Probabilistic learning of treatment trees in cancer

Author:

Yao Tsung-Hung1,Wu Zhenke1,Bharath Karthik2,Li Jinju1,Baladandayuthapani Veerabhadran1

Affiliation:

1. Department of Biostatistics, University of Michigan at Ann Arbor

2. School of Mathematical Sciences, University of Nottingham

Publisher

Institute of Mathematical Statistics

Subject

Statistics, Probability and Uncertainty,Modeling and Simulation,Statistics and Probability

Reference55 articles.

1. Mézard, M. and Montanari, A. (2009). Information, Physics, and Computation. Oxford Graduate Texts. Oxford Univ. Press, Oxford.

2. Liu, J. S. (1994). The collapsed Gibbs sampler in Bayesian computations with applications to a gene regulation problem. J. Amer. Statist. Assoc. 89 958–966.

3. CASELLA, G. and BERGER, R. L. (1990). Statistical Inference. The Wadsworth & Brooks/Cole Statistics/Probability Series. Wadsworth & Brooks/Cole Advanced Books & Software, Pacific Grove, CA.

4. ABDOLAHI, S., GHAZVINIAN, Z., MUHAMMADNEJAD, S., SALEH, M., AGHDAEI, H. A. and BAGHAEI, K. (2022). Patient-derived xenograft (PDX) models, applications and challenges in cancer research. J. Transl. Med. 20 206.

5. BALKO, J. M., MILLER, T. W., MORRISON, M. M., HUTCHINSON, K., YOUNG, C., RINEHART, C., SÁNCHEZ, V., JEE, D., POLYAK, K. et al. (2012). The receptor tyrosine kinase ErbB3 maintains the balance between luminal and basal breast epithelium. Proc. Natl. Acad. Sci. USA 109 221–226.

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