Results 41 to 50 of about 225,995 (267)
Hierarchical clustering of words [PDF]
Proceedings of the 16th conference on Computational linguistics -, 1996 This paper describes a data-driven method for hierarchical clustering of words in which a large vocabulary of English words is clustered bottom-up, with respect to corpora ranging in size from 5 to 50 million words, using a greedy algorithm that tries to minimize average loss of mutual information of adjacent classes.
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FEBS Letters, EarlyView.We reconstituted Synechocystis glycogen synthesis in vitro from purified enzymes and showed that two GlgA isoenzymes produce glycogen with different architectures: GlgA1 yields denser, highly branched glycogen, whereas GlgA2 synthesizes longer, less‐branched chains.
Kenric Lee +3 more
wiley +1 more source
Higra: Hierarchical Graph Analysis
SoftwareX, 2019 Higra — Hierarchical Graph Analysis is a C++/Python library for efficient sparse graph analysis with a special focus on hierarchical methods capable of handling large amount of data.
B. Perret +5 more
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Likelihood Based Hierarchical Clustering [PDF]
IEEE Transactions on Signal Processing, 2004 This paper develops a new method for hierarchical clustering. Unlike other existing clustering schemes, our method is based on a generative, tree-structured model that represents relationships between the objects to be clustered, rather than directly modeling properties of objects themselves.
Castro, R.M., Coates, M., Nowak, R.
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From mice to humans—divergent strategies for intestinal homeostasis and regeneration
FEBS Letters, EarlyView.Recent advances such as organoid genome editing, xenotransplantation, imaging, and whole‐genome sequencing have enabled direct studies of human intestinal stem cells (ISCs). These studies reveal species‐specific features, including slower ISC proliferation, distinct injury responses, slower somatic mutation accumulation in humans, and an inverse ...
Keiko Ishikawa +2 more
wiley +1 more source
The Price of Hierarchical Clustering
AlgorithmicaAbstract Hierarchical Clustering is a popular tool for understanding the hereditary properties of a data set. Such a clustering is actually a sequence of clusterings that starts with the trivial clustering in which every data point forms its own cluster and then successively merges two existing clusters until all points are in the same ...
Anna Arutyunova, Heiko Röglin
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Molecular Oncology, EarlyView.In this explorative study, the abundance of circular RNA molecules in bone marrow stem cells was found to be elevated in patients with high‐risk myelodysplastic neoplasms, and to be associated with an increased risk of progression to acute myeloid leukemia.
Eileen Wedge +17 more
wiley +1 more source
Anytime Hierarchical Clustering
CoRR, 2014 We propose a new anytime hierarchical clustering method that iteratively transforms an arbitrary initial hierarchy on the configuration of measurements along a sequence of trees we prove for a fixed data set must terminate in a chain of nested partitions that satisfies a natural homogeneity requirement.
Ömür Arslan, Daniel E. Koditschek
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Hierarchical kernel spectral clustering [PDF]
Neural Networks, 2012 Kernel spectral clustering fits in a constrained optimization framework where the primal problem is expressed in terms of high-dimensional feature maps and the dual problem is expressed in terms of kernel evaluations. An eigenvalue problem is solved at the training stage and projections onto the eigenvectors constitute the clustering model.
Alzate Perez, Carlos, Suykens, Johan
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Molecular Oncology, EarlyView.RNA profiling of circulating extracellular vesicles (EVs) from blood samples of men undergoing prostate biopsy identifies transcripts associated with clinically significant prostate cancer. Integrative analysis with public tumor datasets links EV‐derived gene signatures to tumor stage and progression‐free survival, highlighting CASP3, XRCC2, and RIT1 ...
Stefan Werner +14 more
wiley +1 more source