Cargando…

Predicting disease associations via biological network analysis

BACKGROUND: Understanding the relationship between diseases based on the underlying biological mechanisms is one of the greatest challenges in modern biology and medicine. Exploring disease-disease associations by using system-level biological data is expected to improve our current knowledge of dis...

Descripción completa

Detalles Bibliográficos
Autores principales:	Sun, Kai, Gonçalves, Joana P, Larminie, Chris, Pržulj, Nataša
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	BioMed Central 2014
Materias:	Research Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4174675/ https://www.ncbi.nlm.nih.gov/pubmed/25228247 http://dx.doi.org/10.1186/1471-2105-15-304

Ejemplares similares

Discovering disease-disease associations by fusing systems-level molecular data
por: Žitnik, Marinka, et al.
Publicado: (2013)

Uncovering Biological Network Function via Graphlet Degree Signatures
por: Milenkoviæ, Tijana, et al.
Publicado: (2008)

Dominating Biological Networks
por: Milenković, Tijana, et al.
Publicado: (2011)

Unveiling new disease, pathway, and gene associations via multi-scale neural network
por: Gaudelet, Thomas, et al.
Publicado: (2020)

Survey of Network-Based Approaches to Research of Cardiovascular Diseases
por: Sarajlić, Anida, et al.
Publicado: (2014)

Network Topology Reveals Key Cardiovascular Disease Genes
por: Sarajlić, Anida, et al.
Publicado: (2013)

L-GRAAL: Lagrangian graphlet-based network aligner
por: Malod-Dognin, Noël, et al.
Publicado: (2015)

Not All Scale-Free Networks Are Born Equal: The Role of the Seed Graph in PPI Network Evolution
por: Hormozdiari, Fereydoun, et al.
Publicado: (2007)

Optimized Null Model for Protein Structure Networks
por: Milenković, Tijana, et al.
Publicado: (2009)

Integration of molecular network data reconstructs Gene Ontology
por: Gligorijević, Vladimir, et al.
Publicado: (2014)

GraphCrunch: A tool for large network analyses
por: Milenković, Tijana, et al.
Publicado: (2008)

Unified Alignment of Protein-Protein Interaction Networks
por: Malod-Dognin, Noël, et al.
Publicado: (2017)

Optimal Network Alignment with Graphlet Degree Vectors
por: Milenković, Tijana, et al.
Publicado: (2010)

Proper evaluation of alignment-free network comparison methods
por: Yaveroğlu, Ömer Nebil, et al.
Publicado: (2015)

Geometric De-noising of Protein-Protein Interaction Networks
por: Kuchaiev, Oleksii, et al.
Publicado: (2009)

GraphCrunch 2: Software tool for network modeling, alignment and clustering
por: Kuchaiev, Oleksii, et al.
Publicado: (2011)

Graphlet-based Characterization of Directed Networks
por: Sarajlić, Anida, et al.
Publicado: (2016)

Protein interaction network topology uncovers melanogenesis regulatory network components within functional genomics datasets
por: Ho, Hsiang, et al.
Publicado: (2010)

A functional analysis of omic network embedding spaces reveals key altered functions in cancer
por: Doria-Belenguer, Sergio, et al.
Publicado: (2023)

Omics Data Complementarity Underlines Functional Cross-Communication in Yeast
por: Malod-Dognin, Noël, et al.
Publicado: (2017)

Topology-function conservation in protein–protein interaction networks
por: Davis, Darren, et al.
Publicado: (2015)

Network neighbors of viral targets and differentially expressed genes in COVID-19 are drug target candidates
por: Zambrana, Carme, et al.
Publicado: (2021)

Modelling the Yeast Interactome
por: Janjić, Vuk, et al.
Publicado: (2014)

Integrated Data Analysis Uncovers New COVID-19 Related Genes and Potential Drug Re-Purposing Candidates
por: Xenos, Alexandros, et al.
Publicado: (2023)

Integrative Data Analytic Framework to Enhance Cancer Precision Medicine
por: Gaudelet, Thomas, et al.
Publicado: (2021)

Revealing the Hidden Language of Complex Networks
por: Yaveroğlu, Ömer Nebil, et al.
Publicado: (2014)

Graphlet eigencentralities capture novel central roles of genes in pathways
por: Windels, Sam F. L., et al.
Publicado: (2022)

ASPASIA: A toolkit for evaluating the effects of biological interventions on SBML model behaviour
por: Evans, Stephanie, et al.
Publicado: (2017)

Identifying cellular cancer mechanisms through pathway-driven data integration
por: Windels, Sam F L, et al.
Publicado: (2022)

Chromatin network markers of leukemia
por: Malod-Dognin, N, et al.
Publicado: (2020)

Predicting drug−disease associations via sigmoid kernel-based convolutional neural networks
por: Jiang, Han-Jing, et al.
Publicado: (2019)

Pathway Network Analysis of Complex Diseases Based on Multiple Biological Networks
por: Zheng, Fang, et al.
Publicado: (2018)

A phenotype driven integrative framework uncovers molecular mechanisms of a rare hereditary thrombophilia
por: Malod-Dognin, Noël, et al.
Publicado: (2023)

Predicting miRNA-disease associations based on PPMI and attention network
por: Xie, Xuping, et al.
Publicado: (2023)

Network Diffusion Approach to Predict LncRNA Disease Associations Using Multi-Type Biological Networks: LION
por: Sumathipala, Marissa, et al.
Publicado: (2019)

Interactogeneous: Disease Gene Prioritization Using Heterogeneous Networks and Full Topology Scores
por: Gonçalves, Joana P., et al.
Publicado: (2012)

Predicting miRNA-disease associations via layer attention graph convolutional network model
por: Han, Han, et al.
Publicado: (2022)

Explainable drug side effect prediction via biologically informed graph neural network
por: Huang, Tongtong, et al.
Publicado: (2023)

Critical Review on Zeolite Clinoptilolite Safety and Medical Applications in vivo
por: Kraljević Pavelić, Sandra, et al.
Publicado: (2018)

Probabilistic Inference of Biological Networks via Data Integration
por: Rogers, Mark F., et al.
Publicado: (2015)

Cannot write session to /tmp/vufind_sessions/sess_0nbg4m9nkh8bnm4k5mh81cvq7g