Results 81 to 90 of about 15,644,966 (296)
The Shewanella oneidensis Fic enzyme SoFic targets the switch‐I region of EF‐Tu for AMPylation
FEBS Letters, EarlyView.Fic enzymes mediate diverse post‐translational modifications across all domains of life, including AMPylation. Prokaryotic EF‐Tu can be AMPylated and deAMPylated by the conserved Fic enzyme SoFic. Structural and biochemical approaches were used to characterize the effect of AMPylation on EF‐Tu, SoFic's enzymatic activities, and the enzyme‐target ...
Svenja Runge +6 more
wiley +1 more source
A divisive hierarchical k-means based algorithm for image segmentation
, 2010 In this paper we present a divisive hierarchical method for the analysis and segmentation of visual images. The proposed method is based on the use of the k-means method embedded in a recursive algorithm to obtain a clustering at each node of the ...
Gómez González, Daniel +6 more
core +1 more source
Molecular Oncology, EarlyView.Matched spatial transcriptomics and single‐nuclei RNA‐seq were generated for anaplastic and BRAFV600E papillary thyroid cancers revealing generic and tumor‐specific states occurring in cancer cells and in the tumor microenvironment. In this context, cancer dedifferentiation mirrored organoid maturation through ordered thyroid marker gain/loss ...
Adrien Tourneur +11 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
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 +3 more sources
Implementation of K-means Clustering Algorithm using Java [PDF]
, 2011 Emergence of modern techniques for scientific data collection has resulted in large scale accumulation of data pertaining to diverse fields. Conventional database querying methods are inadequate to extract useful information from huge data analysis ...
Prof. S.China Venkateswarlu +2 more
core
Molecular Oncology, EarlyView.This study integrates publicly available transcriptomic datasets to identify molecular signatures associated with response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer. By analyzing a combination of multiple cohorts with bioinformatics approaches, we reveal biological pathways and immune‐related features that may improve ...
Aleksandra Stanojevic +10 more
wiley +1 more source
Improved K-Means Algorithm for Nearby Target Localization
IEEE AccessIn a multi-source localization system, direction of arrival (DOA) estimation of angles always suffers from errors due to noise interference, sensor position inaccuracies, and other factors.
Zongwen Yuan +3 more
doaj +1 more source
An Improved NSGA-III Algorithm Using Genetic K-Means Clustering Algorithm
IEEE Access, 2019 The non-dominated sorting genetic algorithm III (NSGA-III) has recently been proposed to solve many-objective optimization problems (MaOPs). While this algorithm achieves good diversity, its convergence is unsatisfactory.
Qingguo Liu +3 more
doaj +1 more source
Algorithms for finding $k$ in $k$-means
CoRR, 2020 38 ...
Chiranjib Bhattacharyya +2 more
openaire +2 more sources