Results 81 to 90 of about 1,461,923 (295)
An Improvement of Spectral Clustering via Message Passing and Density Sensitive Similarity
IEEE Access, 2019 Spectral clustering transforms the data clustering problem into a graph-partitioning problem and classifies data points by finding the optimal sub-graphs. Traditional spectral clustering algorithms use Gaussian kernel function to construct the similarity
Lijuan Wang, Shifei Ding, Hongjie Jia
doaj +1 more source
Covariate-assisted spectral clustering [PDF]
Biometrika, 2017 28 pages, 4 figures, includes substantial changes to theoretical ...
Norbert Binkiewicz +2 more
openaire +4 more sources
Spectral Clustering of Psychological Networks
, 2021 Spectral clustering is a well-known method for clustering the vertices of an undirected network. Although its use in network psychometrics has been limited, spectral clustering has a close relationship to the commonly-used walktrap algorithm.
Douglas Steinley +2 more
core +1 more source
Metastatic niche shaped by host factors influences disseminated cancer cell fate
FEBS Letters, EarlyView.Metastasis is shaped not only by cancer cells but also by the environments they encounter. This review explores how factors such as aging, diet, the microbiome, lifestyle, and environmental exposures remodel organ‐specific niches in the lung, liver, bone, and brain, influencing where metastatic cells survive, remain dormant, or grow, and ultimately ...
Gwennan Delyth Ward +2 more
wiley +1 more source
Scientific Reports, 2017 A major challenge in clinical cancer research is the identification of accurate molecular subtype. While unsupervised clustering methods have been applied for class discovery, this clustering method remains a bottleneck in developing accurate method for ...
Mingguang Shi, Guofu Xu
doaj +1 more source
Robust Spectral Clustering via Matrix Aggregation
IEEE Access, 2018 Spectral clustering has become one of the most popular clustering algorithms in recent years. In real-world clustering problems, the data points for clustering may have considerable noise.
Lei Du, Yan Pan, Xiaonan Luo
doaj +1 more source
Smaller is better: nanobodies meet NMR
FEBS Letters, EarlyView.Nanobodies are single‐domain antigen‐binding fragments derived from camelid heavy chain antibodies. Their small size, high stability, and exceptional specificity make nanobodies uniquely useful probes for NMR studies of protein dynamics, transient conformational states, and protein–protein interactions.
Oleg Y. Dmitriev
wiley +1 more source
CoRRCoresets have become an invaluable tool for solving $k$-means and kernel $k$-means clustering problems on large datasets with small numbers of clusters. On the other hand, spectral clustering works well on sparse graphs and has recently been extended to scale efficiently to large numbers of clusters.
Ben Jourdan +3 more
openaire +3 more sources
Dealing with non-metric dissimilarities in fuzzy central clustering algorithms [PDF]
, 2008 Clustering is the problem of grouping objects on the basis of a similarity measure among them. Relational clustering methods can be employed when a feature-based representation of the objects is not available, and their description is given in terms of ...
Filippone, M., Maurizio Filippone
core +1 more source
Going viral − Virus‐targeting nanobodies for research, diagnostics and therapy
FEBS Letters, EarlyView.The increasing prevalence of viral diseases represents a major threat for global human health. Nanobodies, small binding fragments derived from camelid heavy‐chain antibodies, offer versatile tools for viral research, diagnostics, and antiviral therapy.
Domenic Ebert, Ulrich Rothbauer
wiley +1 more source