Cargando…

Significance evaluation in factor graphs

BACKGROUND: Factor graphs provide a flexible and general framework for specifying probability distributions. They can capture a range of popular and recent models for analysis of both genomics data as well as data from other scientific fields. Owing to the ever larger data sets encountered in genomi...

Descripción completa

Detalles Bibliográficos
Autores principales:	Madsen, Tobias, Hobolth, Asger, Jensen, Jens Ledet, Pedersen, Jakob Skou
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	BioMed Central 2017
Materias:	Methodology Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5374669/ https://www.ncbi.nlm.nih.gov/pubmed/28359297 http://dx.doi.org/10.1186/s12859-017-1614-z

Ejemplares similares

ncdDetect2: improved models of the site-specific mutation rate in cancer and driver detection with robust significance evaluation
por: Juul, Malene, et al.
Publicado: (2019)

Regmex: a statistical tool for exploring motifs in ranked sequence lists from genomics experiments
por: Nielsen, Morten Muhlig, et al.
Publicado: (2018)

Comparison of methods for calculating conditional expectations of sufficient statistics for continuous time Markov chains
por: Tataru, Paula, et al.
Publicado: (2011)

Non-coding cancer driver candidates identified with a sample- and position-specific model of the somatic mutation rate
por: Juul, Malene, et al.
Publicado: (2017)

Pan-cancer screen for mutations in non-coding elements with conservation and cancer specificity reveals correlations with expression and survival
por: Hornshøj, Henrik, et al.
Publicado: (2018)

A site specific model and analysis of the neutral somatic mutation rate in whole-genome cancer data
por: Bertl, Johanna, et al.
Publicado: (2018)

Assessing statistical significance in causal graphs
por: Chindelevitch, Leonid, et al.
Publicado: (2012)

On the significance of edges for connectivity in uncertain random graphs
por: Li, Hao, et al.
Publicado: (2021)

Bayesian coestimation of phylogeny and sequence alignment
por: Lunter, Gerton, et al.
Publicado: (2005)

Soft document clustering using a novel graph covering approach
por: Dörpinghaus, Jens, et al.
Publicado: (2018)

Model selection and robust inference of mutational signatures using Negative Binomial non-negative matrix factorization
por: Pelizzola, Marta, et al.
Publicado: (2023)

METAMVGL: a multi-view graph-based metagenomic contig binning algorithm by integrating assembly and paired-end graphs
por: Zhang, Zhenmiao, et al.
Publicado: (2021)

Network evaluation from the consistency of the graph structure with the measured data
por: Saito, Shigeru, et al.
Publicado: (2008)

The importance of distinguishing between the odds ratio and the incidence rate ratio in GWAS
por: Waltoft, Berit Lindum, et al.
Publicado: (2015)

Algorithms for Hidden Markov Models Restricted to Occurrences of Regular Expressions
por: Tataru, Paula, et al.
Publicado: (2013)

DHPV: a distributed algorithm for large-scale graph partitioning
por: Adoni, Wilfried Yves Hamilton, et al.
Publicado: (2020)

Graphing and reporting heterogeneous treatment effects through reference classes
por: Watson, James A., et al.
Publicado: (2020)

Predicting functions of maize proteins using graph convolutional network
por: Zhou, Guangjie, et al.
Publicado: (2020)

Predicting combinations of drugs by exploiting graph embedding of heterogeneous networks
por: Song, Fei, et al.
Publicado: (2022)

Graph-based prediction of Protein-protein interactions with attributed signed graph embedding
por: Yang, Fang, et al.
Publicado: (2020)

A graph-based filtering method for top-down mass spectral identification
por: Yang, Runmin, et al.
Publicado: (2018)

Drug response prediction using graph representation learning and Laplacian feature selection
por: Xie, Minzhu, et al.
Publicado: (2022)

A proximity-based graph clustering method for the identification and application of transcription factor clusters
por: Spadafore, Maxwell, et al.
Publicado: (2017)

A Novel Graph-based Algorithm to Infer Recurrent Copy Number Variations in Cancer
por: Chi, Chen, et al.
Publicado: (2016)

Drug-target interaction prediction using semi-bipartite graph model and deep learning
por: Eslami Manoochehri, Hafez, et al.
Publicado: (2020)

Detecting circular RNA from high-throughput sequence data with de Bruijn graph
por: Li, Xin, et al.
Publicado: (2020)

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

A novel hybrid framework for metabolic pathways prediction based on the graph attention network
por: Yang, Zhihui, et al.
Publicado: (2022)

Graph-representation of oxidative folding pathways
por: Ágoston, Vilmos, et al.
Publicado: (2005)

CGINet: graph convolutional network-based model for identifying chemical-gene interaction in an integrated multi-relational graph
por: Wang, Wei, et al.
Publicado: (2020)

Identification of early mild cognitive impairment using multi-modal data and graph convolutional networks
por: Liu, Jin, et al.
Publicado: (2020)

Bipartite graph-based collaborative matrix factorization method for predicting miRNA-disease associations
por: Zhou, Feng, et al.
Publicado: (2021)

Drug–target interaction prediction using unifying of graph regularized nuclear norm with bilinear factorization
por: Sorkhi, Ali Ghanbari, et al.
Publicado: (2021)

Reconstruction of large-scale regulatory networks based on perturbation graphs and transitive reduction: improved methods and their evaluation
por: Pinna, Andrea, et al.
Publicado: (2013)

SING: Subgraph search In Non-homogeneous Graphs
por: Di Natale, Raffaele, et al.
Publicado: (2010)

Computing paths and cycles in biological interaction graphs
por: Klamt, Steffen, et al.
Publicado: (2009)

Multiple graph regularized protein domain ranking
por: Wang, Jim Jing-Yan, et al.
Publicado: (2012)

A graph-theory method for pattern identification in geographical epidemiology – a preliminary application to deprivation and mortality
por: Maheswaran, Ravi, et al.
Publicado: (2009)

Revealing post-transcriptional microRNA–mRNA regulations in Alzheimer’s disease through ensemble graphs
por: Armañanzas, Rubén
Publicado: (2018)

Graph regularized L(2,1)-nonnegative matrix factorization for miRNA-disease association prediction
por: Gao, Zhen, et al.
Publicado: (2020)

Cannot write session to /tmp/vufind_sessions/sess_rl3g9vvtrvamrhedrsu7cbqf7d