Abstract
Biomedical research has seen great advances in recent years, in great part due to the long-term aid of the ability to identify biological or genetic markers that uniquely match a given disease. Despite several successes stories, the reality is that most diseases still lack an effective way of treatment, and even diagnostic. While the emergence of –omic technologies, enabled the screening of a whole cell at the molecular level, the large quantities of data produced restricted the capability to extract valid outcomes.
In this paper, we propose an optimization model, based of mixed-integer linear programming, capable of identifying a combination of biomarkers for distinguishing between healthy and diseased samples. The model achieves this taking several individuals’ gene expression profiles, identifying the most relevant genes for differentiation and discovering the optimal combination of biomarkers that best explains the difference between both states. This model was validated on two different datasets through sampling analysis, achieving an out of sample accuracy up to 93%.
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Acknowledgments
This work is co-funded by the North Portugal Regional Operational Programme, under the “Portugal 2020”, through the European Regional Development Fund (ERDF), within project SISBI- Ref\(^{\mathrm {a}}\) NORTE-01-0247-FEDER-003381. This study was also supported by the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2013 unit and COMPETE 2020 (POCI-01-0145-FEDER-006684) and BioTecNorte operation (NORTE-01-0145-FEDER-000004) funded by European Regional Development Fund under the scope of Norte2020 - Programa Operacional Regional do Norte. Joel P. Arrais is funded by CISUC - Center for Informatics and Systems of the University of Coimbra.
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Santiago, A.M., Rocha, M., Dourado, A., Arrais, J.P. (2017). Mixed-Integer Programming Model for Profiling Disease Biomarkers from Gene Expression Studies. In: Rojas, I., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2017. Lecture Notes in Computer Science(), vol 10209. Springer, Cham. https://doi.org/10.1007/978-3-319-56154-7_6
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DOI: https://doi.org/10.1007/978-3-319-56154-7_6
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