Abstract
The massive amounts of data generated by high-throughput experiments makes modern biomedical research a data-intensive discipline, shifting the research methodology from a hypothesis-based approach to a hypothesis-free one. A formal procedure should be defined to properly design a study, understand the outcomes and plan improvements for each task performed during the experiments. Such formal approach needs the identification of a high-level conceptual model of the knowledge discovery process occurring in genome-wide studies: this is what existing computational tools lack. Starting from an epistemological model of the discovery process proposed for diagnostic reasoning, we describe how the design and execution of modern genome-wide studies can be modelled using the same framework. We show the general validity of the model, how it can be instantiated to model typical scenarios of genome-wide studies, and how we use it to develop tools aimed at building semi-automated reasoning systems.
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
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Nuzzo, A., Segagni, D., Milani, G., Rognoni, C., Bellazzi, R.: A Dynamic Query System for Supporting Phenotype Mining in Genetic Studies. Stud. Health Tech. Inf. (Medinfo 2007) 129(Pt 2), 1275–1279 (2007)
Nuzzo, A., Riva, A.: A Knowledge Management Tool for Translational Research. In: 1st AMIA Summit on Translational Bioinformatics, San Francisco, CA, p. 171 (2008)
Giardine, B., Riemer, C., Hardison, R.C., Burhans, R., Elnitski, L., Shah, P., Zhang, Y., Blankenberg, D., Albert, I., Taylor, J., Miller, W., Kent, W.J., Nekrutenko, A.: Galaxy: a platform for interactive large-scale genome analysis. Genome. Res. 15, 1451–1455 (2005)
Huang, W., Sherman, B.T., Tan, Q., Collins, J.R., Alvord, W.G., Roayaei, J., Stephens, R., Baseler, M.W., Lane, H.C., Lempicki, R.A.: The DAVID Gene Functional Classification Tool: a novel biological module-centric algorithm to functionally analyze large gene lists. Genome Biol. 8(9) R183 (2007)
Ramoni, M., Stefanelli, M., Magnani, L., Barosi, G.: An Epistemological Framework for Medical Knowledge-Based Systems. IEEE Trans. on Systems, Man and Cybernetics 22(6), 1361–1375 (1992)
Balding, J.D.: A tutorial on statistical methods for population association studies. Nature Rev. Gen. 7(10), 781–791 (2002)
Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M.A.R., Bender, D., Maller, J., Sklar, P., de Bakker, P.I.W., Daly, M.J., Sham, P.C.: PLINK: a toolset for whole genome association and population-based linkage analysis. Am. J. Hum. Genet. 81, 559–575 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Nuzzo, A., Riva, A., Stefanelli, M., Bellazzi, R. (2009). An Architecture for Automated Reasoning Systems for Genome-Wide Studies. In: Combi, C., Shahar, Y., Abu-Hanna, A. (eds) Artificial Intelligence in Medicine. AIME 2009. Lecture Notes in Computer Science(), vol 5651. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02976-9_61
Download citation
DOI: https://doi.org/10.1007/978-3-642-02976-9_61
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02975-2
Online ISBN: 978-3-642-02976-9
eBook Packages: Computer ScienceComputer Science (R0)