Results 71 to 80 of about 67,103 (261)

Using cell‐free RNA to identify B‐ and T‐cell clonality for diagnosis and monitoring of B‐ and T‐cell neoplasms

FEBS Open Bio, EarlyView.
Using peripheral blood for determining B‐cell or T‐cell clonality is more reliable when we use cell‐free RNA (cfRNA) because cells release blood significantly more RNA than DNA. Next‐generation sequencing (NGS) of cfRNA allows us to evaluate fragment cfRNA and evaluate clonality reliably without the need for prior determination of the specific dominant
Adam Albitar, Sally Agersborg, Ahmed Charifa, Pooja Phull, Noa Biran, David Vesole, Harsh Parmar, Andrew Pecora, Andrew Ip, Andre Goy, David Siegel, Maher Albitar   +11 more
wiley   +1 more source

Elevated Connectivity During Language Processing Is Associated With Cognitive Performance in SeLECTS

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Self‐Limited Epilepsy with Centrotemporal Spikes (SeLECTS) is associated with language impairments despite seizures originating in the motor cortex, suggesting aberrant cross‐network interactions. Here we tested whether functional connectivity in SeLECTS during language tasks predicts language performance.
Wendy Qi, Katharine Lee, Kerry C. Nix, Miguel Menchaca, Xiwei Shé, Lorelei Santa Maria, Wei Wu, Zihuai He, Fiona M. Baumer   +8 more
wiley   +1 more source

Random KNN feature selection - a fast and stable alternative to Random Forests

BMC Bioinformatics, 2011
Background Successfully modeling high-dimensional data involving thousands of variables is challenging. This is especially true for gene expression profiling experiments, given the large number of genes involved and the small number of samples available.
Li Shengqiao, Harner E James, Adjeroh Donald A   +2 more
doaj   +1 more source

Autoencoding Random Forests

CoRR
We propose a principled method for autoencoding with random forests. Our strategy builds on foundational results from nonparametric statistics and spectral graph theory to learn a low-dimensional embedding of the model that optimally represents relationships in the data. We provide exact and approximate solutions to the decoding problem via constrained
Binh Duc Vu, Jan Kapar, Marvin N. Wright, David S. Watson   +3 more
openaire   +2 more sources

Denoising random forests

CoRR, 2017
This paper proposes a novel type of random forests called a denoising random forests that are robust against noises contained in test samples. Such noise-corrupted samples cause serious damage to the estimation performances of random forests, since unexpected child nodes are often selected and the leaf nodes that the input sample reaches are sometimes ...
Masaya Hibino, Akisato Kimura, Takayoshi Yamashita, Yuji Yamauchi, Hironobu Fujiyoshi   +4 more
openaire   +2 more sources

White Matter Hyperintensity Burden and Short‐Interval Change Associated With Sleep Apnoea in the UK Biobank

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Background and Purpose White matter hyperintensities (WMH) are a core neuroimaging marker of cerebral small vessel disease (CSVD). Sleep apnoea (SA) is a recognized vascular risk factor, but its associations with regional WMH burden, short‐interval WMH change and cognitive performance in population‐based cohorts remain incompletely defined. We
Peng Cheng, Diego Z. Carvalho, Shahid Karim, Anwar Chahal, Virend K. Somers   +4 more
wiley   +1 more source

Risk of Non‐Arteritic Anterior Ischemic Optic Neuropathy in Idiopathic Intracranial Hypertension Patients Treated with GLP‐1 Receptor Agonists

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Introduction Glucagon‐like peptide‐1 receptor agonists (GLP‐1 RAs) have demonstrated significant weight‐reducing effects and may offer benefits in idiopathic intracranial hypertension (IIH); however, recent concerns about the risk of non‐arteritic anterior ischemic optic neuropathy (NAION) have emerged.
Faisal A. Al‐Harbi, Mohanad A. Alkuwaiti, Yazeed B. Alaql, Ahmed K. Alsaif, Ahmed A. Alessa, Meshari Ayed Alharbi, Mohammed Alfalah, Saud A. Alnaaim, Sajjad M. AlHaddad, Ahmed Y. Azzam   +9 more
wiley   +1 more source

On the overestimation of random forest's out-of-bag error. [PDF]

PLoS ONE, 2018
The ensemble method random forests has become a popular classification tool in bioinformatics and related fields. The out-of-bag error is an error estimation technique often used to evaluate the accuracy of a random forest and to select appropriate ...
Silke Janitza, Roman Hornung
doaj   +1 more source

Subtractive random forests

Latin American Journal of Probability and Mathematical Statistics
Motivated by online recommendation systems, we study a family of random forests. The vertices of the forest are labeled by integers. Each non-positive integer $i\le 0$ is the root of a tree. Vertices labeled by positive integers $n \ge 1$ are attached sequentially such that the parent of vertex $n$ is $n-Z_n$, where the $Z_n$ are i.i.d.\ random ...
Broutin, Nicolas, Devroye, Luc, Lugosi, Gábor, Oliveira, Roberto Imbuzeiro   +3 more
openaire   +3 more sources

The Macroeconomy as a Random Forest [PDF]

SSRN Electronic Journal, 2020
SummaryI develop the macroeconomic random forest (MRF), an algorithm adapting the canonical machine learning (ML) tool, to flexibly model evolving parameters in a linear macro equation. Its main output, generalized time‐varying parameters (GTVPs), is a versatile device nesting many popular nonlinearities (threshold/switching, smooth transition, and ...
openaire   +2 more sources