Cumulative disease progression models for cross-sectional data: A review and comparison

Biometrical Journal

Volumn 54, Issue 5, 2012, Pages 617-640

  Hainke, Katrin ^a   Rahnenführer, Jörg ^a   Fried, Roland ^a  

^a TU DORTMUND UNIVERSITY   (Germany)

Author keywords

Disease progression; Model choice; Modelling cancer progression

Indexed keywords

COMPLEX NETWORKS; DIAGNOSIS; DISEASE CONTROL; DISEASES; DRUG THERAPY; PROBABILITY DISTRIBUTIONS; VIRUSES;

BAYESIA N NETWORKS; CANCER PROGRESSION; DISEASE PROGRESSION; EARLY DIAGNOSIS; MODEL CHOICE; MODELING CANCER PROGRESSION; PATH MODELS; SIMPLE++; STATISTIC MODELING; TREE COMPONENTS;

BAYESIAN NETWORKS;

EID: 84865555627     PISSN: 03233847     EISSN: 15214036     Source Type: Journal    
DOI: 10.1002/bimj.201100186     Document Type: Article

References (26)

1. Agarwala, R., Bafna, V., Farach, M., Paterson, M. and Thorup, M. (1999). On the approximability of numerical taxonomy (fitting distances by tree metrics). SIAM Journal of Computation 28, 1073-1085.
2. Beerenwinkel, N., Eriksson, N. and Sturmfels, B. (2007). Conjunctive Bayesian networks. Bernoulli 13, 893-909.
3. Beerenwinkel, N., Knupfer, P. and Tresch, A. (2011). Learning monotonic genotype-phenotype maps. Statistical Applications in Genetics and Molecular Biology 10, article 3.
4. Beerenwinkel, N., Rahnenführer, J., Däumer, M., Hoffmann, D., Kaiser, R., Selbig, J. and Lengauer, T. (2005). Learning multiple evolutionary pathways from cross-sectional data. Journal of Computational Biology 12, 584-598.
5. Beerenwinkel, N. and Sullivant, S. (2009). Markov models for accumulating mutations. Biometrika 96, 663-676.
6. Bogojeska, J., Alexa, A., Altmann, A., Lengauer, T. and Rahnenführer, J. (2008). Rtreemix: an R package for estimating evolutionary pathways and genetic progression scores. Bioinformatics 24, 2391-2392.
7. Bunke, H. and Shearer, K. (1998). A graph distance metric based on the maximal common subgraph. Pattern Recognition Letters 19, 225-259.
8. Chernoff, H. (1952). A measure of asymptotic efficiency of tests for a hypothesis based on a sum of observations. The Annals of Mathematical Statistics 23, 493-507.
9. Desper, R., Jiang, F., Kallioniemi, O.-P., Moch, H., Papadimitriou, C. H. and Schäffer, A. A. (1999). Inferring tree models for oncogenesis from comparative genome hybridization data. Journal of Computational Biology 6, 37-52.
10. Desper, R., Jiang, F., Kallioniemi, O.-P., Moch, H., Papadimitriou, C. H. and Schäffer, A. A. (2000). Distance-based reconstruction of tree models for oncogenesis. Journal of Computational Biology 7, 789-803.
11. Edmonds, J. (1967). Optimum branchings. Journal of Research of the National Bureau of Standards B 71, 233-240.
12. Gerstung, M., Baudis, M., Moch, H. and Beerenwinkel, N. (2009). Quantifying cancer progression with conjunctive Bayesian networks. Bioinformatics 25, 2809-2815.
13. Hartung, J., Knapp, G. and Sinha, B. K. (2008). Statistical Meta-Analysis with Applications. John Wiley & Sons, Hoboken, New Jersey.
14. Hjelm, M., Höglund, M. and Lagergren, J. (2006). New probabilistic network models and algorithms for oncogenesis. Journal of Computational Biology 13, 853-865.
15. Kallioniemi, A., Kallioniemi, O.-P., Sudar, D., Rutovitz, D., Gray, J. W., Waldman, F. and Pinkel, D. (1992). Comparative genomic hybridization for molecular cytogenetic analysis of solid tumors. Science 258, 818-821.
16. Kullback, S. and Leibler, R. (1951). On information and sufficiency. The Annals of Mathematical Statistics 22, 79-86.
17. R Development Core Team (2011). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0.
18. Rachev, S. T. (1991). Probability Metrics and the Stability of Stochastic Models. John Wiley & Sons, Chichester, New York.
19. Radmacher, M. D., Simon, R., Desper, R., Taetle, R., Schäffer, A. A. and Nelson, M. A. (2001). Graph models of oncogenesis with an application to melanoma. Journal of Theoretical Biology 212, 535-548.
20. Simon, R., Desper, R., Papadimitriou, C. H., Peng, A., Alberts, D. S., Taetle, R., Trent, J. M. and Schäffer, A. A. (2000). Chromosome abnormalities in ovarian adenocarcinoma: III. Using breakpoint data to infer and test mathematical models for oncogenesis. Genes, Chromosomes & Cancer 28, 106-120.
21. Szabo, A. and Boucher, K. (2002). Estimating an oncogenetic tree when false negatives and positives are present. Mathematical Biosciences 179, 219-240.
22. Tofigh, A. (2009). Using trees to capture reticulate evolution: lateral gene transfers and cancer progression. PhD thesis, KTH School of Computer Science and Communications, Stockholm, Sweden.
23. Vogelstein, B., Fearon, E. R., Hamilton, S. R., Kern, S. E., Preisinger, A. C., Leppert, M., Nakamura, Y., White, R., Smits, A. M. and Bos, J. L. (1988). Genetic alterations during colorectal-tumor development. New England Journal of Medicine 319, 525-532.
24. von Heydebreck, A. (2003). Oncomodel: Maximum likelihood tree models for oncogenesis. R package version 0.8.
25. von Heydebreck, A., Gunawan, B. and Füzesi, L. (2004). Maximum likelihood estimation of oncogenetic tree models. Biostatistics 5, 545-556.
26. Yin, J., Beerenwinkel, N., Rahnenführer, J. and Lengauer, T. (2006). Model selection for mixtures of mutagenetic trees. Statistical Applications in Genetics and Molecular Biology 5, article 17.