Results 81 to 90 of about 761,172 (262)
A density‐based enhancement to dominant sets clustering
IET Computer Vision, 2013 Although there is no shortage of clustering algorithms, existing algorithms are often afflicted by problems of one kind or another. Dominant sets clustering is a graph‐theoretic approach to clustering and exhibits significant potential in various ...
Jian Hou +4 more
doaj +1 more source
Molecular Oncology, EarlyView.AZD9291 has shown promise in targeted cancer therapy but is limited by resistance. In this study, we employed metabolic labeling and LC–MS/MS to profile time‐resolved nascent protein perturbations, allowing dynamic tracking of drug‐responsive proteins. We demonstrated that increased NNMT expression is associated with drug resistance, highlighting NNMT ...
Zhanwu Hou +5 more
wiley +1 more source
Molecular Oncology, EarlyView.This study explores salivary RNA for breast cancer (BC) diagnosis, prognosis, and follow‐up. High‐throughput RNA sequencing identified distinct salivary RNA signatures, including novel transcripts, that differentiate BC from healthy controls, characterize histological and molecular subtypes, and indicate lymph node involvement.
Nicholas Rajan +9 more
wiley +1 more source
Fast Clustering by Affinity Propagation Based on Density Peaks
IEEE Access, 2020 Clustering is an important technique in data mining and knowledge discovery. Affinity propagation clustering (AP) and density peaks and distance-based clustering (DDC) are two significant clustering algorithms proposed in 2007 and 2014 respectively.
Yang Li, Chonghui Guo, Leilei Sun
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Consistency, breakdown robustness, and algorithms for robust improper maximum likelihood clustering [PDF]
, 2017 The robust improper maximum likelihood estimator (RIMLE) is a new method for robust multivariate clustering finding approximately Gaussian clusters.
Coretto, Pietro, Hennig, Christian
core +1 more source
Molecular Oncology, EarlyView.A‐to‐I editing of miRNAs, particularly miR‐200b‐3p, contributes to HGSOC progression by enhancing cancer cell proliferation, migration and 3D growth. The edited form is linked to poorer patient survival and the identification of novel molecular targets.
Magdalena Niemira +14 more
wiley +1 more source
Density-based clustering of polygons
2009 IEEE Symposium on Computational Intelligence and Data Mining, 2009 Clustering is an important task in spatial data mining and spatial analysis. We propose a clustering algorithm P-DBSCAN to cluster polygons in space. P-DBSCAN is based on the well established density-based clustering algorithm DBSCAN. In order to cluster polygons, we incorporate their topological and spatial properties in the process of clustering by ...
Joshi, Deepti +2 more
openaire +2 more sources
Molecular Oncology, EarlyView.This study indicates that Merkel cell carcinoma (MCC) does not originate from Merkel cells, and identifies gene, protein & cellular expression of immune‐linked and neuroendocrine markers in primary and metastatic Merkel cell carcinoma (MCC) tumor samples, linked to Merkel cell polyomavirus (MCPyV) status, with enrichment of B‐cell and other immune cell
Richie Jeremian +10 more
wiley +1 more source
Comprehensive analysis of clustering algorithms: exploring limitations and innovative solutions [PDF]
PeerJ Computer ScienceThis survey rigorously explores contemporary clustering algorithms within the machine learning paradigm, focusing on five primary methodologies: centroid-based, hierarchical, density-based, distribution-based, and graph-based clustering. Through the lens
Aasim Ayaz Wani
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Clustering Algorithm Based on Density Peak and Neighbor Optimization
Jisuanji kexue yu tansuo, 2020 The time complexity of density peak algorithm in selecting the cluster center is very high. It needs to manually select the cutoff distance. When processing the manifold data, there may be multiple density peaks, which leads to the decrease of clustering
HE Yunbin, DONG Heng, WAN Jing, LI Song
doaj +1 more source