Cargando…

Semi-supervised adaptive-height snipping of the hierarchical clustering tree

BACKGROUND: In genomics, hierarchical clustering (HC) is a popular method for grouping similar samples based on a distance measure. HC algorithms do not actually create clusters, but compute a hierarchical representation of the data set. Usually, a fixed height on the HC tree is used, and each conti...

Descripción completa

Detalles Bibliográficos
Autores principales:	Obulkasim, Askar, Meijer, Gerrit A, van de Wiel, Mark A
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	BioMed Central 2015
Materias:	Research Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4302100/ https://www.ncbi.nlm.nih.gov/pubmed/25592847 http://dx.doi.org/10.1186/s12859-014-0448-1

Ejemplares similares

HCsnip: An R Package for Semi-supervised Snipping of the Hierarchical Clustering Tree
por: Obulkasim, Askar, et al.
Publicado: (2015)

Stepwise classification of cancer samples using clinical and molecular data
por: Obulkasim, Askar, et al.
Publicado: (2011)

Semi-supervised oblique predictive clustering trees
por: Stepišnik, Tomaž, et al.
Publicado: (2021)

stepwiseCM: An R Package for Stepwise Classification of Cancer Samples Using Multiple Heterogeneous Data Sets
por: Obulkasim, Askar, et al.
Publicado: (2014)

Semi‐supervised empirical Bayes group‐regularized factor regression
por: Münch, Magnus M., et al.
Publicado: (2022)

Vasectomy: A simple snip?
por: Dhar, Nivedita Bhatta, et al.
Publicado: (2007)

Semi-Supervised Fuzzy Clustering with Feature Discrimination
por: Li, Longlong, et al.
Publicado: (2015)

Semi-Supervised Clustering for Financial Risk Analysis
por: Han, Yihan, et al.
Publicado: (2021)

Semi-supervised adversarial discriminative domain adaptation
por: Nguyen, Thai-Vu, et al.
Publicado: (2022)

Treatment of Hæmorrhoids by Strangulation and Snipping Method
por: Kehar, D. R.
Publicado: (1925)

Formation Mechanisms for Phosphorene and SnIP
por: Pielmeier, Markus R. P., et al.
Publicado: (2021)

Direct comparison of supervised and semi-supervised retraining approaches for co-adaptive BCIs
por: Schwarz, Andreas, et al.
Publicado: (2019)

Colorectal adenoma to carcinoma progression is accompanied by changes in gene expression associated with ageing, chromosomal instability, and fatty acid metabolism
por: Carvalho, Beatriz, et al.
Publicado: (2012)

Fast tree aggregation for consensus hierarchical clustering
por: Hulot, Audrey, et al.
Publicado: (2020)

Hierarchical Wireless Multimedia Sensor Networks for Collaborative Hybrid Semi-Supervised Classifier Learning
por: Wang, Xue, et al.
Publicado: (2007)

Semi-supervised spectral clustering with application to detect population stratification
por: Liu, Binghui, et al.
Publicado: (2013)

Semi-supervised consensus clustering for gene expression data analysis
por: Wang, Yunli, et al.
Publicado: (2014)

Feature selection and semi-supervised clustering using multiobjective optimization
por: Saha, Sriparna, et al.
Publicado: (2014)

Semi-supervised clustering of fractionated electrograms for electroanatomical atrial mapping
por: Orozco-Duque, Andres, et al.
Publicado: (2016)

Supervised, semi-supervised and unsupervised inference of gene regulatory networks
por: Maetschke, Stefan R., et al.
Publicado: (2014)

Supervised machine learning classification of psychosis biotypes based on brain structure: findings from the Bipolar-Schizophrenia network for intermediate phenotypes (B-SNIP)
por: Koen, Joshua D., et al.
Publicado: (2023)

A 'snip' in time: what is the best age to circumcise?
por: Morris, Brian J, et al.
Publicado: (2012)

Selection of informative clusters from hierarchical cluster tree with gene classes
por: Toronen, Petri
Publicado: (2004)

Prior Adaptive Semi-supervised Learning with Application to EHR Phenotyping
por: Zhang, Yichi, et al.
Publicado: (2022)

Semi-Supervised Domain Adaptation for Holistic Counting under Label Gap
por: Litrico, Mattia, et al.
Publicado: (2021)

Psychosis Biotypes: Replication and Validation from the B-SNIP Consortium
por: Clementz, Brett A, et al.
Publicado: (2021)

Semi-supervised learning /
Publicado: (2010)

Array-based genotyping in S.cerevisiae using semi-supervised clustering
por: Bourgon, Richard, et al.
Publicado: (2009)

Semi-Supervised Multi-View Clustering with Weighted Anchor Graph Embedding
por: Wang, Senhong, et al.
Publicado: (2021)

Utilization of temporal autoencoder for semi-supervised intracranial EEG clustering and classification
por: Nejedly, Petr, et al.
Publicado: (2023)

Variational infinite heterogeneous mixture model for semi-supervised clustering of heart enhancers
por: Mehdi, Tahmid F, et al.
Publicado: (2019)

scSemiAAE: a semi-supervised clustering model for single-cell RNA-seq data
por: Wang, Zile, et al.
Publicado: (2023)

Reduced genomic tumor heterogeneity after neoadjuvant chemotherapy is related to favorable outcome in patients with esophageal adenocarcinoma
por: Obulkasim, Askar, et al.
Publicado: (2016)

Seeing the forest for the trees: using the Gene Ontology to restructure hierarchical clustering
por: Dotan-Cohen, Dikla, et al.
Publicado: (2009)

Person re-identification via semi-supervised adaptive graph embedding
por: Liu, Jiao, et al.
Publicado: (2022)

Generative Adversarial Training for Supervised and Semi-supervised Learning
por: Wang, Xianmin, et al.
Publicado: (2022)

A Cluster-then-label Semi-supervised Learning Approach for Pathology Image Classification
por: Peikari, Mohammad, et al.
Publicado: (2018)

A unified view of density-based methods for semi-supervised clustering and classification
por: Castro Gertrudes, Jadson, et al.
Publicado: (2020)

Possibilistic Clustering-Promoting Semi-Supervised Learning for EEG-Based Emotion Recognition
por: Dan, Yufang, et al.
Publicado: (2021)

[Image: see text] 17th SNIP Scientific Conference
Publicado: (2011)

Cannot write session to /tmp/vufind_sessions/sess_r8ud8s4igl70g1ubndql7m4kap