Results 61 to 70 of about 360,197 (329)
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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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
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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
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Knowledge-Based SystemsThe Random Forest (RF) classifier is often claimed to be relatively well calibrated when compared with other machine learning methods. Moreover, the existing literature suggests that traditional calibration methods, such as isotonic regression, do not substantially enhance the calibration of RF probability estimates unless supplied with extensive ...
Shaker, Mohammad Hossein +1 more
openaire +3 more sources
Molecular Oncology, EarlyView.We developed a cost‐effective methylation‐specific droplet digital PCR multiplex assay containing tissue‐conserved and tumor‐specific methylation markers. The assay can detect circulating tumor DNA with high accuracy in patients with localized and metastatic colorectal cancer.
Luisa Matos do Canto +8 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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Training Big Random Forests with Little Resources
, 2018 Without access to large compute clusters, building random forests on large datasets is still a challenging problem. This is, in particular, the case if fully-grown trees are desired.
Beazley David M. +4 more
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Tumor mutational burden as a determinant of metastatic dissemination patterns
Molecular Oncology, EarlyView.This study performed a comprehensive analysis of genomic data to elucidate whether metastasis in certain organs share genetic characteristics regardless of cancer type. No robust mutational patterns were identified across different metastatic locations and cancer types.
Eduardo Candeal +4 more
wiley +1 more source
Frontiers in Plant Science, 2021 Forests in different disturbance regimes provide diverse microhabitats for species growth. However, whether the species distribution of wood plant is random or follows ecological specialization among forests in different disturbance regimes remains to be
Jingjing Xi +11 more
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Analysis of a Random Forests Model [PDF]
, 2012 Random forests are a scheme proposed by Leo Breiman in the 2000's for building a predictor ensemble with a set of decision trees that grow in randomly selected subspaces of data.
Bin Yu, Gérard Biau Lsta
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