Results 61 to 70 of about 86,959 (305)

Determining vertical structure of forests in Poland using a semi-automated approach based on ALS data

Ecological Indicators
The vertical structure of the forest is one of the key characteristics of forest management, influencing biodiversity, resource competition, and various ecological processes. Despite its importance, determining the vertical structure of stands over large
Piotr Janiec, Luiza Tymińska-Czabańska, Paweł Hawryło, Michał Woda, Jarosław Socha   +4 more
doaj   +1 more source

Longitudinal genome‐wide aneuploidy measurements in circulating cell‐free DNA to predict lack of benefit from pembrolizumab in patients with metastatic urothelial cancer

Molecular Oncology, EarlyView.
Many patients with urothelial cancer do not benefit from treatment with pembrolizumab, while at risk of severe side effects. Changes in the levels of circulating tumor DNA early during treatment, measured by a simple and affordable assay that can be easily implemented in the clinic, can be used as a prognostic tool to identify these patients.
Youssra Salhi, Stavros Makrodimitris, Sandra van Wilpe, Iris te Paske, Vanja de Weerd, Corine M. Beaufort, Hans M. Westgeest, Jens Voortman, Maureen J. B. Aarts, Maud Rijnders, Astrid A. M. van der Veldt, Ronald de Wit, Niven Mehra, Saskia M. Wilting, Debbie G. J. Robbrecht   +14 more
wiley   +1 more source

Heterogeneous Random Forest

CoRR
Random 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, Seoung Yun Kim, Hyunjoong Kim   +2 more
openaire   +2 more sources

Random Similarity Forests

, 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, Dariusz Brzezinski, Pawel Zawadzki   +2 more
openaire   +2 more sources

Tumor B‐cell infiltration in platinum‐treated advanced muscle‐invasive urothelial carcinoma

Molecular Oncology, EarlyView.
Bladder tumors with higher pretreatment memory B‐cell infiltration were linked to longer survival after cisplatin chemotherapy, but not carboplatin. These tumors also showed more organized immune structures (tertiary lymphoid structures) and a shared pro‐inflammatory B‐cell‐rich community, suggesting that memory B cells may help identify patients most ...
Konrad Stawiski, Júlia Perera‐Bel, Alejo Rodriguez‐Vida, Nuria Juanpere, Jihyun Lee, Daniel E. Michaud, Jennifer L. Guerriero, Kent W. Mouw, Aristotelis Bamias, Filipe L. F. Carvalho, Joaquim Bellmunt   +10 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
doaj   +1 more source

Generalized random forests [PDF]

The Annals of Statistics, 2019
We propose generalized random forests, a method for non-parametric statistical estimation based on random forests (Breiman, 2001) that can be used to fit any quantity of interest identified as the solution to a set of local moment equations. Following the literature on local maximum likelihood estimation, our method considers a weighted set of nearby ...
Athey, Susan, Tibshirani, Julie, Wager, Stefan   +2 more
openaire   +3 more sources

Evidential Random Forests

Expert Systems with Applications, 2023
In machine learning, some models can make uncertain and imprecise predictions, they are called evidential models. These models may also be able to handle imperfect labeling and take into account labels that are richer than the commonly used hard labels, containing uncertainty and imprecision.
Hoarau, Arthur, Martin, Arnaud, Dubois, Jean-Christophe, Le Gall, Yolande   +3 more
openaire   +1 more source

Large‐scale bidirectional arrayed genetic screens identify OXR1 and EMC4 as modifiers of αSynuclein aggregation

FEBS Open Bio, EarlyView.
Activation of the mitochondrial protein OXR1 increases pSyn129 αSynuclein aggregation by lowering ATP levels and altering mitochondrial membrane potential, particularly in response to MSA‐derived fibrils. In contrast, ablation of the ER protein EMC4 enhances autophagic flux and lysosomal clearance, broadly reducing α‐synuclein aggregates.
Sandesh Neupane, Lea Nikolić, Lorenzo Maraio, Thomas Goiran, Nathan Karpilovsky, Stefano Sellitto, Vangelis Bouris, Jiang‐An Yin, Ronald Melki, Edward A Fon, Adriano Aguzzi, Elena De Cecco   +11 more
wiley   +1 more source

Advancing Psychological Research With Random Forests: A Review of Methods, Tools, and Applications

Advances in Methods and Practices in Psychological Science
Contemporary psychological research increasingly involves machine-learning techniques, including random forests, for their capability in analyzing complex, high-dimensional data sets and modeling nonlinear predictive relations.
Yi Feng, Han Du, Jiarui Song, Yina Sun, Yiting Wang, Aedan Joel   +5 more
doaj   +1 more source