Results 71 to 80 of about 866,547 (282)
Liquid biopsy epigenetics: establishing a molecular profile based on cell‐free DNA
Molecular Oncology, EarlyView.Cell‐free DNA (cfDNA) fragments in plasma from cancer patients carry epigenetic signatures reflecting their cells of origin. These epigenetic features include DNA methylation, nucleosome modifications, and variations in fragmentation. This review describes the biological properties of each feature and explores optimal strategies for harnessing cfDNA ...
Christoffer Trier Maansson +2 more
wiley +1 more source
Cytoplasmic p21 promotes stemness of colon cancer cells via activation of the NFκB pathway
Molecular Oncology, EarlyView.Cytoplasmic p21 promotes colorectal cancer stem cell (CSC) features by destabilizing the NFκB–IκB complex, activating NFκB signaling, and upregulating BCL‐xL and COX2. In contrast to nuclear p21, cytoplasmic p21 enhances spheroid formation and stemness transcription factor CD133.
Arnatchai Maiuthed +10 more
wiley +1 more source
A physical model inspired density peak clustering.
PLoS ONE, 2020 Clustering is an important technology of data mining, which plays a vital role in bioscience, social network and network analysis. As a clustering algorithm based on density and distance, density peak clustering is extensively used to solve practical ...
Hui Zhuang +3 more
doaj +1 more source
Molecular 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
Fast k-means algorithm clustering
, 2011 k-means has recently been recognized as one of the best algorithms for clustering unsupervised data. Since k-means depends mainly on distance calculation between all data points and the centers, the time cost will be high when the size of the dataset is ...
Kecman, Vojislav +4 more
core +1 more source
Molecular Oncology, EarlyView.Generation of two normal and tumour (cancerous) paired human cell lines using an established tissue culture technique and their characterisation is described. Cell lines were characterised at cellular, protein, chromosome and gene expression levels and for HPV status.
Simon Broad +12 more
wiley +1 more source
Color Image Segmentation Algorithm Combining SLIC with Improved Affinity Propagation Clustering [PDF]
Jisuanji gongcheng, 2018 When Adjacent Propagation(AP) clustering algorithm is used to segment color images,the similarity matrix has the problems of large scale,long clustering time,and high space complexity.Therefore,a new color image segmentation algorithm is proposed.A ...
CHENG Xianguo,WANG Mingjun
doaj +1 more source
Image segmentation using fuzzy clustering incorporating spatial information [PDF]
, 2004 Effective image segmentation cannot be achieved for a fuzzy clustering algorithm based on using only pixel intensity, pixel locations or a combination of the two.
Ali, Ameer +2 more
core
Molecular Oncology, EarlyView.We developed and validated a DNA methylation–based biomarker panel to distinguish pleural mesothelioma from other pleural conditions. Using the IMPRESS technology, we translated this panel into a clinically applicable assay. The resulting two classifier models demonstrated excellent performance, achieving high AUC values and strong diagnostic accuracy.
Janah Vandenhoeck +12 more
wiley +1 more source
, 2012 Factorial clustering methods have been developed in recent years thanks to the improving of computational power. These methods perform a linear transformation of data and a clustering on transformed data optimizing a common criterion.
A. Ben-Israel +6 more
core +2 more sources