Results 71 to 80 of about 2,068,010 (305)
Interactive Random Forests Plots [PDF]
, 2012 Random Forests is a useful data mining tool that is quite popular in finding variable importance. However, many people don’t make use of the Random Forests results in interactive graphs.
Quach, Anna T.
core +1 more source
Assessing Predictability of Marine Heatwaves With Random Forests
Geophysical Research Letters, 2022 Marine heatwaves (MHWs) have increased in frequency and duration over the last century and are expected to intensify in the future. Such events have become an increasing threat for marine ecosystems and subsequently the economies and populations that ...
K. Giamalaki +2 more
doaj +1 more source
Targeting the EpCAM‐AXL axis to overcome drug resistance in lung cancer
Molecular Oncology, EarlyView.Lung cancer cells often evade therapy by hijacking signaling pathways. We reveal that cleaved EpCAM (sEpCAM) stabilizes the oncogenic protein AXL, driving NF‐κB and STAT3‐mediated chemoresistance. This EpCAM‐AXL axis identifies a high‐risk patient subset with poor prognosis.
Alexa Guerrero‐Alba +5 more
wiley +1 more source
Prediction of glycosylation sites using random forests
BMC Bioinformatics, 2008 Background Post translational modifications (PTMs) occur in the vast majority of proteins and are essential for function. Prediction of the sequence location of PTMs enhances the functional characterisation of proteins.
Hirst Jonathan D, Hamby Stephen E
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IEICE Transactions on Information and Systems, 2015 Machine learning is used in various fields and demand for implementations is increasing. Within machine learning, a Random Forest is a multi-class classifier with high-performance classification, achieved using bagging and feature selection, and is capable of high-speed training and classification. However, as a type of ensemble learning, Random Forest
MISHINA, Yohei +4 more
openaire +3 more sources
Directed evolution of enzymes at the crossroads of tradition and innovation
FEBS Open Bio, EarlyView.An iterative cycle of data‐driven enzyme optimization comprising four stages: genetic diversification of a template enzyme, expression of protein variants, high‐throughput evaluation, and machine‐learning‐guided redesign of the next variant library.
Maria Tomkova +2 more
wiley +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
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CoRRRandom forest (RF) stands out as a highly favored machine learning approach for classification problems. The effectiveness of RF hinges on two key factors: the accuracy of individual trees and the diversity among them. In this study, we introduce a novel approach called heterogeneous RF (HRF), designed to enhance tree diversity in a meaningful way ...
Ye-eun Kim +2 more
openaire +3 more sources
Journal of Thoracic Oncology, 2011 In the article by Chen et al,1 the authors used Random Survival Forests (RSF) as part of their approach for analyzing the data. In this note, we will explain RSF in a nontechnical way; precise details of the RSF method are described in the article by Ishwaran et al.2 RSF are an adaptation of Random Forests (RF)3 designed to be used for survival data ...
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Accurate and noninvasive prostate cancer detection using plasma‐derived extracellular vesicle RNA
FEBS Open Bio, EarlyView.Plasma extracellular vesicles were captured with WGA‐conjugated magnetic beads and profiled for RNA biomarkers. A three‐RNA panel (NM_024955, NR_047469, and NR_002564) distinguished prostate cancer from healthy controls and benign prostatic hyperplasia, supporting a simple, noninvasive approach to improve prostate cancer detection.
Hanping Wei, Haoran Wu, Wei Feng
wiley +1 more source