random forests for blocks of clinical and omics covariate data
BMC Bioinformatics, 2019 Background In the last years more and more multi-omics data are becoming available, that is, data featuring measurements of several types of omics data for each patient. Using multi-omics data as covariate data in outcome prediction is both promising and
Roman Hornung, Marvin N. Wright
doaj +1 more source
Prediction of unconventional protein secretion by exosomes
BMC Bioinformatics, 2021 Motivation In eukaryotes, proteins targeted for secretion contain a signal peptide, which allows them to proceed through the conventional ER/Golgi-dependent pathway.
Alvaro Ras-Carmona +2 more
doaj +1 more source
Estimation of Vegetation Indices With Random Kernel Forests
IEEE Access, 2023 Vegetation indexes help perform precision farming because they provide useful information regarding moisture, nutrient content, and crop health. Primary sources of those indexes are satellites and unmanned aerial vehicles equipped with expensive ...
Dmitry A. Devyatkin
doaj +1 more source