Systems biology and cancer: Promises and perils

Progress in Biophysics and Molecular Biology

Volumn 106, Issue 2, 2011, Pages 410-413

  Baker, Stuart G ^a   Kramer, Barnett S ^a  

^a NATIONAL CANCER INSTITUTE   (United States)

Author keywords

Bioinformatics; Microarrays; Receiver operating characteristic curves; Reverse engineering

Indexed keywords

ANIMAL; BIOLOGICAL MODEL; BIOLOGY; ENGINEERING; HUMAN; METHODOLOGY; MICROARRAY ANALYSIS; NEOPLASM; RECEIVER OPERATING CHARACTERISTIC; REVIEW; SYSTEMS BIOLOGY;

ANIMALS; COMPUTATIONAL BIOLOGY; ENGINEERING; HUMANS; MICROARRAY ANALYSIS; MODELS, BIOLOGICAL; NEOPLASMS; ROC CURVE; SYSTEMS BIOLOGY;

EID: 80051634563     PISSN: 00796107     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.pbiomolbio.2011.03.002     Document Type: Review

References (26)

1. Aijo T., Landesmaki H. Learning gene regulatory networks from gene expression measurements using non-parametric molecular kinetics. Bioinformatics 2009, 25:2937-2944.
2. An G., Mi Q., Dutta-Moscato J., Vodovotz Y. Agent-based models in translational systems biology. WIREs Systems Biology and Medicine 2009, 1:159-171.
3. Ao P., Galas D., Hood L., Zhu X. Cancer as robust intrinsic state of endogenous molecular-cellular network shaped by evolution. Medial Hypotheses 2008, 70:678-684.
4. Baker S.G. Simple and flexible classification via swirls-and-ripples. BMC Bioinformatics 2010, 11:452.
5. Baker S.G. The central role of receiver operating characteristic (ROC) curves in evaluating tests for the early detection of cancer. Journal of the National Cancer Institute 2003, 95:511-515.
6. Baker S.G. Putting risk prediction in perspective: relative utility curves. Journal of the National Cancer Institute 2009, 101:1538-1542.
7. Baker S.G., Soto A.M., Sonnenschein C., Cappuccio A., Potter J.D., Kramer B.S. Plausibility of stromal initiation of epithelial cancers without a mutation in the epithelium: a computer simulation of morphostats. BMC Cancer 2009, 9.
8. Baker S.G., Kramer B.S. Identifying genes that contribute most to good classification in microarrays. BMC Bioinformatics 2006, 7:407.
9. Baker S.G., Kramer B.S. Paradoxes in carcinogenesis: new opportunities for research directions. BMC Cancer 2007, 7:151. 2007.
10. Baker S.G., Kramer B.S. Peirce, Youden, and receiver operating characteristic curves. The American Statistician 2007, 61:343-346.
11. Chuang H., Lee E., Liu Y., Lee D., Ideker T. Network-based classification of breast cancer metastasis. Molecular Systems Biology 2007, 3:140.
12. Fujita A., Patriota A.G., Sato J.R., Miyano S. The impact of measurement errors in the identification of regulatory networks. BMC Bioinformatics 2009, 10:412.
13. Laubenbacher R., Hower V., Jarrah A., Torti S.V., Shulaev V., Mendes P., Torti F.M., Akman S. A systems biology view of cancer. Biochimica et Biophysica Acta 2009, 1796:129-139.
14. Lemon G., King J.R., Byrne H.M., Jensen O.E., Shakesheff K.M. Mathematical modeling of engineered tissue growth using a multiphase porous flow mixture theory. Journal of Mathematical Biology 2006, 52:571-594.
15. Margolin A.A., Nemenman I., Basso K., Wiggins C., Stolovitizky G., Favera R.D., Califano A. ARACNE. An algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinformatics 2007, 7(Suppl. 1):57.
16. Michiels S., Koscielny S., Hill C. Prediction of cancer outcome with microarrays: a multiple random validation strategy. The Lancet 2005, 365:488-492.
17. Pittelkow Y.E., Wilson S.R. Simpler evaluation of predictions and signature stability for gene expression data. Journal of Biomedicine and Biotechnology 2009, 2009. DOI:1155/2009/587405.
18. Potter J.D. Morphostats, morphogens, microarchitecture and malignancy. National Reviews Cancer 2007, 7:464-474.
19. Rosenfeld S. Characteristics of transcriptional activity in nonlinear dynamics of genetic regulatory networks. Gene Regulation and Systems Biology 2009, 3:159-179.
20. Simon R. The use of genomics in clinical trial design. Clinical Cancer Research 2008, 14:5984-5994.
21. Smithers D.J. Cancer. An attack on cytologism. The Lancet 1962, 279:493-499.
22. Sonnenschein C., Soto A.M. Theories of carcinogenesis: an emerging perspective. Seminars in Cancer Biology 2008, 18:372-377.
23. Soto A.M., Sonnenschein C. The somatic mutation theory of cancer: growing problems with the paradigm. BioEssays 2004, 26:1097-1107.
24. Taylor I.W., Linding R., Warde-Farley D., Liu Y., Pesquita C., Faria D., Bull S., Pawson T., Morris Q., Wrana J.L. Dynamic modularity in protein interaction networks predicts breast cancer outcome. Nature Biotechnology 2009, 27:199-204.
25. Wang E., Lenferink A., O'Connor-McCourt M.O. Cancer systems biology: exploring cancer-associated genes on cellular networks. Cellular and Molecular Life Sciences 2007, 64:1752-1762.
26. Werhli A.V., Grzegorcyk M., Husmeier D. Comparative evaluation of reverse engineering gene regulatory networks with relevance networks, graphical Gaussian models and Bayesian networks. Bioinformatics 2006, 22:2523-2531.