Results 71 to 80 of about 1,986,530 (277)
Molecular Oncology, EarlyView.This study investigated how PYCR1 inhibition in bone marrow stromal cells (BMSCs) indirectly affects multiple myeloma (MM) cell metabolism and viability. Culturing MM cells in conditioned medium from PYCR1‐silenced BMSCs impaired oxidative phosphorylation and increased sensitivity to bortezomib.
Inge Oudaert +13 more
wiley +1 more source
Molecular Oncology, EarlyView.Adaptaquin selectively kills glioma stem cells while sparing differentiated brain cells. Transcriptomic and proteomic analyses show Adaptaquin disrupts iron and cholesterol homeostasis, with iron chelation amplifying cytotoxicity via cholesterol depletion, mitochondrial dysfunction, and elevated reactive oxygen species.
Adrien M. Vaquié +16 more
wiley +1 more source
Attainment of K-Means Algorithm using Hellinger distance [PDF]
Ovidius University Annals: Economic Sciences Series, 2017 In this article in the first part I will begin with an introduction to unsupervised learning methods, focusing on the K-Means clustering algorithm, which is achieved with the help of the Euclidian distance.
Stancu Ana-Maria Ramona +2 more
doaj
Randomized Dimensionality Reduction for k-means Clustering [PDF]
, 2013 We study the topic of dimensionality reduction for $k$-means clustering. Dimensionality reduction encompasses the union of two approaches: \emph{feature selection} and \emph{feature extraction}.
Boutsidis, Christos +3 more
core
An efficient k′-means clustering algorithm
Pattern Recognition Letters, 2008 This paper introduces k'-means algorithm that performs correct clustering without pre-assigning the exact number of clusters. This is achieved by minimizing a suggested cost-function. The cost-function extends the mean-square-error cost-function of k-means. The algorithm consists of two separate steps.
openaire +2 more sources
K+ Means : An Enhancement Over K-Means Clustering Algorithm
, 2017 Authors: Co-author's name added Section 3: Step (a) and (b) of K+Means algorithm are merged for simplicity. Section 3.1: K+ Means algorithm complexity rectified.
Kolay, Srikanta +2 more
openaire +2 more sources
Aggressive prostate cancer is associated with pericyte dysfunction
Molecular Oncology, EarlyView.Tumor‐produced TGF‐β drives pericyte dysfunction in prostate cancer. This dysfunction is characterized by downregulation of some canonical pericyte markers (i.e., DES, CSPG4, and ACTA2) while maintaining the expression of others (i.e., PDGFRB, NOTCH3, and RGS5).
Anabel Martinez‐Romero +11 more
wiley +1 more source
Privacy Preserving Multi-Server k-means Computation over Horizontally Partitioned Data
, 2019 The k-means clustering is one of the most popular clustering algorithms in data mining. Recently a lot of research has been concentrated on the algorithm when the dataset is divided into multiple parties or when the dataset is too large to be handled by ...
A Likas +13 more
core +1 more source
Molecular Oncology, EarlyView.Detection of extrachromosomal circular DNA (eccDNA) in plasma samples from EGFR‐mutated non‐small cell lung cancer patients. Plasma was collected before and during treatment with the EGFR‐tyrosine kinase inhibitor osimertinib. Plasma eccDNA was detected in all cancer samples, and the presence of the EGFR gene on eccDNA serves as a potential biomarker ...
Simone Stensgaard +5 more
wiley +1 more source
A K-means Algorithm Based On Feature Weighting
MATEC Web of Conferences, 2018 Cluster analysis is a statistical analysis technique that divides the research objects into relatively homogeneous groups. The core of cluster analysis is to find useful clusters of objects.
Xu Yan +4 more
doaj +1 more source