Results 41 to 50 of about 415,661 (284)

Circular RNA expression landscapes in myelodysplastic neoplasms: Associations with mutational signatures and disease progression

open access: yesMolecular Oncology, EarlyView.
In this explorative study, the abundance of circular RNA molecules in bone marrow stem cells was found to be elevated in patients with high‐risk myelodysplastic neoplasms, and to be associated with an increased risk of progression to acute myeloid leukemia.
Eileen Wedge   +17 more
wiley   +1 more source

Combination of DEA and PCA for Full Ranking of Decision Making Units [PDF]

open access: yesمدیریت صنعتی, 2009
This paper presents a combination of Data Envelopment Analysis (DEA) and Principal Component Analysis (PCA) to reduce the dimensionality of data set. DEA is known as effective tool for assessment and benchmarking.
Mojtaba Khazaei, Hamid Reza Izadbakhsh
doaj  

Colorectal cancer‐derived FGF19 is a metabolically active serum biomarker that exerts enteroendocrine effects on mouse liver

open access: yesMolecular Oncology, EarlyView.
Meta‐transcriptome analysis identified FGF19 as a peptide enteroendocrine hormone associated with colorectal cancer prognosis. In vivo xenograft models showed release of FGF19 into the blood at levels that correlated with tumor volumes. Tumoral‐FGF19 altered murine liver metabolism through FGFR4, thereby reducing bile acid synthesis and increasing ...
Jordan M. Beardsley   +5 more
wiley   +1 more source

Community awareness about disaster preparedness: Principal component analysis (PCA)

open access: yesMajmaah Journal of Health Sciences, 2021
Background and Aim: Responsibility for disaster preparedness is not limit- ed to healthcare institutions and healthcare providers; communities must also be involved. Knowledge of community members’ awareness of disaster preparedness will enhance and strengthen a community’s resilience to disaster.
Abdullelah Thobaity, Modi Moteri
openaire   +1 more source

The principal independent components of images [PDF]

open access: yes, 2010
This paper proposes a new approach for the encoding of images by only a few important components. Classically, this is done by the Principal Component Analysis (PCA).
Arlt, Björn, Brause, Rüdiger W.
core  

Identification of serum protein biomarkers for pre‐cancerous lesions associated with pancreatic ductal adenocarcinoma

open access: yesMolecular Oncology, EarlyView.
This work identified serum proteins associated with pancreatic epithelial neoplasms (PanINs) and early‐stage PDAC. Proteomics screens assessed genetically engineered mice with abundant PanINs, KPC mice (Lox‐STOP‐Lox‐KrasG12D/+ Lox‐STOP‐Lox‐Trp53R172H/+ Pdx1‐Cre) before PDAC development and also early‐stage PDAC patients (n = 31), compared to benign ...
Hannah Mearns   +10 more
wiley   +1 more source

Determining Principal Component Cardinality through the Principle of Minimum Description Length

open access: yes, 2019
PCA (Principal Component Analysis) and its variants areubiquitous techniques for matrix dimension reduction and reduced-dimensionlatent-factor extraction. One significant challenge in using PCA, is thechoice of the number of principal components.
A Blumer   +18 more
core   +1 more source

Subtype‐specific enhancer RNAs define transcriptional regulators and prognosis in breast cancers

open access: yesMolecular Oncology, EarlyView.
This study employed machine learning methodologies to perform the subtype‐specific classification of RNA‐seq data sets, which are mapped on enhancers from TCGA‐derived breast cancer patients. Their integration with gene expression (referred to as ProxCReAM eRNAs) and chromatin accessibility profiles has the potential to identify lineage‐specific and ...
Aamena Y. Patel   +6 more
wiley   +1 more source

Network divergence analysis identifies adaptive gene modules and two orthogonal vulnerability axes in pancreatic cancer

open access: yesMolecular Oncology, EarlyView.
Tumors contain diverse cellular states whose behavior is shaped by context‐dependent gene coordination. By comparing gene–gene relationships across biological contexts, we identify adaptive transcriptional modules that reorganize into distinct vulnerability axes.
Brian Nelson   +9 more
wiley   +1 more source

Robust PCA as Bilinear Decomposition with Outlier-Sparsity Regularization [PDF]

open access: yes, 2011
Principal component analysis (PCA) is widely used for dimensionality reduction, with well-documented merits in various applications involving high-dimensional data, including computer vision, preference measurement, and bioinformatics.
Giannakis, Georgios B., Mateos, Gonzalo
core   +1 more source

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