Results 71 to 80 of about 5,516,106 (266)
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
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
EPJ Data Science, 2023 Temperature-economic growth relationships are computed to quantify the impact of climate change on the economy. However, model performance and differences of predictions among research complicate the use of climate econometric estimation.
Yu Song +11 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
A Novel Consistent Random Forest Framework: Bernoulli Random Forests [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2018 Random forests (RFs) are recognized as one type of ensemble learning method and are effective for the most classification and regression tasks. Despite their impressive empirical performance, the theory of RFs has yet been fully proved. Several theoretically guaranteed RF variants have been presented, but their poor practical performance has been ...
Yisen Wang +4 more
openaire +3 more sources
Random forests for high-dimensional longitudinal data [PDF]
Statistical Methods in Medical Research, 2019 Random forests are one of the state-of-the-art supervised machine learning methods and achieve good performance in high-dimensional settings where p, the number of predictors, is much larger than n, the number of observations.
L. Capitaine, R. Genuer, R. Thi'ebaut
semanticscholar +1 more source
On PAC-Bayesian Bounds for Random Forests
, 2019 Existing guarantees in terms of rigorous upper bounds on the generalization error for the original random forest algorithm, one of the most frequently used machine learning methods, are unsatisfying.
Igel, Christian +2 more
core +1 more source
Random Survival Forests Incorporated by the Nadaraya-Watson Regression
Информатика и автоматизация, 2022 An attention-based random survival forest (Att-RSF) is presented in the paper. The first main idea behind this model is to adapt the Nadaraya-Watson kernel regression to the random survival forest so that the regression weights or kernels can be regarded
Lev Utkin, Andrei Konstantinov
doaj +1 more source
, 2018 . Low-cost sensing strategies hold the promise of denser air quality monitoring networks, which could significantly improve our understanding of personal air pollution exposure.
Naomi Zimmerman +7 more
semanticscholar +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