Results 151 to 160 of about 6,969,735 (365)

Comparing self‐reported race and genetic ancestry for identifying potential differentially methylated sites in endometrial cancer: insights from African ancestry proportions using machine learning models

open access: yesMolecular Oncology, EarlyView.
Integrating ancestry, differential methylation analysis, and machine learning, we identified robust epigenetic signature genes (ESGs) and Core‐ESGs in Black and White women with endometrial cancer. Core‐ESGs (namely APOBEC1 and PLEKHG5) methylation levels were significantly associated with survival, with tumors from high African ancestry (THA) showing ...
Huma Asif, J. Julie Kim
wiley   +1 more source

Unsupervised feature selection algorithm based on L 2,p-norm feature reconstruction.

open access: yesPLoS ONE
Traditional subspace feature selection methods typically rely on a fixed distance to compute residuals between the original and feature reconstruction spaces.
Wei Liu   +5 more
doaj   +1 more source

A large‐scale retrospective study in metastatic breast cancer patients using circulating tumour DNA and machine learning to predict treatment outcome and progression‐free survival

open access: yesMolecular Oncology, EarlyView.
There is an unmet need in metastatic breast cancer patients to monitor therapy response in real time. In this study, we show how a noninvasive and affordable strategy based on sequencing of plasma samples with longitudinal tracking of tumour fraction paired with a statistical model provides valuable information on treatment response in advance of the ...
Emma J. Beddowes   +20 more
wiley   +1 more source

Multidimensional OMICs reveal ARID1A orchestrated control of DNA damage, splicing, and cell cycle in normal‐like and malignant urothelial cells

open access: yesMolecular Oncology, EarlyView.
Loss of the frequently mutated chromatin remodeler ARID1A, a subunit of the SWI/SNF cBAF complex, results in less open chromatin, alternative splicing, and the failure to stop cells from progressing through the cell cycle after DNA damage in bladder (cancer) cells. Created in BioRender. Epigenetic regulators, such as the SWI/SNF complex, with important
Rebecca M. Schlösser   +11 more
wiley   +1 more source

Automatic feature selection for unsupervised image segmentation [PDF]

open access: bronze, 2000
Waleed Al‐Nuaimy   +3 more
openalex   +1 more source

Escape from TGF‐β‐induced senescence promotes aggressive hallmarks in epithelial hepatocellular carcinoma cells

open access: yesMolecular Oncology, EarlyView.
Chronic TGF‐β exposure drives epithelial HCC cells from a senescent state to a TGF‐β resistant mesenchymal phenotype. This transition is characterized by the loss of Smad3‐mediated signaling, escape from senescence, enhanced invasiveness and metastatic potential, and upregulation of key resistance modulators such as MARK1 and GRM8, ultimately promoting
Minenur Kalyoncu   +11 more
wiley   +1 more source

ShcD adaptor protein drives invasion of triple negative breast cancer cells by aberrant activation of EGFR signaling

open access: yesMolecular Oncology, EarlyView.
We identified adaptor protein ShcD as upregulated in triple‐negative breast cancer and found its expression to be correlated with reduced patient survival and increased invasion in cell models. Using a proteomic screen, we identified novel ShcD binding partners involved in EGFR signaling pathways.
Hayley R. Lau   +11 more
wiley   +1 more source

Clustering and Selecting Categorical Features

open access: yes, 2013
In data clustering, the problem of selecting the subset of most relevant features from the data has been an active research topic. Feature selection for clustering is a challenging task due to the absence of class labels for guiding the search for relevant features. Most methods proposed for this goal are focused on numerical data.
Silvestre, Cláudia   +2 more
openaire   +4 more sources

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