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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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
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