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ANALYSIS OF BREAST CANCER PROFILES USING BAYESIAN NETWORK MODELING

ANALYSIS OF BREAST CANCER PROFILES USING BAYESIAN NETWORK MODELING
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摘要 Breast cancer is the leading cause of cancer-related death for women in Tunisia and the prognosis of its metastasis remains a major problem for oncologists despite advances in treatment. In this work we use Bayesian networks to develop a decision support system that is based on the modeling of relationships between key signaling proteins and clinical and pathological characteristics of breast tumors and patients. Motivated by the lack of prior information on the parameters of the problem, we use the Implicit inference for the structure and parameter learning. A dataset of 84 Tunisian breast cancer patients was used and new prognosis factors were identified. The system predicts a metastasis risk for different patients by computing a score that is the joint probability of the Bayesian network using parameters estimated on the learning database. Based on the results of the developed system we identified that overexpression of ErbB2, ErbB3, bcl2 as well as of oestrogen and progesterone receptors associated with a low level of ErbB4 was the predominant profile associated with high risk of metastasis.
出处 《International Journal of Biomathematics》 2013年第3期53-66,共14页 生物数学学报(英文版)
关键词 Breast cancer profiles ErbB family learning Bayesian network Implicit inference metastasis risk. 贝叶斯网络 网络建模 乳腺癌 决策支持系统 作者 参数学习 ErbB2 孕激素受体
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  • 1D. M. Abd E1-Rehim, S. E. Pinder, C. E. Paish, J. A. Bell, R. S. Rampaul, R. W. Blarney, J. F. Robertson, R. I. Nicholson and I. O. Ellis, Expression and co-expression of the members of the epidermal growth factor receptor (EGFR) family in invasive breast carcinoma, British J. Cancer 91 (2004) 1532-1542.
  • 2S. Ahmed, S. Aloulou, M. Bibi, A. LaiMolsi', M. Nouira, L. Fatma, L. Kallel, O. Gharbi, S. Korbi, H. Khairiet and C. Kraiem, Breast cancer prognosis in nisian women: Analysis of a hospital series of 729 patients, Sante Publique 14 (2002) 231-241.
  • 3H. Akaike, Information theory and extension of the maximum likelihood principle, in Proc. Second Int. Syrup. Information Theory, Budapest (1973), pp. 26281.
  • 4M. Aubele, G. Auer, A. K. Walch, A. Munro, M. J. Atkinson, H. Braselmann, T. Fornander and J. M. Bartlett, PTK (protein tyrosine kinase)-6 and HER2 and 4, but not HER1 and 3 predict long-term survival in breast carcinomas, British J. Cancer 5 (2007) 801 807.
  • 5V. J. Bardou, G. Arpino, R. M. Elledge, C. K. Osborne and G. M. Clark, Proges- terone receptor status significantly improves outcome prediction over estrogen recep- tor status alone for adjuvant endocrine therapy in two large breast cancer databases, J. Clin. Oncol. 21(10) (2003) 1973-1979.
  • 6H. Ben Hassen, A. Masmoudi and A. Rebai, Causal inference in biomolecular path- ways using a Bayesian network approach and an implicit method, J. Theor. Biol. 4 (2008) 717 724.
  • 7H. Ben Hassen, A. Masmoudi and A. Rebai, Inference in signal transduction pathways using EM algorithm and an implicit algorithm: Incomplete data case, J. Comput. Biol. 16 (2009) 1227-1240.
  • 8I. Biche, P. Onody, S. Tozlu, K. Driouch, M. Vidaud and R. Lidereau, Prognostic value of ERBB family mRNA expression in breast carcinomas, Int. J. Cancer 106 (2003) 758 765.
  • 9L. Bouchaala, A. Masmoudi, F. Gargouri and A. Rebai, Improving algorithms for structure learning in Bayesian Networks using a new implicit score, Expert Syst. Appl. 37(7) (2010) 5470-5475.
  • 10J. P. Choi, T. H. Han and R. W. Park, A hybrid Bayesian network model for pre- dicting breast cancer prognosis, J. Korean Soe. Med. Inform. 15 (2009) 49-57.

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