Abstract
The increasing amount and variety of data in biosciences call for innovative methods of visualization, scientific verification, and pathway analysis. Novel approaches to biological networks and research quality control are important because of their role in development of new products, improvement, and acceleration of existing health policies and research for novel ways of solving scientific challenges. One such approach is sbv IMPROVER. It is a platform that uses crowdsourcing and verification to create biological networks with easy public access. It contains 120 networks built in Biological Expression Language (BEL) to interpret data from PubMed articles with high-quality verification available for free on the CBN database. Computable, human-readable biological networks with a structured syntax are a powerful way of representing biological information generated from high-density data. This article presents sbv IMPROVER, a crowd-verification approach for the visualization and expansion of biological networks.
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References
Medline/Pubmed resources Detailed Indexing Statistics: 1965–2014. http://www.nlm.nih.gov/bsd/index_stats_comp.html. Accessed 24 Feb 2016
Kanehisa M, Goto S (2000) KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res 28:27–30
Yong E (2012) Replication studies: bad copy. Nature 485:298–300. doi:10.1038/485298a
Ioannidis JP, Allison DB, Ball CA et al (2009) Repeatability of published microarray gene expression analyses. Nat Genet 41:149–155
Repetitive flaws (2016) Nature 529:256. http://www.nature.com/news/repetitive-flaws-1.19192
Meyer P, Alexopoulos LG, Bonk T et al (2011) Verification of systems biology research in the age of collaborative competition. Nat Biotechnol 29(9):811–815. doi:10.1038/nbt.1968
Peitsch M C (2013) sbv IMPROVER: species translation challenge open to the scientific community for submissions. American Laboratory, http://www.americanlaboratory.com/913-Technical-Articles/138841-sbv-IMPROVER-Species-Translation-Challenge-Open-to-the-Scientific-Community-for-Submissions/. Accessed 24 Feb 2016
Meyer P, Hoeng J, Rice JJ et al (2012) Industrial methodology for process verification in research (IMPROVER): toward systems biology verification. Bioinformatics 28(9):1193–1201. doi:10.1093/bioinformatics/bts116
Boue S, Fields B, Hoeng J et al (2015) Enhancement of COPD biological networks using a web-based collaboration interface. F1000Res 4:32. doi:10.12688/f1000research.5984.1
Boue S, Talikka M, Westra JW et al (2015) Causal biological network database: a comprehensive platform of causal biological network models focused on the pulmonary and vascular systems. Database (Oxford) 2015:bav030. doi:10.1093/database/bav030
Younesia E, Hofmann-Apitius M (2013) Biomarker-guided translation of brain imaging into disease pathway models. Sci Rep 3:3375. doi:10.1038/srep03375
Ansari S, Binder J, Boue S et al (2013) On crowd-verification of biological networks. Bioinform Biol Insights 7:307–325. doi:10.4137/BBI.S12932
Hoeng J, Deehan R, Pratt D et al (2012) A network-based approach to quantifying the impact of biologically active substances. Drug Discov Today 17(9–10):413–418
Sewer A, Hoeng J, Deehan R et al (2014) Systems biology approaches for compound testing. In: Hoffmann RD, Gohier A, Pospisil P (eds) Data mining in drug discovery, 1st edn. Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim, Germany
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Guryanova, S., Guryanova, A. (2017). sbv IMPROVER: Modern Approach to Systems Biology. In: Tatarinova, T., Nikolsky, Y. (eds) Biological Networks and Pathway Analysis. Methods in Molecular Biology, vol 1613. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7027-8_2
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DOI: https://doi.org/10.1007/978-1-4939-7027-8_2
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