Results 61 to 70 of about 811,918 (310)

Class IIa HDACs forced degradation allows resensitization of oxaliplatin‐resistant FBXW7‐mutated colorectal cancer

open access: yesMolecular Oncology, EarlyView.
HDAC4 is degraded by the E3 ligase FBXW7. In colorectal cancer, FBXW7 mutations prevent HDAC4 degradation, leading to oxaliplatin resistance. Forced degradation of HDAC4 using a PROTAC compound restores drug sensitivity by resetting the super‐enhancer landscape, reprogramming the epigenetic state of FBXW7‐mutated cells to resemble oxaliplatin ...
Vanessa Tolotto   +13 more
wiley   +1 more source

Pengenalan Wajah Menggunakan Metode Principal Component Analysis (PCA) dan Canberra Distance [PDF]

open access: yes, 2017
Wajah merupakan salah satu karakteristik biometrik yang digunakan untuk mengenali seseorang selain karakteristik yang lain seperti ucapan; sidik jari; retina; dll.
Rosyani, P. (Perani)
core   +2 more sources

In vitro models of cancer‐associated fibroblast heterogeneity uncover subtype‐specific effects of CRISPR perturbations

open access: yesMolecular Oncology, EarlyView.
Development of therapies targeting cancer‐associated fibroblasts (CAFs) necessitates preclinical model systems that faithfully represent CAF–tumor biology. We established an in vitro coculture system of patient‐derived pancreatic CAFs and tumor cell lines and demonstrated its recapitulation of primary CAF–tumor biology with single‐cell transcriptomics ...
Elysia Saputra   +10 more
wiley   +1 more source

The Active Nucleus in NGC 4579

open access: yes, 2009
In this work, we present an analysis of a data cube obtained with the instrument IFU/GMOS Gemini North telescope centered on the nuclear region of the LINER galaxy NGC 4579. This galaxy is known to have a type 1 AGN (see Eracleous et al.
Menezes, Roberto B.   +3 more
core   +1 more source

Robust Principal Component Analysis on Graphs [PDF]

open access: yes, 2015
Principal Component Analysis (PCA) is the most widely used tool for linear dimensionality reduction and clustering. Still it is highly sensitive to outliers and does not scale well with respect to the number of data samples.
Bresson, Xavier   +4 more
core   +2 more sources

LINC01116, a hypoxia‐lncRNA marker of pathological lymphangiogenesis and poor prognosis in lung adenocarcinoma

open access: yesMolecular Oncology, EarlyView.
The LINC01116 long noncoding RNA is induced by hypoxia and associated with poor prognosis and high recurrence rates in two cohorts of lung adenocarcinoma patients. Here, we demonstrate that besides its expression in cancer cells, LINC01116 is markedly expressed in lymphatic endothelial cells of the tumor stroma in which it participates in hypoxia ...
Marine Gautier‐Isola   +12 more
wiley   +1 more source

Structural Analysis of Network Traffic Matrix via Relaxed Principal Component Pursuit

open access: yes, 2012
The network traffic matrix is widely used in network operation and management. It is therefore of crucial importance to analyze the components and the structure of the network traffic matrix, for which several mathematical approaches such as Principal ...
Dong, Xiaowen   +4 more
core   +1 more source

ATF4‐mediated stress response as a therapeutic vulnerability in chordoma

open access: yesMolecular Oncology, EarlyView.
We screened 5 chordoma cell lines against 100+ inhibitors of epigenetic and metabolic pathways and kinases and identified halofuginone, a tRNA synthetase inhibitor. Mechanistically halofuginone induces an integrated stress response, with eIF2alpha phosphorylation, activation of ATF4 and its target genes CHOP, ASNS, INHBE leading to cell death ...
Lucia Cottone   +11 more
wiley   +1 more source

Algorithms for Projection - Pursuit robust principal component analysis. [PDF]

open access: yes
The results of a standard principal component analysis (PCA) can be affected by the presence of outliers. Hence robust alternatives to PCA are needed. One of the most appealing robust methods for principal component analysis uses the Projection-Pursuit ...
Croux, Christophe   +2 more
core   +3 more sources

Face Detection using Principal Component Analysis (PCA)

open access: yesInternational Journal of Computer Applications, 2014
Face Detection makes it possible to use the facial images of a person to authenticate him into secure system, for criminal identification, for passport verification etc. It is done by Principal Component Analysis (PCA).Face images are projected onto a face space that encodes best variation among known face images.
Tarun Metta, Jatin Agarwal, Pushpak Dave
openaire   +1 more source

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