Mathematical modeling for novel cancer drug discovery and development

Expert Opinion on Drug Discovery

Volumn 9, Issue 10, 2014, Pages 1133-1150

  Zhang, Ping ^a   Brusic, Vladimir ^b,c  

^a CSIRO   (Australia)

^b DANA FARBER CANCER INSTITUTE   (United States)

^c BOSTON UNIVERSITY   (United States)

Author keywords

Cancer; Computational models; Drug discovery; Mathematical modeling

Indexed keywords

ANTINEOPLASTIC AGENT;

ANTINEOPLASTIC ACTIVITY; BREAST CANCER; CANCER DIAGNOSIS; CANCER GROWTH; CANCER IMMUNOTHERAPY; CANCER MODEL; CANCER SCREENING; CLINICAL TRIAL (TOPIC); COMPUTER MODEL; DRUG DEVELOPMENT; DRUG EFFECT; DRUG INFORMATION; HIGH RISK POPULATION; HUMAN; IMMUNE RESPONSE; IMMUNE SYSTEM; KNOWLEDGE BASE; MATHEMATICAL MODEL; MOLECULARLY TARGETED THERAPY; PERSONALIZED MEDICINE; PHARMACOGENOMICS; PREDICTION; REVIEW; TUMOR GROWTH; BIOLOGICAL MODEL; BIOLOGY; BREAST NEOPLASMS; CHEMISTRY; FACTUAL DATABASE; FEMALE; PROCEDURES; RISK FACTOR;

ANTINEOPLASTIC AGENTS; BREAST NEOPLASMS; COMPUTATIONAL BIOLOGY; DATABASES, FACTUAL; DRUG DISCOVERY; FEMALE; HUMANS; MODELS, BIOLOGICAL; RISK FACTORS;

EID: 84907442845     PISSN: 17460441     EISSN: 1746045X     Source Type: Journal    
DOI: 10.1517/17460441.2014.941351     Document Type: Review

References (154)

1. Deisboeck TS, Wang Z, Macklin P, Cristini V. Multiscale cancer modeling. Annu Rev Biomed Eng 2011;13:127-55
2. Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell 2011;144:646-74
3. Quaranta V, Weaver AM, Cummings PT, Anderson AR. Mathematical modeling of cancer: the future of prognosis and treatment. Clin Chim Acta 2005;357(2):173-9
4. Deisboeck TS, Zhang L, Yoon J, Costa J. In silico cancer modeling: is it ready for prime time? Nat Clin Pract Oncol 2009;6(1):34-42
5. Byrne HM. Dissecting cancer through mathematics: from the cell to the animal model. Nat Rev Cancer 2010;10(3):221-30
6. Wang Z, Deisboeck TS. Mathematical modeling in cancer drug discovery. Drug Discov Today 2013;19(2):145-50
7. Howe D, Costanzo M, Fey P, et al. Big data: the future of biocuration. Nature 2008;455(7209):47-50
8. Costa FF. Big data in biomedicine. Drug Discov Today 2014;19(4):433-40
9. Alemani D, Pappalardo F, Pennisi M, et al. Combining cellular automata and Lattice Boltzmann method to model multiscale avascular tumor growth coupled with nutrient diffusion and immune competition. J Immunol Methods 2012;376(1-2):55-68
10. Vicini P. Multiscale modeling in drug discovery and development: future opportunities and present challenges. Clin Pharmacol Ther 2010;88(1):126-9
11. Roukos DH. Novel clinico-genome network modeling for revolutionizing genotype-phenotype-based personalized cancer care. Expert Rev Mol Diagn 2010;10(1):33-48
12. Powell AA, Talasaz AH, Zhang H, et al. Single cell profiling of circulating tumor cells: transcriptional heterogeneity and diversity from breast cancer cell lines. PLoS One 2012;7(5):e33788
13. Hakomori SI. Tumor malignancy defined by aberrant glycosylation and sphingo (glyco) lipid metabolism. Cancer Res 1996;56(23):5309-18
14. Santos CR, Schulze A. Lipid metabolism in cancer. FEBS J 2012;279(15):2610-23
15. Versteeg R. Cancer: tumours outside the mutation box. Nature 2014;506(7489):438-9
16. Gammon K. Matematical modelling: forcasting cancer. Nature 2012;491:S66-7
17. Yeoh EJ, Ross ME, Shurtleff SA, et al. Classification, subtype discovery, and prediction of outcome in pediatric acute lymphoblastic leukemia by gene expression profiling. Cancer Cell 2002;1(2):133-43
18. Yildirim MA, Goh KI, Cusick ME, et al. Drug-target network. Nat Biotechnol 2007;25(10):1119-26
19. Gottesman MM. Mechanisms of cancer drug resistance. Annu Rev Med 2002;53:615-27
20. Kumar N, Hendriks BS, Janes KA, et al. Applying computational modeling to drug discovery and development. Drug Discov Today 2006;11(17-18):806-11
21. Materi W, Wishart DS. Computational systems biology in drug discovery and development: methods and applications. Drug Discov Today 2007;12(7-8):295-303
22. Arrell DK, Terzic A. Network systems biology for drug discovery. Clin Pharmacol Ther 2010;88(1):120-5
23. Bozic I, Reiter JG, Allen B, et al. Evolutionary dynamics of cancer in response to targeted combination therapy. Elife 2013;2:e00747
24. Hornberg JJ, Bruggeman FJ, Westerhoff HV, Lankelma J. Cancer: a systems biology disease. Biosystems 2006;83(2-3):81-90
25. Bulusu KC, Tym JE, Coker EA, et al. canSAR: updated cancer research and drug discovery knowledgebase. Nucleic Acids Res 2014;42(1):D1040-7
26. Wooley J, Lin H. editor. Catalyzing inquiry at the interface of computing and biology. N. R. Council. The National Academies Press; Washington DC: 2005
27. Gibbs JB. Mechanism-based target identification and drug discovery in cancer research. Science 2000;287(5460):1969-73
28. Sander C. Genomic medicine and the future of health care. Science 2000;287(5460):1977-8
29. Noble D. Modeling the heart-from genes to cells to the whole organ. Science 2002;295(5560):1678-82
30. Rafferty JA. Mathematical models in biological theory. Am Sci 1950;4:549-67.79
31. Armitage P, Doll R. Stochastic models for carcinogenesis. Proceedings of the 4th Berkeley symposium on mathematical statistics and probability. Volume 4 University of California Press; Berkeley; 1961. p. 19-38
32. Araujo RP, McElwain DL. A history of the study of solid tumour growth: the contribution of mathematical modelling. Bull Math Biol 2004;66(5):1039-91
33. Anderson AR. A hybrid mathematical model of solid tumour invasion: the importance of cell adhesion. Math Med Biol 2005;22(2):163-86
34. Scott JG, Gerlee P, Basanta D, et al. Mathematical modeling of the metastatic process. In: Malek A, editor. Experimental metastasis: modeling and analysis. Springer, Dordrecht; 2013, p. 189-208
35. Traina TA, Theodoulou M, Feigin K, et al. Phase I study of a novel capecitabine schedule based on the Norton-Simon mathematical model in patients with metastatic breast cancer. J Clin Oncol 2008;26(11):1797-802
36. Straver ME, Glas AM, Hannemann J, et al. The 70-gene signature as a response predictor for neoadjuvant chemotherapy in breast cancer. Breast Cancer Res Treat 2010;119(3):551-8
37. Enderling H, Hahnfeldt P, Hlatky L, Almog N. Systems biology of tumor dormancy: linking biology and mathematics on multiple scales to improve cancer therapy. Cancer Res 2012;72(9):2172-5
38. Moreira J, Deutsch A. Cellular automaton models of tumor development: a critical review. Advs Complex Syst 2002;5(2):247-67
39. Mitsos A, Melas IN, Siminelakis P, et al. Identifying drug effects via pathway alterations using an integer linear programming optimization formulation on phosphoproteomic data. PLoS Comput Biol 2009;5(12):e1000591
40. Chakrabarti A, Verbridge S, Stroock AD, et al. Multiscale models of breast cancer progression. Ann Biomed Eng 2012;40(11):2488-500
41. Brusic V, Wilkins JS, Stanyon CA, Zeleznikow J. Data learning: understanding biological data. In: Merrill G, Pathak DK, editors. Knowledge sharing across biological and medical knowledge based systems: 1998 AAAI Workshop. AAAI Technical Report WS-98-04 AAAI Press, Cambridge, MA; 1998. p. 12-19
42. Murray JD. Mathematical biology. Volume 2 Springer, New York; 2002
43. Kan Z, Jaiswal BS, Stinson J, et al. Diverse somatic mutation patterns and pathway alterations in human cancers. Nature 2010;466(7308):869-73
44. Franceschini A, Szklarczyk D, Frankild S, et al. STRING v9.1: protein-protein interaction networks, with increased coverage and integration. Nucleic Acids Res 2013;41(Database issue):D808-15
45. Croft D, O'Kelly G, Wu G, et al. Reactome: a database of reactions, pathways and biological processes. Nucleic Acids Res 2011;39(Database issue):D691-7
46. Liberzon A, Subramanian A, Pinchback R, et al. Molecular signatures database (MSigDB) 3.0. Bioinformatics 2011;27(12):1739-40
47. Kotla V, Goel S, Nischal S, et al. Mechanism of action of lenalidomide in hematological malignancies. J Hematol Oncol 2009;2:36
48. Levine A. p53, the cellular gatekeeper for growth and division. Cell 1997;88(3):323-31
49. Lahav G. Oscillations by the p53-Mdm2 feedback loop. Adv Exp Med Biol 2008;641:28-38
50. Wagner J, Ma L, Rice JJ, et al. p53-Mdm2 loop controlled by a balance of its feedback strength and effective dampening using ATM and delayed feedback. Syst Biol (Stevenage) 2005;152(3):109-18
51. Ciliberto A, Novak B, Tyson JJ. Steady states and oscillations in the p53/ Mdm2 network. Cell Cycle 2005;4(3):488-93
52. Toettcher JE, Mock C, Batchelor E, et al. A synthetic-natural hybrid oscillator in human cells. Proc Natl Acad Sci USA 2010;107:17047-52
53. Kim J, Jackson T. Mechanisms that enhance sustainability of p53 pulses. PLoS ONE 2013;8(6):e65242
54. Mandinova A, Lee SW. The p53 pathway as a target in cancer therapeutics: obstacles and promise. Sci Transl Med 2011;3(64):64rv1
55. Lane DP, Cheok CF, Lain S. p53-based cancer therapy. Cold Spring Harb Perspect Biol 2010;2(9):a001222
56. Joerger AC, Fersht AR. The tumor suppressor p53: from structures to drug discovery. Cold Spring Harb Perspect Biol 2010;2(6):a000919
57. Ding Q, Zhang Z, Liu JJ, et al. Discovery of RG7388, a potent and selective p53-MDM2 inhibitor in clinical development. J Med Chem 2013;56(14):5979-83
58. Parmigiani G, Boca S, Lin J, et al. Design and analysis issues in genomewide somatic mutation studies of cancer. Genomics 2009;93:17-21
59. McCarthy PL, Owzar K, Hofmeister CC, et al. Lenalidomide after stem-cell transplantation for multiple myeloma. N Engl J Med 2012;366(19):1770-81
60. Available from: www.accessdata.fda.gov/ drugsatfda-docs/label/2009/ 021880s006s016s017lbl.pdf
61. Attarwala H. TGN1412: from discovery to disaster. J Young Pharm 2010;2(3):332-6
62. Suntharalingam G, Perry MR, Ward S, et al. Cytokine storm in a phase 1 trial of the anti-CD28 monoclonal antibody TGN1412. N Engl J Med 2006;355(10):1018-28
63. Available from: http://dtp.nci.nih.gov/ branches/npb/repository.html and www. cancer.gov/cancertopics/factsheet/NCI/ drugdiscovery
64. Lee M, Ye A, Gardino A, et al. Sequential application of anticancer drugs enhances cell death by rewiring apoptotic signaling networks. Cell 2012;149:780-94
65. Kim DS. Cancer vaccines in the immunotherapy era: rational approach. Hum Vaccin Immunother 2013;9(9):2017-18
66. De Boer RJ, Hogeweg P, Dullens HF, et al. Macrophage T lymphocyte interactions in the anti-tumor immune response: a mathematical model. J Immunol 1985;134(4):2748-58
67. Bellomo N, Preziosi L. Modelling and mathematical problems related to tumor evolution and its interaction with the immune system. Math Comp Model 2000;32(3):413-52
68. de Pillis LG, Radunskaya AE, Wiseman CL. A validated mathematical model of cell-mediated immune response to tumor growth. Cancer Res 2005;65(17):7950-8
69. Pappalardo F, Lollini PL, Castiglione F, Motta S. Modeling and simulation of cancer immunoprevention vaccine. Bioinformatics 2005;21(12):2891-7
70. Castiglione F, Toschi F, Bernaschi M, et al. Computational modeling of the immune response to tumor antigens. J Theor Biol 2005;237(4):390-400
71. Mallet DG, De Pillis LG. A cellular automata model of tumor-immune system interactions. J Theor Biol 2006;239(3):334-50
72. Kansal AR, Torquato S, Harsh IV GR, et al. Simulated brain tumor growth dynamics using a three-dimensionalcellular automaton. J Theor Biol 2000;203(4):367-82
73. Celada F, Seiden PE. A computer model of cellular interactions in the immune system. Immunol Today 1992;13(2):56-62
74. Qi AS, Zheng X, Du CY, An BS. A cellular automaton model of cancerous growth. J Theor Biol 1993;161(1):1-12
75. Dormann S, Deutsch A. Modeling of self-organized avascular tumor growth with a hybrid cellular automaton. In silico Biol 2002;2(3):393-406
76. d'Onofrio A. A general framework for modeling tumor-immune system competition and immunotherapy: mathematical analysis and biomedical inferences. Physica D 2005;208(3):220-35
77. Kuznetsov VA, Makalkin IA, Taylor MA, Perelson AS. Nonlinear dynamics of immunogenic tumors: parameter estimation and global bifurcation analysis. Bull Math Biol 1994;56(2):295-321
78. Palladini A, Nicoletti G, Pappalardo F, et al. In silico modeling and in vivo efficacy of cancer-preventive vaccinations. Cancer Res 2010;70(20):7755-63
79. Sanga S, Frieboes HB, Zheng X, et al. Predictive oncology: a review of multidisciplinary, multiscale in silico modeling linking phenotype, morphology and growth. Neuroimage 2007;37:S120-34
80. Pappalardo F, Halling-Brown MD, Rapin N, et al. ImmunoGrid, an integrative environment for large-scale simulation of the immune system for vaccine discovery, design and optimization. Brief Bioinform 2009;10(3):330-40
81. Evans WE, Relling MV. Moving towards individualized medicine with pharmacogenomics. Nature 2004;429(6990):464-8
82. Jain KK. Role of pharmacoproteomics in the development of personalized medicine. Pharmacogenomics 2004;5(3):331-6
83. Guo Y, Xiao P, Lei S, et al. How is mRNA expression predictive for protein expression? A correlation study on human circulating monocytes. Acta Biochim Biophys Sin (Shanghai) 2008;40(5):426-36
84. Holmes E, Wilson ID, Nicholson JK. Metabolic phenotyping in health and disease. Cell 2008;134(5):714-17
85. Kitano H. Systems biology: a brief overview. Science 2002;295(5560):1662-4
86. Kreeger PK, Lauffenburger DA. Cancer systems biology: a network modeling perspective. Carcinogenesis 2010;31(1):2-8
87. Zeger SL, Liang KY. Longitudinal data analysis for discrete and continuous outcomes. Biometrics 1986;121-30
88. van Kessel AG. Changing landscapes in cellular oncology. Cell Oncol 2012;1-
89. Anderson AR, Quaranta V. Integrative mathematical oncology. Nat Rev Cancer 2008;8(3):227-34
90. Frieboes HB, Zheng X, Sun CH, et al. An integrated computational/ experimental model of tumor invasion. Cancer Res 2006;66(3):1597-604
91. Frieboes HB, Edgerton ME, Fruehauf JP, et al. Prediction of drug response in breast cancer using integrative experimental/computational modeling. Cancer Res 2009;69(10):4484-92
92. Laird AK. Dynamics of tumor growth. Br J Cancer 1964;13:490-502
93. Citron ML, Berry DA, Cirrincione C, et al. Randomized trial of dose-dense versus conventionally scheduled and sequential versus concurrent combination chemotherapy as postoperative adjuvant treatment of node-positive primary breast cancer: first report of Intergroup Trial C9741/Cancer and Leukemia Group B Trial 9741. J Clin Oncol 2003;21:1431-9
94. Iliadis A, Barbolosi D. Optimizing drug regimens in cancer chemotherapy by an efficacy-toxicity mathematical model. Comput Biomed Res 2000;33(3):211-26
95. Hulka BS. Overview of biological markers. In: Hulka BS, Griffith JD, Wilcosky TC, editors. Biological markers in epidemiology. Oxford University Press; New York: 1990. p. 3-15
96. Alonso A, Molenberghs G, Renard D, Darstein C. Evaluation of biomarkers for pharmacological activity. J Biopharm Stat 2009;19(2):256-72
97. Cole KD, He HJ, Wang L. Breast cancer biomarker measurements and standards. Proteomics Clin Appl 2013;7(1-2):17-29
98. Al-Hajj M, Wicha MS, Benito-Hernandez A, et al. Prospective identification of tumorigenic breast cancer cells. Proc Natl Acad Sci USA 2003;100(7):3983-8
99. Cheang MC, Voduc D, Bajdik C, et al. Basal-like breast cancer defined by five biomarkers has superior prognostic value than triple-negative phenotype. Clin Cancer Res 2008;14(5):1368-76
100. van't Veer LJ, Dai H, Van De Vijver MJ, et al. Gene expression profiling predicts clinical outcome of breast cancer. Nature 2002;415(6871):530-6
101. Cristofanilli M, Budd GT, Ellis MJ, et al. Circulating tumor cells, disease progression, and survival in metastatic breast cancer. N Engl J Med 2004;351(8):781-91
102. Woodcock J, Woosley R. The FDA critical path initiative and its influence on new drug development. Annu Rev Med 2008;59:1-12
103. Costantino JP, Gail MH, Pee D, et al. Validation studies for models projecting the risk of invasive and total breast cancer incidence. J Natl Cancer Inst 1999;91(18):1541-8
104. Armitage P, Doll R. The age distribution of cancer and a multistage theory of carcinogenesis. Br J Cancer 1954;8:1-12
105. Moolgavkar SH, Day NE, Stevens RG. Two-stage model for carcinogenesis: epidemiology of breast cancer in females. J Natl Cancer Inst 1980;65(3):559-69
106. Colditz GA, Rosner BA. What can be learnt from models of incidence rates? Breast Cancer Res 2006;8(3):208
107. Gøtzsche PC, Nielsen M. Screening for breast cancer with mammography. Cochrane Database Syst Rev 2011(1):CD001877
108. Verma B, Zakos J. A computer-Aided diagnosis system for digital mammograms based on fuzzy-neural and feature extraction techniques. IEEE Trans Inf Technol Biomed 2001;5(1):46-54
109. Rangayyan RM, Ayres FJ, Desautel JL. A review of computer-Aided diagnosis of breast cancer: Toward the dection of subtle signs. J Franklin Inst 2006;344(3-4):312-48
110. Zhang P, Verma B, Kumar K. Neural vs. statistical classifier in conjunction with genetic algorithm based feature selection. Pattern Recognit Lett 2005;26(7):909-19
111. Kriege M, Brekelmans CT, Boetes C, et al. Efficacy of MRI and mammography for breast-cancer screening in women with a familial or genetic predisposition. N Engl J Med 2004;351(5):427-37
112. Dorrius M, Jansen-van der Weide M, van Ooijen P, et al. Computer-Aided detection in breast MRI: a systematic review and meta-Analysis. Eur Radiol 2011;21(8):1600-8
113. Zhang P, Kumar K, Verma B. A hybrid classifier for mass classification with different kinds of features in mammography. LNAI 2005;3614:316-19
114. Tan H, Li F, Singh J, et al. A 3-dimentional multiscale model to simulate tumor progression in response to interactions between cancer stem cells and tumor microenvironmental factors. IEEE 6th International Conference on Systems Biology (ISB); IEEE; 2012. p. 297-303
115. Dunn A. Hierarchical cellular automata methods. In: Hoekstra AG, Kroc J, Sloot PM, editors. Simulating complex systems by cellular automata. Springer-Verlag, Berlin; 2010. p. 59-80
116. Nelson C, Inman J, Bissell M. Three-dimensional lithographically defined organotypic tissue arrays for quantitative analysis of morphogenesis and neoplastic progression. Nat Protoc 2008;3:674-8
117. Shumate S, El-Shenawee M. Computational model of ductal carcinoma in situ: the effects of contact inhibition on pattern formation. IEEE Trans Biomed Eng 2009;56(5):1341-7
118. Norton KA, Namazi S, Barnard N, et al. Automated reconstruction algorithm for identification of 3D architectures of cribriform ductal carcinoma in situ. PLoS One 2012;7(9):e44011
119. Franks LB. Mathematical modelling of comedo ductal carcinoma in situ of the breast. Math Med Biol 2003;20:277-308
120. Franks SJ, Byrne HM, Underwood JC, Lewis CE. Biological inferences from a mathematical model of comedo ductal carcinoma in situ of the breast. J Theor Biol 2005;232:523-43
121. Pham K, Frieboes HB, Cristini V, Lowengrub J. Predictions of tumour morphological stability and evaluation against experimental observations. J R Soc Interface 2011;8(54):16-29
122. Macklin P, Edgerton ME, Thompson AM, Cristini V. Patient-calibrated agent-based modelling of ductal carcinoma in situ (DCIS): from microscopic measurements to macroscopic predictions of clinical progression. J Theor Biol 2012;301:122-40
123. Xu Y, Gilbert R. Some inverse problems raised from a mathematical model of ductal carcinoma in situ. Math Comp Model 2009;49(3-4):814-28
124. Michor F, Hughes TP, Iwasa Y, et al. Dynamics of chronic myeloid leukaemia. Nature 2005;435(7046):1267-70
125. Simon R, Norton L. The Norton-Simon hypothesis: designing more effective and less toxic chemotherapeutic regimens. Nat Clin Pract Oncol 2006;3(8):406-7
126. Gompertz B. On the nature of the function expressive of the law of human mortality, and on a new method of determining the value of life contingencies. Phil Trans R Soc Lond 1825;115:513-85
127. Winsor CP. The Gompertz curve as a growth curve. Proc Natl Acad Sci USA 1932;18(1):1-8
128. Norton L. A Gompertzian model of human breast cancer growth. Cancer Res 1988;48(24 Part 1):7067-71
129. Montgomery DC, Montgomery DC. Design and analysis of experiments. Volume 7 Wiley; New York: 1984
130. McGuire MF, Enderling H, Wallace DI, et al. Formalizing an integrative, multidisciplinary cancer therapy discovery workflow. Cancer Res 2013;73(20):6111-17
131. Yang J, Zhao H, Garnett C, et al. Combination of exposure-response and case-control analyses in regulatory decision making. J Clin Pharmacol 2013;53:160-6
132. Akhoon BA, Gupta SK, Verma V, et al. In silico designing and optimization of anti-breast cancer antibody mimetic oligopeptide targeting HER-2 in women. J Mol Graph Model 2010;28(7):664-9
133. Gorelik B, Ziv I, Shohat R, et al. Efficacy of weekly docetaxel and bevacizumab in mesenchymal chondrosarcoma: a new theranostic method combining xenografted biopsies with a mathematical model. Cancer Res 2008;68:9033-40
134. Sokolov A, Funk C, Graim K, et al. Combining heterogeneous data sources for accurate functional annotation of proteins. BMC Bioinformatics 2013;14(Suppl 3):S10
135. Mostafavi S, Morris Q. Fast integration of heterogeneous data sources for predicting gene function with limited annotation. Bioinformatics 2010;26(14):1759-65
136. Al-Lazikani B, Banerji U, Workman P. Combinatorial drug therapy for cancer in the post-genomic era. Nat Biotechnol 2012;30(7):679-92
137. Hopkins AL. Network pharmacology: the next paradigm in drug discovery. Nat Chem Biol 2008;4(11):682-90
138. Chuang HY, Lee E, Liu YT, et al. Network-based classification of breast cancer metastasis. Mol Syst Biol 2007;3:140
139. Tagore S, Chowdhury N, De RK. Analyzing methods for path mining with applications in metabolomics. Gene 2014;534(2):125-38
140. Tian Q, Price ND, Hood L. Systems cancer medicine: towards realization of predictive, preventive, personalized and participatory (P4) medicine. J Intern Med 2012;271:111-21
141. Schwab ED, Pienta KJ. Cancer as a complex adaptive system. Med Hypotheses 1996;47(3):235-41
142. Mattfeldt T. Nonlinear deterministic analysis of tissue texture: a stereological study on mastopathic and mammary cancer tissue using chaos theory. J Microsc 1997;185(Pt 1):47-66
143. Letellier C, Denis F, Aguirre LA. What can be learned from a chaotic cancer model? J Theor Biol 2013;322:7-16
144. Fernández-Suárez XM, Rigden DJ, Galperin MY. The 2014 Nucleic Acids Research Database Issue and an updated NAR online Molecular Biology Database Collection. Nucleic Acids Res 2014;42(1):D1-6
145. NCBI Resource Coordinators. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res 2014;42(1):D7-D17
146. Chen J, Zhang D, Yan W, et al. Translational bioinformatics for diagnostic and prognostic prediction of prostate cancer in the next-generation sequencing era. Biomed Res Int 2013;2013:901578
147. Trinh QM, Jen FY, Zhou Z, et al. Cloud-based uniform ChIP-Seq processing tools for modENCODE and ENCODE. BMC Genomics 2013;14:494
148. O'Connor BD, Merriman B, Nelson SF. SeqWare Query Engine: storing and searching sequence data in the cloud. BMC Bioinformatics 2010;11(Suppl 12):S2
149. Gail MH, Brinton LA, Byar DP, et al. Projecting individualized probabilities of developing breast cancer for white females who are being examined annually. J Natl Cancer Inst 1989;81:1879-86
150. Claus EB, Risch N, Thompson WD, et al. Autosomal dominant inheritance of early-onset breast cancer. Implications for risk prediction. Cancer 1994;73:643-51
151. Berry DA, Iversen ES, Gudbjartsson DF, et al. BRCAPRO validation, sensitivity of genetic testing of BRCA1/BRCA2, and prevalence of other breast cancer susceptibility genes. J Clin Oncol 2002;20:2701-12
152. Antoniou AC, Cunningham AP, Peto J, et al. The BOADICEA model of genetic susceptibility to breast and ovarian cancers: updates and extensions. Br J Cancer 2008;98:1457-66
153. Tyrer J, Duffy SW, Cuzick J, et al. A breast cancer prediction model incorporating familial and personal risk factors. Stat Med 2004;23:1111-30
154. Singletary SE. Rating the risk factors for breast cancer. Ann Surg 2003.237(4):474-82; Available from: www.cancer.org/cancer/breastcancer