Results 221 to 230 of about 13,094,494 (259)
Molecular Oncology, EarlyView.LUNAR is a liver‐specific long noncoding RNA (lncRNA) that is highly expressed in normal liver but becomes epigenetically silenced in hepatocellular carcinoma through promoter hypermethylation. Loss of LUNAR is associated with NOTCH activation, epithelial–mesenchymal transition, and metastasis, whereas restoring LUNAR restrains metastatic progression ...
Se Ha Jang +9 more
wiley +1 more source
CEACAM1 participation in breast cancer progression
Molecular Oncology, EarlyView.In invasive breast cancer (BC), CEACAM1 shifts from an apical to a uniform membranous/cytoplasmic pattern, or is lost, as tumors dedifferentiate, inversely tracking the Ki‐67 proliferative index. In MCF‐7 cells, only CEACAM1‐4L suppresses proliferation, repressing cell cycle and growth factor genes.
Mykola Lyndin +3 more
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Pharmacological chromatin remodeling enhances response to estrogen therapy in ER+ breast cancer
Molecular Oncology, EarlyView.Estrogen therapy elicits clinical benefit in ~ 30% of patients with endocrine‐resistant estrogen receptor (ER)‐positive breast cancer. Based on findings that ER transcriptional activation underlies response to estrogen therapy, we tested the effects of epigenetic dysregulation via pharmacological inhibition of histone deacetylases (HDACi).
Anneka L. Johnson Thomas +16 more
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PANoptosis in the pathogenesis of myelodysplastic syndromes
Molecular Oncology, EarlyView.PANoptosis, a combination of three types of programmed cell death, is mediated by a large protein complex called a PANoptosome. In healthy bone marrow hematopoietic cells, PANoptosis is restricted by inhibitory signaling. In MDS, bone marrow cells become sensitive to the PANoptotic stimuli due to the aberrant inactivation of inhibitory signaling or ...
Rohit Thalla +4 more
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Molecular Oncology, EarlyView.Tumour heterogeneity and clonal evolution of metastatic salivary gland cancer were evaluated in two patients with adenoid carcinoma and one patient with myoepithelial carcinoma. Radiology‐guided autopsy enabled multi‐region sampling (total samples n = 149), followed by whole‐genome sequencing and phylogenetic reconstruction (17 tumour samples, 4–7 per ...
Gerben Lassche +10 more
wiley +1 more source
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2013 IEEE Conference on Computer Vision and Pattern Recognition, 2013
A fundamental limitation of quantization techniques like the k-means clustering algorithm is the storage and run-time cost associated with the large numbers of clusters required to keep quantization errors small and model fidelity high. We develop new models with a compositional parameterization of cluster centers, so representational capacity ...
Mohammad Norouzi 0002, David J. Fleet
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A fundamental limitation of quantization techniques like the k-means clustering algorithm is the storage and run-time cost associated with the large numbers of clusters required to keep quantization errors small and model fidelity high. We develop new models with a compositional parameterization of cluster centers, so representational capacity ...
Mohammad Norouzi 0002, David J. Fleet
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K*-Means: An Effective and Efficient K-Means Clustering Algorithm
2016 IEEE International Conferences on Big Data and Cloud Computing (BDCloud), Social Computing and Networking (SocialCom), Sustainable Computing and Communications (SustainCom) (BDCloud-SocialCom-SustainCom), 2016K-means is a widely used clustering algorithm in field of data mining across different disciplines in the past fifty years. However, k-means heavily depends on the position of initial centers, and the chosen starting centers randomly may lead to poor quality of clustering.
Jianpeng Qi +3 more
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2017
K-Means is a very common method of unsupervised learning in data mining. It is introduced by Steinhaus in 1956. As time flies, many other enhanced methods of k-Means have been introduced and applied. One of the significant characteristic of k-Means is randomize.
Chen-Ling Tai, Chen-Shu Wang
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K-Means is a very common method of unsupervised learning in data mining. It is introduced by Steinhaus in 1956. As time flies, many other enhanced methods of k-Means have been introduced and applied. One of the significant characteristic of k-Means is randomize.
Chen-Ling Tai, Chen-Shu Wang
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PERFORMANCE COMPARISON OF K-MEANS, PARALLEL K-MEANS AND K-MEANS++
K-means clustering is a fundamental unsupervised machine learning technique widely applied in various domains such as data analysis, pattern recognition, and clustering-based tasks. However, its efficiency and scalability can be challenged, particularly when dealing with large-scale datasets and complex data structures.Aliguliyev, Ramiz, Shalala F. Tahirzada
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A Modified K-Means Algorithm - Two-Layer K-Means Algorithm
2014 Tenth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 2014In this paper, a modified K-means algorithm is proposed to categorize a set of data. K-means algorithm is a simple and easy clustering method which can efficiently classify a large number of continuous numerical data of high-dimensions. Moreover, the data in each cluster are similar to one another.
Chen-Chung Liu +3 more
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