Early Diagnosis of Complex Diseases by Molecular Biomarkers, Network Biomarkers, and Dynamical Network Biomarkers

Medicinal Research Reviews

Volumn 34, Issue 3, 2014, Pages 455-478

  Liu, Rui ^a   Wang, Xiangdong ^b   Aihara, Kazuyuki ^c   Chen, Luonan ^c,d  

^a SOUTH CHINA UNIVERSITY OF TECHNOLOGY   (China)

^b FUDAN UNIVERSITY   (China)

^c INSTITUTE OF INDUSTRIAL SCIENCE   (Japan)

^d Shanghai Jiao Tong University Affiliated Sixth People s Hospital   (China)

Author keywords

Complex diseases; Dynamical network biomarker; Early diagnosis; Molecular biomarker; Network biomarker

Indexed keywords

BIOLOGICAL MARKER; MOLECULAR MARKER;

ARTICLE; CLINICAL PRACTICE; EARLY DIAGNOSIS; FALSE NEGATIVE RESULT; FALSE POSITIVE RESULT; GENOTYPE; HIGH THROUGHPUT SCREENING; HUMAN; NONHUMAN; PHENOTYPE; THEORY;

COMPLEX DISEASES; DYNAMICAL NETWORK BIOMARKER; EARLY DIAGNOSIS; MOLECULAR BIOMARKER; NETWORK BIOMARKER;

BIOLOGICAL MARKERS; DISEASE; DISEASE PROGRESSION; EARLY DIAGNOSIS; HUMANS;

EID: 84897433296     PISSN: 01986325     EISSN: 10981128     Source Type: Journal    
DOI: 10.1002/med.21293     Document Type: Article

References (141)

1. Venegas JG, Winkler T, Musch G, Vidal Melo MF, Layfield D, Tgavalekos N, Fischman AJ, Callahan RJ, Bellani G, Harris RS. Self-organized patchiness in asthma as a prelude to catastrophic shifts. Nature 2005;434:777-782.
2. McSharry PE, Smith LA, Tarassenko L. Prediction of epileptic seizures: Are nonlinear methods relevant? Nat Med 2003;9:241-242.
3. Roberto PB, Eliseo G, Josef C. Transition models for change-point estimation in logistic regression. Stat Med 2003;22:1141-1162.
4. Paek SH, Chung HT, Jeong SS, Park CK, Kim CY, Kim JE, Kim DG, Jung HW. Hearing preservation after gamma knife stereotactic radiosurgery of vestibular schwannoma. Cancer 2005;104:580-590.
5. Liu JK, Rovit RL, Couldwell WT. Pituitary apoplexy. Semin Neurosurg 2001;12:315-320.
6. Biomarkers Definitions Working Group. Biomarkers and surrogate endpoints: Preferred definitions and conceptual framework. Clin. Pharmavcol Ther 2001;69:89-95.
7. Wang XD, Ward PA. Opportunities and challenges of disease biomarkers: A new section in the journal of translational medicine. J Transl Med 2012;10:220, 1-4.
8. Ptolemy AS, Rifai N. What is a biomarker? Research investments and lack of clinical integration necessitate a review of biomarker terminology and validation schema. Scand Clin Lab Invest 2010;70(Suppl 242):6-14.
9. Ransohoff DF. How to improve reliability and efficiency of research about molecular markers: Roles of phases, guidelines, and study design. J Clin Epidemiol 2007;60:1205-1219.
10. Rhodes DR, Sanda MG, Otte AP, Chinnaiyan AM, Rubin MA. Multiplex biomarker approach for determining risk of prostate-specific antigen-defined recurrence of prostate cancer. J Natl Cancer Inst 2003;9:661-668.
11. Mikolajczyk SD, Song Y, Wong JR, Matson RS, Rittenhouse HG. Are multiple markers the future of prostate cancer diagnostics? Clin Biochem 2004;37:519-528.
12. Huang Y, Isharwal S, Haese A, Chun FK, Makarov DV, Feng Z, Han M, Humphreys E, Epstein JI, Partin AW, Veltri RW. Prediction of patient-specific risk and percentile cohort risk of pathological stage using continuous PSA measurement, clinical stage and biopsy Gleason score. BJU Int 2011;107(10):1562-1569.
13. Berchuck A. Biomarkers in the ovary. J Cell Biochem 1995;23:223-226.
14. Rhodes DR, Sanda MG, Otte AP, Chinnaiyan AM, Rubin MA. Shaping genetic alterations in human cancer: The p53 mutation paradigm. Cancer Cell 2007;12:303-312.
15. Landgren O, Weiss BM. Patterns of monoclonal gammopathy of undetermined significance and multiple myeloma in various ethnic/racial groups: Support for genetic factors in pathogenesis. Leukemia 2009;23:1691-1697.
16. Jin G, Zhou X, Wang H, Zhao H, Cui K, Zhang X-S, Chen L, Hazen AL, Li K, Wong STC. The knowledge-integrated network biomarkers discovery for major adverse cardiac events. J Proteome Res 2008;7:4013-4021.
17. Ideker T, Sharan R. Protein networks in disease. Genome Res 2008;18:644-652.
18. Chen L, Liu R, Liu Z, Li M, Aihara K. Detecting early-warning signals for sudden deterioration of complex diseases by dynamical network biomarkers. Sci Rep 2012;2:342, 1-8. doi: 10.1038/srep00342.
19. Liu R, Li M, Liu Z, Wu J, Chen L, Aihara K. Identifying critical transitions and their leading networks for complex diseases. Sci Rep 2013;2:813, 1-9. doi: 10.1038/srep00813.
20. Cook IA, Hunter AM, Abrams M, Siegman B, Leuchter AF. Midline and right frontal brain function as a physiologic biomarker of remission in major depression. Psychiatry Res Neuroimag 2009;174:152-157.
21. Atkinson C, Warren R, Bingham SA, Day NE. Mammographic patterns as a predictive biomarker of breast cancer risk: Effect of tamoxifen. Cancer Epidemiol Biomark Prev 1999;8:863-866.
22. Cristofanilli M, Budd GT, Ellis MJ, Stopeck A, Matera J, Miller MC, Reuben JM, Doyle GV, Allard WJ, Terstappen L, Hayes DF. Circulating tumor cells, disease progression, and survival in metastatic breast cancer. N Engl J Med 2004;351:781-791.
23. Hernandez J, Thompson IM. Prostate-specific antigen: A review of the validation of the most commonly used cancer biomarker. Am Cancer Soc 2004;894-904.
24. Allhoff EP, Proppe KH, Chapman CM, Lin CW, Prout GR, Jr,. Evaluation of prostate specific acid phosphatase and prostate specific antigen in identification of prostatic cancer. J Urol 1983;129:315-318.
25. Hirata Y, Bruchovsky N, Aihara K. Development of a mathematical model that predicts the outcome of hormone therapy for prostate cancer. J Theor Biol 2010;264:517-527.
26. Poste G, Carbone DP, Parkinson DR, Verweij J, Hewitt SM, Jessup JM. Leveling the playing field: Bringing development of biomarkers and molecular diagnostics up to the standards for drug development. Clin Cancer Res 2012;18(6):1515-1523.
27. Duffy MJ, Crown J. A personalized approach to cancer treatment: How biomarkers can help. Clin Chem 2008;54(11):1770-1779.
28. Pepe MS, Etzioni R, Feng Z, John D, Potter Thompson ML, Thornquist M, Winget M, Yasui Y. Phases of biomarker development for early detection of cancer. J Natl Cancer Inst 2001;93(14):1054-1061.
29. Pepe MS, Feng Z, Janes H, Bossuyt PM, Potter JD. Pivotal evaluation of the accuracy of a biomarker used for classification or prediction: Standards for study design. J Natl Cancer Inst 2008;100:1432-1438.
30. Betsou F, Barnes R, Burke T, Coppola D, DeSouza Y, Eliason J, Glazer B, Horsfall D, Kleeberger C, Lehmann S, Prasad A, Skubitz A, Somiari S, Gunter E. Human biospecimen research: Experimental protocol and quality control tools. Cancer Epidemiol Biomarkers Prev 2009;18(4):1017-1025.
31. Rouzier R, Perou CM, Symmans WF, Ibrahim N. Breast cancer molecular subtypes respond differently to preoperative chemotherapy. Clin Cancer Res 2005;11:5678-5685.
32. Hosmer DW, Lemeshow S. Applied Logistic Regression. New York: Wiley; 1989.
33. Lette J, Colletti BW, Cerino M, MeNamara D, Eybalin MC, Levasseur A, Nattel S. Artificial intelligence versus logistic regression statistical modeling to predict cardiac complications after non-cardiac surgery. Clin Cardiol 1994, 17:609-614.
34. Skates SJ, Xu FJ, Yu YH, Sjovall K, Einhorn N, Chang Y, Bast RC, Jr., Knapp RC. Toward an optimal algorithm for ovarian cancer screening with longitudinal tumor markers. Cancer 1995, 76:2004-2010.
35. Bhatavdekar JM, Patel DD, Shah NG, Vora HH, Suthar TP, Chikhlikar PR, Ghosh N, Trivedi TI. Prognostic significance of immunohistochemically localized biomarkers in stage II and stage III breast cancer: A multivariate analysis. Ann Surg Oncol 2000;7:305-311.
36. Diamandis EP. Tumor Markers: Physiology, Pathobiology, Technology, and Clinical Applications. Washington, DC: American Association for Clinical Chemistry Press; 2002.
37. Etzioni R, Kooperberg C, Pepe M, Smith R, Gann PH. Combining biomarkers to detect disease with application to prostate cancer. Biostatistics 2003;4(4):523-538.
38. Potter SR, Miller MC, Mangold LA, Jones KA, Epstein JI, Veltri RW, Partin AW. Genetically engineered neural networks for predicting prostate cancer progression after radical prostatectomy. Urology 1999;54:791-795.
39. Veltri RW, Manisha C, Miller MC, Poole EC, O'Dowd GJ, Partin AW. Comparison of logistic regression and neural net modeling for prediction of prostate cancer pathologic stage. Clin Chem 2002;48(10):1828-1834.
40. Xue X, Kim MY, Gaudet MM, Park Y, Heo M, Hollenbeck AR, Strickler HD, Gunter MJ. A comparison of the polytomous logistic regression and joint cox proportional hazards models for evaluating multiple disease subtypes in prospective cohort studies. Cancer Epidemiol Biomarkers Prev 2013;22(2):275-285. doi:10.1158/1055-9965.EPI-12-1050.
41. Mullera R, Mockel M. Logistic regression and CART in the analysis of multimarker studies. Lancet Neurol 2008;394:1-6.
42. Vlahou A, Schorge JO, Gregory BW, Coleman RL. Diagnosis of ovarian cancer using decision tree classification of mass spectral data. J Biomed Biotechnol 2003;5:308-314.
43. Breiman L, Friedman JH. Estimating optimal transformations for multiple regression and correlation. J Am Statist Assoc 1985;80:590-598.
44. Li Y, John M, Zhou X, Kim Y, Sinha U, Jordan R, Eisele D, Abemayor E, Elashoff D, Park NH, Wong DT. Salivary transcriptome diagnostics for oral cancer detection. Clin Cancer Res 2004;10:8442-8450.
45. Veltri RW, Isharwal S, Miller MC, Epstein JI, Mangold LA, Humphreys E, Partin AW. Long-term assessment of prostate cancer progression free survival: Evaluation of pathological parameters, nuclear shape and molecular biomarkers of pathogenesis. Prostate 2008;68:1806-1815.
46. Zlobec I, Steele R, Nigam N, Compton CC. A predictive model of rectal tumor response to preoperative radiotherapy using classification and regression tree methods. Clin Cancer Res 2005;11:5440-5443.
47. Yu Y, Chen S, Wang LS, Chen WL, Guo WJ, Yan H, Zhang WH, Peng CH, Zhang SD, Li HW, Chen GQ. Prediction of pancreatic cancer by serum biomarkers using surface-enhanced laser desorption/ionization-based decision tree classification. Oncology 2005;68:79-86.
48. Barnholtz-Sloan JS, Guan X, Zeigler-Johnson C, Meropol NJ, Rebbeck TR. Decision tree-based modeling of androgen pathway genes and prostate cancer risk. Cancer Epidemiol Biomarkers Prev 2011;20(6):1146-1155.
49. Oluwadara O, Chiappelli F. Biomarkers for early detection of high risk cancers: From gliomas to nasopharyngeal carcinoma. Bioinformation 2009;3:332-339.
50. Robin X, Turck N, Hainard A, Lisacek F, Sanchez JC, Muller M. Bioinformatics for protein biomarker panel classification: What is needed to bring biomarker panels into in vitro diagnostics? Expert Rev Proteomics 2009;6:675-689.
51. Woolas RP, Xu FJ, Jacobs IJ Yu YH, Daly L, Berchuck A, Soper JT, Clarke-Pearson DL, Oram DH, Bast RC, Jr., Elevation of multiple serum markers in patients with stage I ovarian cancer. J Natl Cancer Inst 1993;85:1748-1751.
52. Woolas RP, Conaway MR, Xu F, Jaeobs IJ, Yu Y, Daly L, Davies AP, O'Briant K, Berchuck A, Soper JT, Clarke-Pearson DL, Rodriguez G, Oram DH, Bast Jr., RC. Combinations of multiple serum markers are superior to individual assays for discriminating malignant from benign pelvic masses. Gynecol Oncol 1995;59:111-116.
53. Burczynski ME, Peterson RL, Twine NC, Zuberek KA, Brodeur BJ, Casciotti L, Maganti V, Reddy PS, Strahs A, Immermann F, Spinelli W, Schwertschlag U, Slager AM, Cotreau MM, Dorner AJ. Molecular classification of Crohn's disease and ulcerative colitis patients using transcriptional profiles in peripheral blood mononuclear cells. J Mol Diagn 2006;8:51-61.
54. Mian S, Ball G, Hornbuckle J, Holding F, Carmichael J, Ellis I, Ali S, Li G, McArdle S, Creaser C, Ree R. A prototype methodology combining surface-enhanced laser desorption/ionization protein chip technology and artificial neural network algorithms to predict the chemoresponsiveness of breast cancer cell lines exposed to Paclitaxel and Doxorubicin under in vitro conditions. Proteomics 2003;3:1725-1737.
55. Ball G, Mian S, Holding F, Allibone RO, Lowe J, Ali S, Li G, McCardle S, Ellis IO, Creaser C, Rees RC. An integrated approach utilizing artificial neural networks and SELDI mass spectrometry for the classification of human tumours and rapid identification of potential biomarkers. Bioinformatics 2002;18:395-404.
56. Wei JT, Zhang Z, Barnhill SD, Madyastha KR, Zhang H, Oesterling JE. Understanding artificial neural networks and exploring their potential applications for the practicing urologist. Urology 1998;52:161-172.
57. Rodvold DM, McLeod DG, Brandt JM, Snow PB, Murphy GP. Introduction to artificial neural networks for physicians: Taking the lid off the black box. Prostate 2001;46:39-44.
58. Babaian RJ, Zhang Z. Computer-assisted diagnostics: Application to prostate cancer. Mol Urol 2001;5:175-180.
59. Rumelhart DE, McCelland JL. Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Cambridge, MA: MIT Press; 1995.
60. Su AI, Welsh JB, Sapinoso LM, Kern SG, Dimitrov P, Lapp H, Schultz PG, Powell SM, Moskaluk CA, Frierson Jr., HF, Hampton GM. Molecular classification of human carcinomas by use of gene expression signatures. Cancer Res 2001;61:7388-7393.
61. Vapnik VN. In: Haykin S, Ed. Statistical Learning Theory. The support vector method for estimating indicator functions. New York: John Wiley & Sons; 1998. p 401-441.
62. Burges CJC. A tutorial on support vector machines for pattern recognition. Data Min Knowl Discov 1998, 2:121-167.
63. Chow ML, Moler EJ, Mian IS. Identifying marker genes in transcription profiling data using a mixture of feature relevance experts. Physiol Genomics 2001;5:99-111.
64. Sanchez-Carbayo M, Socci ND, Lozano J, Li W, Charytonowicz E, Belbin T, Prystowsky MB, Ortiz AR Childs G, Cordon-Cardo C. Gene discovery in bladder cancer progression using cDNA microarrays. Am J Pathol 2003;163:505-516.
65. Lee Y, Lee C. Classification of multiple cancer types by multicategory support vector machines using gene expression data. Bioinformatics 2002;19:1132-1139.
66. Dolled-Filhart M, Ryden L, Cregger M, Jirstrom K, Harigopal M, Camp RL, Rimm DL. Classification of breast cancer using genetic algorithms and tissue microarrays. Clin Cancer Res 2006;12:6459-6468.
67. Liu JJ, Cutler G, Li W, Pan Z, Peng S, Hoey T, Chen L, Ling XB. Multiclass cancer classification and biomarker discovery using GA-based algorithms. Bioinformatics 2005;21:2691-2697.
68. Bailey WJ, Ulrich R. Molecular profiling approaches for identifying novel biomarkers. Expert Opin Drug Saf 2004;3:137-151.
69. Petricoin EF, Ardekani A, Hitt B, Levine P, Fusarob VA, Steinberg S, Mills G, Simone C, Fishman D, Kohn E, Liotta L. Use of proteomic patterns in serum to identify ovarian cancer. Lancet 2002;359:572-577.
70. Adam B, Qu Y, Davis JW, Ward MD, Clements MA, Cazares LH, Semmes OJ, Schellhammer PF, Yasui Y, Feng Z, Wright G. Serum protein fingerprinting coupled with a pattern-matching algorithm distinguishes prostate cancer from benign prostate hyperplasia and healthy men. Cancer Res 2002;62:3609-3614.
71. Hilario M, Kalousis A. Approaches to dimensionality reduction in proteomic biomarker studies. Brief Bioinform 2008;9(2):102-118. doi: 10.1093/bib/bbn005.
72. Viswanath S, Madabhushi A. Consensus embedding: Theory, algorithms and application to segmentation and classification of biomedical data. BMC Bioinformatics 2012;13:26, 1-20.
73. Janes H, Pepe MS, Gu W. Assessing the value of risk predictions by using risk stratification tables. Ann Intern Med 2008;149:751-760.
74. Apple FS, Wu AHB, Mair J, Ravkilde J, Panteghini M, Tate J, Pagani F, Christenson RH, Mockel M, Danne O, Jaffe AS. Future biomarkers for detection of ischemia and risk stratification in acute coronary syndrome. Clin Chem 2005;51(5):810-824.
75. Taylor JMG, Ankerst DP, Andridge RR. Validation of biomarker-based risk prediction models. Clin Cancer Res 2008;14(19):5977-5983.
76. Xu QW, Zhao W, Wang Y, Sartor MA, Han DM, Deng J, Ponnala R, Yang JY, Zhang QY, Liao GQ, Qu YM, Li L, Liu FF, Zhao HM, Yin YH, Chen WF, Zhang Y, Wang XS. An integrated genome-wide approach to discover tumor-specific antigens as potential immunologic and clinical targets in cancer. Cancer Res 2012;72(24):6351-6361.
77. Xu QW, Zhang Y, Wang XS. HEPA and PARSE: Systematic discovery of clinically relevant tumor-specific antigens. OncoImmunology 2013;2(3):e23249, 1-3.
78. van't Veer LJ, Dai H, van de Vijver MJ, He YD, Hart AAM, Mao M, Peterse HL, van der Kooy K, Marton MJ, Witteveen AT, Schreiber GJ, Kerkhoven RM, Roberts C, Linsley PS, Bernards R, Friend SH. Gene expression profiling predicts clinical outcome of breast cancer. Nature 2002;415:530-536.
79. Hedenfalk I, Duggan D, Chen Y, Radmacher M, Bittner M, Simon R, Meltzer P, Gusterson B, Esteller M, Raffeld M, Yakhini Z, Ben-Dor A, Dougherty E, Kononen J, Bubendorf L, Fehrle W, Pittaluga S, Gruvberger S, Loman N, Johannsson O, Olsson H, Wilfond B, Sauter G, Kallioniemi OP, Borg Å, Trent J. Gene-expression profiles in hereditary breast cancer. N England J Med 2001;344:539-548.
80. Mischel PS, Cloughesy TF, Nelson SF. DNA-microarray analysis of brain cancer: Molecular classification for therapy. Nat Rev Neurosci 2004;5:782-792.
81. Jimeno A, Hidalgo M. Molecular biomarkers: Their increasing role in the diagnosis, characterization, and therapy guidance in pancreatic cancer. Mol Cancer Ther 2006;5:787-796.
82. Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP, Coller H, Loh ML, Downing JR, Caligiuri MA, Bloomfield CD, Lander ES. Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science 1999;15:531-537.
83. Thomas CE, Sexton W, Benson K, Sutphen R, Koomen J. Urine collection and processing for protein discovery and quantification. Cancer Epidemiol Biomarkers Prev, 2010;19(4):953-959.
84. Adam BL, Vlahou A, Semmes OJ, Wright GL, Jr., Proteomic approaches to biomarker discovery in prostate and bladder cancers. Proteomics 2001;1:1264-1270.
85. Xu XZ, Lu X, Yang X, Yin P, Kong H. Comprehensive two-dimensional gas chromatography/time-of-flight mass spectrometry for metabonomics: Biomarker discovery for diabetes mellitus. Anal Chim Acta 2009;633:257-262.
86. Robosky LC, Robertson DG, Baker JD, Rane S, Reily MD. In vivo toxicity screening programs using metabonomics. Comb Chem High Throughput Screen 2002;5:651-662.
87. Ilyin SE, Belkowski SM, Plata-Salamán CR. Biomarker discovery and validation: Technologies and integrative approaches. Trends Biotechnol 2004;22:411-416.
88. Martínez-Cruz LA, Rubio A, Martínez-Chantar ML, Labarga A, Barrio I, Podhorski A, Segura V, Sevilla Campo JL, Avila MA, Mato JM. GARBAN: Genomic analysis and rapid biological annotation of cDNA microarray and proteomic data. Bioinformatics 2003;19:2158-2160.
89. Schadt EE. Molecular networks as sensors and drivers of common human diseases. Nature 2009;461(7261):218-223.
90. Wang X. Role of clinical bioinformatics in the development of network-based biomarkers. J Clin Bioinform 2011;1:28, 1-3.
91. Barabási AL, Gulbahce N, Loscalzo J. Network medicine: A network-based approach to human disease. Nat Rev Genet 2011;12:56-68.
92. Garraway LA, Lander ES. Lessons from the cancer genome. Cell 2013;153:17-37.
93. Chen L, Wang R, Zhang X. Biomolecular Networks: Methods and Applications in Systems Biology. Hoboken, NJ: John Wiley & Sons; 2009.
94. Barabasi AL, Albert R. Emergence of scaling in random networks. Science 1999;286:509-512.
95. Albert R. Scale-free networks in cell biology. J Cell Sci 2005;118:4947-4957.
96. Liu Z-P, Wang Y, Zhang XS, Chen L. Network-based analysis of complex diseases. IET Syst Biol 2010;1-12.
97. Kitano H. Systems biology: A brief overview. Science 2002;295:1662-1664.
98. Aryee MJ, Liu W, Engelmann JC, Nuhn P, Gurel M, Haffner MC, Esopi D, Irizarry RA, Nelson WG, Luo J, Xu J, Isaacs WB, Bova GS, Yegnasubramanian S. DNA methylation alterations exhibit intraindividual stability and interindividual heterogeneity in prostate cancer metastases. Sci Transl Med 2013;5:169ra10, 1-10.
99. Chuang HY, Lee E, Liu YT, Lee D, Ideker T. Network-based classification of breast cancer metastasis. Mol Syst Biol 2007;3:140, 1-10.
100. Wang P, Tang H, Zhang H, Whiteaker J, Paulovich AG, McIntosh M. Normalization regarding non-random missing values in high-throughput mass spectrometry data. Pac Symp Biocomput 2006;315-326.
101. Ideker T, Ozier O, Schwikowski B, Siegel AF. Discovering regulatory and signalling circuits in molecular interaction networks. Bioinformatics 2002;18(Suppl 1):S233-S240.
102. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T. Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res 2003;13:2498-2504.
103. Scott J, Ideker T, Karp RM, Sharan R. Efficient algorithms for detecting signaling pathways in protein interaction networks. J Comput Biol 2006;13:133-144.
104. Qiu Y, Zhang S, Zhang X, Chen L. Detecting disease associated modules and prioritizing active genes based on high throughput data. BMC Bioinformatics 2010;11:26-37.
105. Liu M, Liberzon A, Kong SW, Lai WR, Park PJ, Kohane IS, Kasif S. Network-based analysis of affected biological processes in type 2 diabetes models. PLoS Genet 2007;3:e96, 958-972.
106. Jiang BB, Wang JG, Xiao JF, Wang Y. Gene prioritization for type 2 diabetes in tissue-specific protein interaction networks. Lect Notes Oper Res 2009;11:319-328.
107. Jin G, Zhou X, Cui K, Zhang XS, Chen L, Wong ST. Cross-platform method for identifying candidate network biomarkers for prostate cancer. IET Syst Biol 2009;3:505-512.
108. Krauthammer M, Kaufmann CA, Gilliam TC, Rzhetsky A. Molecular triangulation: Bridging linkage and molecular-network information for identifying candidate genes in Alzheimer's disease. Proc Natl Acad Sci USA 2004;101:15148-15153.
109. Liu Z-P, Wang Y, Wen T, Zhang XS, Xia W, Chen L. Dynamically dysfunctional protein interactions in the development of Alzheimer's disease. Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, San Antonio, USA 2009. p 4262-4267.
110. Dao P, Wang K, Collins C, Ester M, Lapuk A, Sahinalp SC. Optimally discriminative subnetwork markers predict response to chemotherapy. ISMB 2011;27:i205-i213.
111. Liu X, Liu Z-P, Zhao XM, Chen L. Identifying disease genes and module biomarkers by differential interactions. J Am Med Inform Assoc 2012;19:241-248.
112. Lee E, Chuang HY, Kim JW, Ideker T, Lee D. Inferring pathway activity toward precise disease classification. PLoS Comput Biol 2008;4:e1000217, 1-9.
113. Wang Y-C, Chen B-S. A network-based biomarker approach for molecular investigation and diagnosis of lung cancer. BMC Med Genom 2011;4:2, 1-15.
114. Xu M, Kao MCJ, Nunez-Iglesias J, Nevins JR, West M, Zhou XJ. An integrative approach to characterize disease-specific pathways and their coordination: A case study in cancer. BMC Genom 2008;9(Suppl 1):S12, 1-12.
115. Huang Y, Sun X, Hu G. An integrated genetics approach for identifying protein signal pathways of Alzheimer's disease. Comput Methods Biomech Biomed Engin 2011;14:1-7.
116. Bonnet E, Michoel T, Van de Peer Y. Prediction of a gene regulatory network linked to prostate cancer from gene expression, microRNA and clinical data. Bioinformatics 2010;26:i638-i644.
117. Zhan X, Desiderio D. Signaling pathway networks mined from human pituitary adenoma proteomics data. BMC Med Genom 2010, 3:13, 1-26. doi: 10.1186/1755-8794-3-13.
118. Liu KQ, Liu Z-P, Hao JK, Chen L, Zhao XM. Identifying dysregulated pathways in cancers from pathway interaction networks. BMC Bioinformatics 2012;13:126-131.
119. He D, Liu Z-P, Chen L. Identification of dysfunctional modules and disease genes in congenital heart disease by a network-based approach. BMC Genomics 2011;12:592-607.
120. Kreeger PK, Lauffenburger DI. Cancer systems biology: A network modeling perspective. Carcinogenesis 2010;31(1):2-8.
121. Lavi O, Dror G, Shamir R. Network-induced classification kernels for gene expression profile analysis. J Comput Biol 2012;19(6):694-709.
122. Zhu Y, Shen X, Pan W. Network-based support vector machine for classification of microarray samples. BMC Bioinformatics, 2009;10(Suppl 1):S21, 1-11.
123. Chen L, Xuan J, Riggins RB, Clarke R, Wang Y. Identifying cancer biomarkers by network-constrained support vector machines. BMC Syst Biol 2011;5:161, 1-20.
124. Zhang B, Li H, Riggins RB, Zhan M, Xuan J, Zhang Z, Hoffman EP, Clarke R, Wang Y. Differential dependency network analysis to identify condition-specific topological changes in biological networks. Bioinformatics 2009;25(4):526-532.
125. Zhang B, Tian Y, Jin L, Li H, Shih I-M, Madhavan S, Clarke R, Hoffman EP, Xuan J, Hilakivi-Clarke L, Wang Y. DDN: A caBIG® analytical tool for differential network analysis. Bioinformatics 2011;27(7):1036-1038.
126. Taylor IW, Linding R, Warde-Farley D, Liu Y, Pesquita C, Faria D, Bull S, Pawson T, Morris Q, Wrana JL. Dynamic modularity in protein interaction networks predicts breast cancer outcome. Nat Biotechnol 2009;27:199-204.
127. Liu Y, Shin S, Zeng X, Zhan M, Gonzalez R, Mueller F-J, Schwartz CM, Xue H, Li H, Baker SC, Chudin E, Barker DL, McDaniel TK, Oeser S, Loring JF, Mattson MP, Rao MS. Genome wide profiling of human embryonic stem cells (hESCs), their derivatives and embryonal carcinoma cells to develop base profiles of U.S. Federal government approved hESC lines. BMC Dev Biol, 2006;6:20, 1-16.
128. Wang K, Li M, Hakonarson H. Analysing biological pathways in genome-wide association studies. Nat Rev Genet 2010;11:843-854.
129. Li M, Wang K, Grant SF, Hakonarson H, Li C. ATOM: A powerful gene-based association test by combining optimally weighted markers. Bioinformatics 2009;25:497-503.
130. Eleftherohorinou H, Wright V, Hoggart C, Hartikainen A-L, Jarvelin M-R, Balding D, Coin L, Levin M. Pathway analysis of GWAS provides new insights into genetic susceptibility to 3 inflammatory diseases. PLoS ONE 2009;4:e8068, 1-11.
131. Gauderman WJ, Murcray C, Gilliland F, Conti DV. Testing association between disease and multiple SNPs in a candidate gene. Genet Epidemiol 2007;31:383-395.
132. Li Y, Willer C, Sanna S, Abecasis G. Genotype imputation. Annu Rev Genomics Hum Genet 2009;10:387-406.
133. Marchini J, Howie B. Genotype imputation for genome-wide association studies. Nat Rev Genet 2010;11:499-511.
134. Wang K, Li M, Bucan M. Pathway-based approaches for analysis of genomewide association studies. Am J Hum Genet 2007;81:1278-1283.
135. Gentles AJ, Gallahan D. Systems Biology: Confronting the complexity of cancer. Cancer Res 2011;71(18):5961-5964.
136. Vandin F, Upfal E, Raphael BJ. Algorithms for detecting significantly mutated pathways in cancer. J Comput Biol 2011;18:507-522.
137. Nguyen V, Cao L, Lin JT, Hung N, Ritz A, Yu K, Jianu R, Ulin SP, Raphael BJ, Laidlaw DH, Brossay L, Salomon AR. A new approach for quantitative phosphoproteomic dissection of signaling pathways applied to T cell receptor activation. Mol Cell Proteomics 2009;8:2418-2431.
138. Liu R, Aihara K, Chen L. Dynamical network biomarkers for identifying critical transitions and their driving networks of biologic processes. Quant Biol 2013. doi:10.1007/s40484-013-0008-0.
139. Liu XP, Liu R, Zhao X-M, Chen L. Detecting early-warning signals of type 1 diabetes and its leading biomolecular networks by dynamical network biomarkers. BMC Med Genom 2013;6(Suppl 2):S8, 1-10. doi:10.1186/1755-8794-6-S2-S8.
140. Li M, Tao Z, Liu R, Chen L. Detecting tissue-specific early-warning signals for complex diseases based on dynamical network biomarkers: Study of type-2 diabetes by cross-tissue analysis. Brief Bioinform 2013. doi: 10.1093/bib/bbt027.
141. Sciuto AM, Phillips CS, Orzolek LD, Hege AI, Moran TS, Dillman JF. Genomic analysis of murine pulmonary tissue following carbonyl chloride inhalation. Chem Res Toxicol 2005;18:1654-1660.