Network-based classification of breast cancer metastasis.
Reads0
Chats0
TLDR
A protein‐network‐based approach is applied that identifies markers not as individual genes but as subnetworks extracted from protein interaction databases, which provide novel hypotheses for pathways involved in tumor progression.Abstract:
Mapping the pathways that give rise to metastasis is one of the key challenges of breast cancer research. Recently, several large-scale studies have shed light on this problem through analysis of gene expression profiles to identify markers correlated with metastasis. Here, we apply a protein-network-based approach that identifies markers not as individual genes but as subnetworks extracted from protein interaction databases. The resulting subnetworks provide novel hypotheses for pathways involved in tumor progression. Although genes with known breast cancer mutations are typically not detected through analysis of differential expression, they play a central role in the protein network by interconnecting many differentially expressed genes. We find that the subnetwork markers are more reproducible than individual marker genes selected without network information, and that they achieve higher accuracy in the classification of metastatic versus non-metastatic tumors.read more
Citations
More filters
Journal ArticleDOI
STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets.
Damian Szklarczyk,Annika L. Gable,David Lyon,Alexander Junge,Stefan Wyder,Jaime Huerta-Cepas,Milan Simonovic,Nadezhda Tsankova Doncheva,John H. Morris,Peer Bork,Lars Juhl Jensen,Christian von Mering +11 more
TL;DR: The latest version of STRING more than doubles the number of organisms it covers, and offers an option to upload entire, genome-wide datasets as input, allowing users to visualize subsets as interaction networks and to perform gene-set enrichment analysis on the entire input.
Journal ArticleDOI
Network Medicine: A Network-Based Approach to Human Disease
TL;DR: Advances in this direction are essential for identifying new disease genes, for uncovering the biological significance of disease-associated mutations identified by genome-wide association studies and full-genome sequencing, and for identifying drug targets and biomarkers for complex diseases.
Journal ArticleDOI
Plasma MicroRNA Profiling Reveals Loss of Endothelial MiR-126 and Other MicroRNAs in Type 2 Diabetes
Anna Zampetaki,Stefan Kiechl,Ignat Drozdov,Peter Willeit,Ursula Mayr,Marianna Prokopi,Agnes Mayr,Siegfried Weger,Friedrich Oberhollenzer,Enzo Bonora,Ajay M. Shah,Johann Willeit,Manuel Mayr +12 more
TL;DR: A plasma miRNA signature for DM is revealed that includes loss of endothelial miR-126, which might explain the impaired peripheral angiogenic signaling in patients with DM.
Journal ArticleDOI
A travel guide to Cytoscape plugins
Rintaro Saito,Michael E. Smoot,Keiichiro Ono,Johannes Ruscheinski,Peng-Liang Wang,Samad Lotia,Alexander R. Pico,Gary D. Bader,Trey Ideker +8 more
TL;DR: A travel guide to the world of plugins, covering the 152 publicly available plugins for Cytoscape 2.5–2.8 and ongoing efforts to distribute, organize and maintain the quality of the collection.
Journal ArticleDOI
Uncovering disease-disease relationships through the incomplete interactome
Jörg Menche,Amitabh Sharma,Maksim Kitsak,Maksim Kitsak,Susan Dina Ghiassian,Susan Dina Ghiassian,Marc Vidal,Joseph Loscalzo,Albert-László Barabási +8 more
TL;DR: A network-based framework to identify the location of disease modules within the interactome and use the overlap between the modules to predict disease-disease relationships is presented and it is found that disease pairs with overlapping disease modules display significant molecular similarity, elevated coexpression of their associated genes, and similar symptoms and high comorbidity.
References
More filters
Journal ArticleDOI
LIBSVM: A library for support vector machines
Chih-Chung Chang,Chih-Jen Lin +1 more
TL;DR: Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
Journal ArticleDOI
Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles
Aravind Subramanian,Pablo Tamayo,Vamsi K. Mootha,Sayan Mukherjee,Benjamin L. Ebert,Michael A. Gillette,Amanda G. Paulovich,Scott L. Pomeroy,Todd R. Golub,Eric S. Lander,Jill P. Mesirov +10 more
TL;DR: The Gene Set Enrichment Analysis (GSEA) method as discussed by the authors focuses on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation.
Journal ArticleDOI
The hallmarks of cancer.
TL;DR: This work has been supported by the Department of the Army and the National Institutes of Health, and the author acknowledges the support and encouragement of the National Cancer Institute.
Journal ArticleDOI
Molecular classification of cancer: class discovery and class prediction by gene expression monitoring.
Todd R. Golub,Todd R. Golub,Donna K. Slonim,Pablo Tamayo,Christine Huard,Michelle Gaasenbeek,Jill P. Mesirov,Hilary A. Coller,Mignon L. Loh,James R. Downing,Michael A. Caligiuri,Clara D. Bloomfield,Eric S. Lander +12 more
TL;DR: A generic approach to cancer classification based on gene expression monitoring by DNA microarrays is described and applied to human acute leukemias as a test case and suggests a general strategy for discovering and predicting cancer classes for other types of cancer, independent of previous biological knowledge.
Journal ArticleDOI
Categorical Data Analysis
TL;DR: In this article, categorical data analysis was used for categorical classification of categorical categorical datasets.Categorical Data Analysis, categorical Data analysis, CDA, CPDA, CDSA
Related Papers (5)
Gene Ontology: tool for the unification of biology
M Ashburner,Catherine A. Ball,Judith A. Blake,David Botstein,Heather Butler,J. M. Cherry,Allan Peter Davis,Kara Dolinski,Selina S. Dwight,J.T. Eppig,Midori A. Harris,David P. Hill,Laurie Issel-Tarver,Andrew Kasarskis,Suzanna E. Lewis,John C. Matese,Joel E. Richardson,M. Ringwald,Gerald M. Rubin,Gavin Sherlock +19 more