Cargando…

Finding combinatorial histone code by semi-supervised biclustering

BACKGROUND: Combinatorial histone modification is an important epigenetic mechanism for regulating chromatin state and gene expression. Given the rapid accumulation of genome-wide histone modification maps, there is a pressing need for computational methods capable of joint analysis of multiple maps...

Descripción completa

Detalles Bibliográficos
Autores principales:	Teng, Li, Tan, Kai
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	BioMed Central 2012
Materias:	Methodology Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3443427/ https://www.ncbi.nlm.nih.gov/pubmed/22759587 http://dx.doi.org/10.1186/1471-2164-13-301

Ejemplares similares

A comparison and evaluation of five biclustering algorithms by quantifying goodness of biclusters for gene expression data
por: Li, Li, et al.
Publicado: (2012)

Semi-supervised discovery of differential genes
por: Oba, Shigeyuki, et al.
Publicado: (2006)

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

Semi-supervised COVID-19 CT image segmentation using deep generative models
por: Zammit, Judah, et al.
Publicado: (2022)

A novel biclustering approach with iterative optimization to analyze gene expression data
por: Sutheeworapong, Sawannee, et al.
Publicado: (2012)

IILLS: predicting virus-receptor interactions based on similarity and semi-supervised learning
por: Yan, Cheng, et al.
Publicado: (2019)

Chemical toxicity prediction based on semi-supervised learning and graph convolutional neural network
por: Chen, Jiarui, et al.
Publicado: (2021)

Robust identification of molecular phenotypes using semi-supervised learning
por: Roder, Heinrich, et al.
Publicado: (2019)

Discovering biclusters in gene expression data based on high-dimensional linear geometries
por: Gan, Xiangchao, et al.
Publicado: (2008)

A visual analytics approach for understanding biclustering results from microarray data
por: Santamaría, Rodrigo, et al.
Publicado: (2008)

Biclustering of gene expression data by non-smooth non-negative matrix factorization
por: Carmona-Saez, Pedro, et al.
Publicado: (2006)

Integrated biclustering of heterogeneous genome-wide datasets for the inference of global regulatory networks
por: Reiss, David J, et al.
Publicado: (2006)

EDISA: extracting biclusters from multiple time-series of gene expression profiles
por: Supper, Jochen, et al.
Publicado: (2007)

A novel semi-supervised algorithm for the taxonomic assignment of metagenomic reads
por: Le, Vinh Van, et al.
Publicado: (2016)

Biclustering for the comprehensive search of correlated gene expression patterns using clustered seed expansion
por: Yun, Taegyun, et al.
Publicado: (2013)

A semi–supervised tensor regression model for siRNA efficacy prediction
por: Thang, Bui Ngoc, et al.
Publicado: (2015)

Predicting the hosts of prokaryotic viruses using GCN-based semi-supervised learning
por: Shang, Jiayu, et al.
Publicado: (2021)

Semi-supervised machine learning for automated species identification by collagen peptide mass fingerprinting
por: Gu, Muxin, et al.
Publicado: (2018)

Identification of coherent patterns in gene expression data using an efficient biclustering algorithm and parallel coordinate visualization
por: Cheng, Kin-On, et al.
Publicado: (2008)

Deep semi-supervised learning ensemble framework for classifying co-mentions of human proteins and phenotypes
por: Pourreza Shahri, Morteza, et al.
Publicado: (2021)

Metabolic labeling in middle-down proteomics allows for investigation of the dynamics of the histone code
por: Sidoli, Simone, et al.
Publicado: (2017)

Finding distinct biclusters from background in gene expression matrices
por: Wu, Zhengpeng, et al.
Publicado: (2007)

Semantic biclustering for finding local, interpretable and predictive expression patterns
por: Kléma, Jiří, et al.
Publicado: (2017)

Incorporating the type and direction information in predicting novel regulatory interactions between HIV-1 and human proteins using a biclustering approach
por: Mukhopadhyay, Anirban, et al.
Publicado: (2014)

Differential co-expression framework to quantify goodness of biclusters and compare biclustering algorithms
por: Chia, Burton Kuan Hui, et al.
Publicado: (2010)

Automatic layout and visualization of biclusters
por: Grothaus, Gregory A, et al.
Publicado: (2006)

Multi-species integrative biclustering
por: Waltman, Peter, et al.
Publicado: (2010)

RUBic: rapid unsupervised biclustering
por: Sriwastava, Brijesh K., et al.
Publicado: (2023)

A biclustering algorithm based on a Bicluster Enumeration Tree: application to DNA microarray data
por: Ayadi, Wassim, et al.
Publicado: (2009)

Bi-Force: large-scale bicluster editing and its application to gene expression data biclustering
por: Sun, Peng, et al.
Publicado: (2014)

Semi-supervised learning /
Publicado: (2010)

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

Quality Measures for Gene Expression Biclusters
por: Pontes, Beatriz, et al.
Publicado: (2015)

Exact model reduction of combinatorial reaction networks
por: Conzelmann, Holger, et al.
Publicado: (2008)

Bayesian biclustering of gene expression data
por: Gu, Jiajun, et al.
Publicado: (2008)

FABIA: factor analysis for bicluster acquisition
por: Hochreiter, Sepp, et al.
Publicado: (2010)

Fluorogenic sensor platform for the histone code using receptors from dynamic combinatorial libraries
por: Peacor, Brendan C., et al.
Publicado: (2017)

A Dynamic and Combinatorial Histone Code Drives Malaria Parasite Asexual and Sexual Development
por: von Grüning, Hilde, et al.
Publicado: (2022)

In-silico predictive mutagenicity model generation using supervised learning approaches
por: Seal, Abhik, et al.
Publicado: (2012)

MS_HistoneDB, a manually curated resource for proteomic analysis of human and mouse histones
por: El Kennani, Sara, et al.
Publicado: (2017)

Cannot write session to /tmp/vufind_sessions/sess_iao4mtue7bn3qkqqmhdtl531cf