Results 91 to 100 of about 15,644,966 (296)
Molecular Oncology, EarlyView.This study identifies ARHGAP5, in addition to the frequently mutated ARHGAP35, as significantly mutated in endometrial cancer. Mutations in both genes co‐occur and are associated with their correlated downregulation. Functional CRISPR studies show that both paralogs regulate similar pathways, including actin cytoskeleton organization.
Mathilde Pinault +12 more
wiley +1 more source
PageRank and The K-Means Clustering Algorithm
CoRR, 2020 We utilize the PageRank vector to generalize the $k$-means clustering algorithm to directed and undirected graphs. We demonstrate that PageRank and other centrality measures can be used in our setting to robustly compute centrality of nodes in a given graph.
Mustafa Hajij, Eyad Said, Robert Todd
openaire +2 more sources
Molecular Oncology, EarlyView.Incomplete microwave ablation (iMWA) of liver cancer triggers a biphasic progression in residual tumors. At Day 3, the microenvironment is characterized by acute inflammatory responses and extracellular matrix (ECM) remodeling. By Day 14, a profound shift occurs toward oncogenic signal transduction and immunosuppression, marked by macrophage ...
Yu Liu +9 more
wiley +1 more source
Pengelompokan Komentar Dataset Sentipol dengan Modified K-Means Clustering
JuTISI (Jurnal Teknik Informatika dan Sistem Informasi), 2020 Clustering is a technique in data mining that groups data sets into similar data clusters. One of the algorithms that is commonly used for clustering is K-Means.
Ruddy Cahyanto +2 more
doaj +1 more source
Improving The Performance Of The K-means Algorithm
CoRR, 2020 The Incremental K-means (IKM), an improved version of K-means (KM), was introduced to improve the clustering quality of KM significantly. However, the speed of IKM is slower than KM. My thesis proposes two algorithms to speed up IKM while remaining the quality of its clustering result approximately.
openaire +3 more sources
K-means+: an autonomous clustering algorithm
, 2003 The traditional clustering algorithm, K-means, is famous for its simplicity and low time complexity. However, the usability of K-means is limited by its shortcoming that the clustering result is heavily dependent on the user-defined variants, i.e., the ...
Ghorbani, Ali A., Guan, Yu, Belacel, N.
core +1 more source
Molecular Oncology, EarlyView.Circulating microRNA profiles distinguish nonmalignant Lynch syndrome from cancer‐associated Lynch syndrome, sporadic colorectal cancer, and healthy controls. Age‐stratified analysis reveals disease‐specific regulatory states, and a six‐miRNA panel shows concordant separation in an independent colorectal tissue cohort.
Ramadhani Salum Chambuso +5 more
wiley +1 more source
RACK: RApid Clustering using K-means algorithm
, 2009 The k-means algorithm is an extremely popular technique for clustering data. One of the major limitations of the k-means is that the time to cluster a given dataset D is linear in the number of clusters, k.
Murty, MN, Garg, Vikas K
core
MACOC: a medoid-based ACO clustering algorithm [PDF]
, 2014 The application of ACO-based algorithms in data mining is growing over the last few years and several supervised and unsupervised learning algorithms have been developed using this bio-inspired approach. Most recent works concerning unsupervised learning
Otero, Fernando E. B. +7 more
core +1 more source
Mutant p53R273H disrupts PDPK1 homodimerization and contributes to PDPK1 activation
Molecular Oncology, EarlyView.How mutant p53R273H drives AKT signaling is unclear. We show that p53R273H, but not wild‐type, directly binds PDPK1 via a mutation‐dependent conformational change. This interaction disrupts inhibitory PDPK1 homodimerization and enhances AKT phosphorylation.
Mei Chee Lim +11 more
wiley +1 more source