Results 51 to 60 of about 334,720 (282)
Enhancing random forests performance in microarray data classification [PDF]
, 2013 Random forests are receiving increasing attention for classification of microarray datasets. We evaluate the effects of a feature selection process on the performance of a random forest classifier as well as on the choice of two critical parameters, i.e.
DESSI, NICOLETTA +2 more
core +1 more source
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
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
, 2012 We consider unimodular random rooted trees (URTs) and invariant forests in Cayley graphs. We show that URTs of bounded degree are the same as the law of the component of the root in an invariant percolation on a regular tree.
Aldous +5 more
core +1 more source
, 2019 Space partitioning methods such as random forests and the Mondrian process are powerful machine learning methods for multi-dimensional and relational data, and are based on recursively cutting a domain. The flexibility of these methods is often limited by the requirement that the cuts be axis aligned.
Ge, S +4 more
openaire +3 more sources
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
A comparison among interpretative proposals for Random Forests
Machine Learning with Applications, 2021 The growing success of Machine Learning (ML) is making significant improvements to predictive models, facilitating their integration in various application fields.
Massimo Aria +2 more
doaj +1 more source
Variable selection with Random Forests for missing data [PDF]
, 2013 Variable selection has been suggested for Random Forests to improve their efficiency of data prediction and interpretation. However, its basic element, i.e.
Hapfelmeier, Alexander, Ulm, Kurt
core +1 more source
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
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