Abstract
The importance of semantic-based methods and algorithms for the analysis and management of biological data is growing for two main reasons. From a biological side, knowledge contained in ontologies is more and more accurate and complete, from a computational side, recent algorithms are using in a valuable way such knowledge. Here we focus on semantic-based management and analysis of protein interaction networks referring to all the approaches of analysis of protein–protein interaction data that uses knowledge encoded into biological ontologies.
Semantic approaches for studying high-throughput data have been largely used in the past to mine genomic and expression data. Recently, the emergence of network approaches for investigating molecular machineries has stimulated in a parallel way the introduction of semantic-based techniques for analysis and management of network data. The application of these computational approaches to the study of microarray data can broad the application scenario of them and simultaneously can help the understanding of disease development and progress.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Barabasi AL, Oltvai ZN (2004) Network biology: understanding the cell’s functional organization. Nat Rev Genet 5(2):101–113
Cannataro M, Guzzi PH, Veltri P (2010) Protein-to-protein interactions: technologies, databases, and algorithms. ACM Comput Surv. doi:10.1145/1824795.1824796
Ciriello G et al (2012) AlignNemo: a local network alignment method to integrate homology and topology. PLoS One. doi:10.1371/journal.pone.0038107
West DB (2000) Introduction to graph theory, 2nd edn. Prentice Hall, New York
Blake JA, Bult CJ (2006) Beyond the data deluge: data integration and bio-ontologies. J Biomed Informat 39(3):314–320
Harris MA et al (2004) The gene ontology (go) database and informatics resource. Nucleic Acids Res 32:258–261
Barrell D et al (2009) The GOA database in 2009-an integrated gene ontology annotation resource. Nucleic acids Research. doi:10.1093/nar/gkn803
Huang DW, Sherman BT, Lempicki RA (2009) Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res 37(1):1–13
Alexeyenko A et al (2012) Network enrichment analysis: extension of gene-set enrichment analysis to gene networks. BMC Bioinformatics. doi:10.1186/1471-2105-13-226
Guzzi PH et al (2012) Semantic similarity analysis of protein data: assessment with biological features and issues. Brief Bioinform 13(5):569–585
Smoot ME et al (2011) Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics 27(3):431–432
Pesquita C et al (2009) Semantic similarity in biomedical ontologies. PLoS Comput Biol. doi:10.1371/journal.pcbi.1000443
Dai X et al (2014) A comprehensive semantic similarity measurement for predicting the function of gene products. J Bionanosci 8(4):287–292
Agapito G, Guzzi PH, Cannataro M (2013) Visualization of protein interaction networks: problems and solutions. BMC Bioinformatics. doi:10.1186/1471-2105-14-S1-S1
Guzzi PH, Cannataro M (2012) Cyto-sevis: semantic similarity-based visualisation of protein interaction networks. EMB-Net J doi: http://dx.doi.org/10.14806/ej.18.A.397
Cannataro M et al (2007) Using ontologies for preprocessing and mining spectra data on the grid. Future Generat Comput Syst 23(1):55–60
Smith B et al (2007) The OBO foundry: coordinated evolution of ontologies to support biomedical data integration. Nat Biotechnol 25(11):1251–1255
Popescu M, Keller JM, Mitchell JA (2006) Fuzzy measures on the gene ontology for gene product similarity. IEEE/ACM Trans Comput Biol Bioinformatics 3(3):263–274
Yu G, Li F et al (2010) GOSemSim: an R package for measuring semantic similarity among GO terms and gene products. Bioinformatics 26(7):976–978
Guzzi PH, Mina M (2012) Towards the assessment of semantic similarity analysis of protein data: main approaches and issues. ACM SIGBioinformatics Rec 2(3):17–18
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer Science+Business Media New York
About this protocol
Cite this protocol
Giuseppe, A., Milano, M. (2015). Ontology-Based Analysis of Microarray Data. In: Guzzi, P. (eds) Microarray Data Analysis. Methods in Molecular Biology, vol 1375. Humana Press, New York, NY. https://doi.org/10.1007/7651_2015_249
Download citation
DOI: https://doi.org/10.1007/7651_2015_249
Published:
Publisher Name: Humana Press, New York, NY
Print ISBN: 978-1-4939-3172-9
Online ISBN: 978-1-4939-3173-6
eBook Packages: Springer Protocols