Abstract
Copy–number alterations (CNAs) represent an important component of genetic variations and play a significant role in many human diseases. Such alterations are related to certain types of cancers, including those of the pancreas, colon, and breast, among others. CNAs have been used as biomarkers for cancer prognosis in multiple studies, but few works report on the relation of CNAs with the disease progression. In this paper, we provide cases where the inference on the disease progression improves when exploiting CNA information. To this aim, a specific dissimilarity-based representation of patients is given. The employed framework outperforms a typical approach where patients are represented through a set of available attribute values. Three datasets were employed to validate the results of our analysis.
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Bomme, L., Bardi, G., Pandis, N., Fenger, C., Kronborg, O., Heim, S.: Clonal karyotypic abnormalities in colorectal adenomas: clues to the early genetic events in the adenoma-carcinoma sequence. Genes Chrom. Cancer 10(3), 190–196 (1994)
Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proc. of the 23rd Int. Conf. on Machine Learning, ICML 2006, pp. 233–240 (2006)
Guyon, I., Gunn, S., Nikravesh, M., Zadeh, L.: Feature Extraction: Foundations and Applications. Springer (2006)
Kadota, M., Sato, M., Duncan, B., Ooshima, A., Yang, H.H., Diaz-Meyer, N., Gere, S., Kageyama, S.I., Fukuoka, J., Nagata, T., Tsukada, K., Dunn, B.K., Wakefield, L.M., Lee, M.P.: Identification of novel gene amplifications in breast cancer and coexistence of gene amplification with an activating mutation of pik3ca. Cancer Res. 69(18), 7357–7365 (2009)
Kurashina, K., Yamashita, Y., Ueno, T., Koinuma, K., Ohashi, J., Horie, H., Miyakura, Y., Hamada, T., Haruta, H., Hatanaka, H., Soda, M., Choi, Y.L., Takada, S., Yasuda, Y., Nagai, H., Mano, H.: Chromosome copy number analysis in screening for prognosis-related genomic regions in colorectal carcinoma. Cancer Sci. 99(9), 1835–1840 (2008)
Mierswa, I., Wurst, M., Klinkenberg, R., Scholz, M., Euler, T.: Yale: Rapid prototyping for complex data mining tasks. In: KDD 2006, pp. 935–940 (2006)
Pekalska, E., Duin, R.P.W.: The Dissimilarity Representation for Pattern Recognition: Foundations and Applications. In: Machine Perception and Artificial Intelligence. World Scientific Publishing Company (2005)
Reid, J.F., Gariboldi, M., Sokolova, V., Capobianco, P., Lampis, A., Perrone, F., Signoroni, S., Costa, A., Leo, E., Pilotti, S., Pierotti, M.A.: Integrative approach for prioritizing cancer genes in sporadic colon cancer. Genes Chrom. Cancer 48(11), 953–962 (2009)
Zoppis, I., Borsani, M., Gianazza, E., Chinello, C., Rocco, F., Albo, G., Deelder, A.M., van der Burgt, Y.E.M., Magni, F., Antoniotti, M., Mauri, G.: Analysis of correlation structures in renal cell carcinoma patient data. In: BIOINF, pp. 251–256. SciTe Press (2012)
Zoppis, I., Gianazza, E., Borsani, M., Chinello, C., Mainini, V., Galbusera, C., Ferrarese, C., Galimberti, G., Sorbi, S., Borroni, B., Magni, F., Antoniotti, M., Mauri, G.: Mutual information optimization for mass spectra data alignment. IEEE/ACM Trans. Comp. Biol. Bioinf. 9(3), 934–939 (2012)
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Cava, C., Zoppis, I., Gariboldi, M., Castiglioni, I., Mauri, G., Antoniotti, M. (2013). Copy–Number Alterations for Tumor Progression Inference. In: Peek, N., Marín Morales, R., Peleg, M. (eds) Artificial Intelligence in Medicine. AIME 2013. Lecture Notes in Computer Science(), vol 7885. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38326-7_16
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DOI: https://doi.org/10.1007/978-3-642-38326-7_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38325-0
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