Results 81 to 90 of about 1,246,571 (292)
Algorithms for finding $k$ in $k$-means
CoRR, 2020 38 ...
Chiranjib Bhattacharyya +2 more
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Molecular Oncology, EarlyView.Keratin 19 (KRT19) is overexpressed in high‐grade serous ovarian cancer with high levels of Kallikrein‐related peptidases (KLK) 4–7 and is associated with poor survival. In vivo analyses demonstrate that elevated KRT19 increases peritoneal tumour burden.
Sophia Bielesch +13 more
wiley +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 +2 more sources
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
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MITF maintains genome stability in nonmelanocyte lineages
Molecular Oncology, EarlyView.MITF is essential for melanocyte survival and acts as an oncogene in 10%–20% of melanomas. We show that MITF depletion causes genome instability in nonmelanocytic cells, leading to LATS2‐mediated P53 activation, cell cycle arrest, and apoptosis. This study highlights the role of MITF as a genome maintenance factor beyond the melanocyte lineage. Created
Drifa H. Gudmundsdottir +13 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
Bilateral k-Means Algorithm for Fast Co-Clustering
AAAI Conference on Artificial Intelligence, 2017 With the development of the information technology, the amount of data, e.g. text, image and video, has been increased rapidly. Efficiently clustering those large scale data sets is a challenge. To address this problem, this paper proposes a novel co-
Junwei Han, Kun Song, F. Nie, Xuelong Li
semanticscholar +1 more source
Molecular Oncology, EarlyView.The novel styrylquinazolinone‐based molecule W1B effectively suppresses glioblastoma by inhibiting IGF1R and EGFR. In high‐glucose microenvironments driving tumor resistance, W1B acts synergistically with the EGFR inhibitor dacomitinib. This combination safely blocks compensatory survival signaling in zebrafish xenograft models. Showcasing promising in
Patryk Rurka +9 more
wiley +1 more source
Self-Adaptive K-Means Based on a Covering Algorithm
Complexity, 2018 The K-means algorithm is one of the ten classic algorithms in the area of data mining and has been studied by researchers in numerous fields for a long time.
Yiwen Zhang +6 more
doaj +1 more source
Liquid biopsy‐based diagnostic evaluation of hypermethylated CpG sites for ovarian cancer diagnosis
Molecular Oncology, EarlyView.This schematic outlines the workflow from biomarker identification to duplex MethyLight assay validation for epithelial ovarian cancer diagnosis using cfDNA‐based liquid biopsy. Initial screening of hypermethylated CpG candidates (cg02957270, cg10061138 cg00480298, COL2A1) was performed in tissue using ARMS‐PCR, COBRA, qPCR and image analysis. Selected
Deepa Bisht +3 more
wiley +1 more source