Abstract
Proteomic-based approaches are quickly becoming a powerful and widely used technique to identify specific ”molecular signatures” in several pathologic conditions. In particular, cancer, which is one of the most challenging and socially important diseases, is currently under intensive investigation in order to overcome limitations still affecting conventional diagnostic strategies. In particular, one of the major goals in this field is the identification of reliable markers for early diagnosis, as well as for prognosis and treatment. Among cancer, breast carcinoma is the most important malignant disease for western women. A hereditary form has been identified which is related to inherited cancer-predisposing germ-line mutations. Germ-line mutations of BRCA1 gene have been identified in 15-20% of women with a family history of breast cancer and 60-80% with family history of both breast and ovarian cancer. Pathological as well as molecular profiling studies support the concept that inherited breast tumors are different forms of disease, suggesting the intriguing possibility of tailored chemopreventive and therapeutic approaches in this setting. Bioinformatics, and in particular pattern recognition learning algorithms, offer the enabling analysis tools, so the paper also discusses a software environment to conduct such data-intensive computations over Computational Grids.
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Cuda, G. et al. (2003). Proteomic Profiling of Inherited Breast Cancer: Identification of Molecular Targets for Early Detection, Prognosis and Treatment, and Related Bioinformatics Tools. In: Apolloni, B., Marinaro, M., Tagliaferri, R. (eds) Neural Nets. WIRN 2003. Lecture Notes in Computer Science, vol 2859. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45216-4_28
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DOI: https://doi.org/10.1007/978-3-540-45216-4_28
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