Results 21 to 30 of about 216,194 (219)

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, Shinya Sugimoto, Toshiro Sato   +2 more
wiley   +1 more source

Interactive interpretation of hierarchical clustering [PDF]

Intelligent Data Analysis, 1997
Automatic clustering methods are part of data mining methods. They aim at building clusters of items so that similar items fall into the same cluster while unsimilar items fall into separate clusters. A particular class of clustering methods are hierarchical ones where recursive clusters are formed to grow a binary tree representing an approximation of
Eric Boudaillier, Georges Hébrail
openaire   +1 more source

Circular RNA expression landscapes in myelodysplastic neoplasms: Associations with mutational signatures and disease progression

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, Morten Tulstrup, Gabriele Todisco, Pedro Moura, Christophe Côme, Jakob Werner Hansen, Jakob Schmidt Jespersen, Balthasar Schlotmann, Maria Creignou, Mette Dahl, Claudia Schöllkopf, Klas Raaschou‐Jensen, Krister Wennerberg, Bo Porse, Joachim Weischenfeldt, Eva Hellström‐Lindberg, Lasse Sommer Kristensen, Kirsten Grønbæk   +17 more
wiley   +1 more source

Hierarchical Hexagonal Clustering and Indexing [PDF]

Symmetry, 2019
Space-filling curves (SFCs) represent an efficient and straightforward method for sparse-space indexing to transform an n-dimensional space into a one-dimensional representation. This is often applied for multidimensional point indexing which brings a better perspective for data analysis, visualization and queries.
Vojtech Uher, Petr Gajdos, Václav Snásel, Yu-Chi Lai, Michal Radecký   +4 more
openaire   +3 more sources

Subtype‐specific enhancer RNAs define transcriptional regulators and prognosis in breast cancers

Molecular Oncology, EarlyView.
This study employed machine learning methodologies to perform the subtype‐specific classification of RNA‐seq data sets, which are mapped on enhancers from TCGA‐derived breast cancer patients. Their integration with gene expression (referred to as ProxCReAM eRNAs) and chromatin accessibility profiles has the potential to identify lineage‐specific and ...
Aamena Y. Patel, Peyman Zarrineh, Nathnael T. Tuffa, Jigar H. Sheth, Sumitra Mohan, Mudassar Iqbal, Sankari Nagarajan   +6 more
wiley   +1 more source

Network divergence analysis identifies adaptive gene modules and two orthogonal vulnerability axes in pancreatic cancer

Molecular Oncology, EarlyView.
Tumors contain diverse cellular states whose behavior is shaped by context‐dependent gene coordination. By comparing gene–gene relationships across biological contexts, we identify adaptive transcriptional modules that reorganize into distinct vulnerability axes.
Brian Nelson, Lyanne Delgado‐Coka, Natalia Marchenko, Luisa F. Escobar‐Hoyos, Kenneth R. Shroyer, Alisa Yurovsky, Trey Ideker, Gábor Balázsi, Thomas MacCarthy, Scott Powers   +9 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
openaire   +2 more sources

Hierarchical Clustering: Objective Functions and Algorithms [PDF]

Journal of the ACM, 2018
Hierarchical clustering is a recursive partitioning of a dataset into clusters at an increasingly finer granularity. Motivated by the fact that most work on hierarchical clustering was based on providing algorithms, rather than optimizing a specific objective, Dasgupta framed similarity-based hierarchical clustering as a combinatorial optimization ...
Vincent Cohen-Addad, Varun Kanade, Frederik Mallmann-Trenn, Claire Mathieu   +3 more
openaire   +6 more sources

Transcriptional profiling of circulating extracellular vesicles from prebiopsy prostate cancer patients

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, Pierre Tennstedt, Randi C. Pose, Christian Müller, Katharina Besler, Svenja Schneegans, Malik Alawi, Marie C. Roesch, Sven Peine, Desiree Bonci, Joanna Budna‐Tukan, Evi Lianidou, Catherine Alix‐Panabières, Derya Tilki, Klaus Pantel   +14 more
wiley   +1 more source

Statistical Significance for Hierarchical Clustering [PDF]

Biometrics, 2017
Summary Cluster analysis has proved to be an invaluable tool for the exploratory and unsupervised analysis of high-dimensional datasets. Among methods for clustering, hierarchical approaches have enjoyed substantial popularity in genomics and other fields for their ability to simultaneously uncover multiple layers of clustering structure.
Kimes, Patrick K., Liu, Yufeng, Hayes, David Neil, Marron, James Stephen   +3 more
openaire   +3 more sources