Results 61 to 70 of about 2,068,010 (305)
Two Applications of Random Spanning Forests [PDF]
, 2017 International audienceWe use random spanning forests to find, for any Markov process on a finite set of size n and any positive integer m
Gaudillière, Alexandre +5 more
core +1 more source
Molecular Oncology, EarlyView.This study shows that lung adenocarcinomas exploit developmental branching morphogenesis to acquire a therapy resistant basal‐like tumour cell state. This process was found to be regulated by combined TP53 loss‐of‐function and type‐I interferon signalling, identifying a novel axis for biomarker and therapeutic target discovery.
Kamila J Bienkowska +13 more
wiley +1 more source
Design of Probabilistic Random Forests with Applications to Anticancer Drug Sensitivity Prediction [PDF]
, 2015 Random forests consisting of an ensemble of regression trees with equal weights are frequently used for design of predictive models. In this article, we consider an extension of the methodology by representing the regression trees in the form of ...
Ranadip Pal +8 more
core +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
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
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
CoRR, 2015 Random forests are a type of ensemble method which makes predictions by combining the results of several independent trees. However, the theory of random forests has long been outpaced by their application. In this paper, we propose a novel random forests algorithm based on cooperative game theory.
Jianyuan Sun +3 more
openaire +3 more sources
Molecular Oncology, EarlyView.This study integrates publicly available transcriptomic datasets to identify molecular signatures associated with response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer. By analyzing a combination of multiple cohorts with bioinformatics approaches, we reveal biological pathways and immune‐related features that may improve ...
Aleksandra Stanojevic +10 more
wiley +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
Molecular Oncology, EarlyView.Circulating microRNA profiles distinguish nonmalignant Lynch syndrome from cancer‐associated Lynch syndrome, sporadic colorectal cancer, and healthy controls. Age‐stratified analysis reveals disease‐specific regulatory states, and a six‐miRNA panel shows concordant separation in an independent colorectal tissue cohort.
Ramadhani Salum Chambuso +5 more
wiley +1 more source