A novel network model for molecular prognosis

2010 ACM International Conference on Bioinformatics and Computational Biology, ACM-BCB 2010

Volumn , Issue , 2010, Pages 342-345

  Wan, Ying Wooi ^a   Bose, Swetha ^a   Denvir, James ^a   Guo, Nancy Lan ^a  

^a WEST VIRGINIA UNIVERSITY   (United States)

Author keywords

Gene co expression networks; Implication networks; Molecular prognosis; Personalized therapy

Indexed keywords

BIOMARKER IDENTIFICATION; CANCER RECURRENCE; CLINICAL OUTCOME; CO-EXPRESSION NETWORKS; CONNECTED NETWORKS; DIFFERENTIAL COMPONENT; DISEASE STATE; GENE EXPRESSION PROFILES; GENOME-WIDE ASSOCIATION; IMPLICATION NETWORKS; LUNG CANCER; MOLECULAR PROGNOSIS; NETWORK MODELS; NETWORK-BASED; PERSONALIZED THERAPY; PREDICTION LOGIC;

ASSOCIATION REACTIONS; BIOINFORMATICS; FORMAL LOGIC; GENE THERAPY;

GENE EXPRESSION;

EID: 77958024236     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1854776.1854825     Document Type: Conference Paper

References (30)

1. Ideker T, Sharan R. 2008. Protein networks in disease. Genome Res 18: 644-652.
2. Chuang HY, Lee E, Liu YT, Lee D, Ideker T. 2007. Network-based classification of breast cancer metastasis. Mol Syst Biol 3: 140.
3. Csermely P, Agoston V, Pongor S. 2005. The efficiency of multi-target drugs: the network approach might help drug design. Trends Pharmacol Sci 26: 178-182.
4. Sotiriou C, Piccart MJ. 2007. Taking gene-expression profiling to the clinic: when will molecular signatures become relevant to patient care? Nat Rev Cancer 7: 545-553.
5. Hoffman PC, Mauer AM, Vokes EE. 2000. Lung cancer. Lancet 355: 479-485.
6. Chen HY, Yu SL, Chen CH, Chang GC, Chen CY et al. 2007. A five-gene signature and clinical outcome in non-small-cell lung cancer. N Engl J Med 356: 11-20.
7. Potti A, Mukherjee S, Petersen R, Dressman HK, Bild A et al. 2006. A genomic strategy to refine prognosis in early-stage non-small-cell lung cancer. N Engl J Med 355: 570-580.
8. Shedden K, Taylor JM, Enkemann SA, Tsao MS, Yeatman TJ et al. 2008. Gene expression-based survival prediction in lung adenocarcinoma: a multi-site, blinded validation study. Nat Med 14: 822-827.
9. Emir B, Wieand S, Su JQ, Cha S. 1998. Analysis of repeated markers used to predict progression of cancer. Stat Med 17: 2563-2578.
10. Hartwell LH, Hopfield JJ, Leibler S, Murray AW. 1999. From molecular to modular cell biology. Nature 402: C47-C52.
11. Emilsson V, Thorleifsson G, Zhang B, Leonardson AS, Zink F et al. 2008. Genetics of gene expression and its effect on disease. Nature 452: 423-428.
12. Calvano SE, Xiao W, Richards DR, Felciano RM, Baker HV et al. 2005. A network-based analysis of systemic inflammation in humans. Nature 437: 1032-1037.
13. Albert R, Othmer HG. 2003. The topology of the regulatory interactions predicts the expression pattern of the segment polarity genes in Drosophila melanogaster. J Theor Biol 223: 1-18.
14. Karlebach G, Shamir R. 2008. Modelling and analysis of gene regulatory networks. Nat Rev Mol Cell Biol 9: 770-780.
15. Friedman N. 2004. Inferring cellular networks using probabilistic graphical models. Science 303: 799-805.
16. Zhu J, Zhang B, Smith EN, Drees B, Brem RB et al. 2008. Integrating large-scale functional genomic data to dissect the complexity of yeast regulatory networks. Nat Genet 40: 854-861.
17. Sachs K, Perez O, Pe'er D, Lauffenburger DA, Nolan GP. 2005. Causal protein-signaling networks derived from multiparameter single-cell data. Science 308: 523-529.
18. Milo R, Shen-Orr S, Itzkovitz S, Kashtan N, Chklovskii D et al. 2002. Network motifs: simple building blocks of complex networks. Science 298: 824-827.
19. Milo R, Itzkovitz S, Kashtan N, Levitt R, Shen-Orr S et al. 2004. Superfamilies of evolved and designed networks. Science 303: 1538-1542.
20. Wuchty S, Oltvai ZN, Barabasi AL. 2003. Evolutionary conservation of motif constituents in the yeast protein interaction network. Nat Genet 35: 176-179.
21. Kim SY, Imoto S, Miyano S. 2003. Inferring gene networks from time series microarray data using dynamic Bayesian networks. Brief Bioinform 4: 228-235.
22. Pe'er D, Regev A, Elidan G, Friedman N. 2001. Inferring subnetworks from perturbed expression profiles. Bioinformatics 17 Suppl 1: S215-S224.
23. Desmarais MC, Maluf A, Liu J. 1996. User-expertise modeling with empirically derived probabilistic implication networks. User Modeling and User-Adapted Interaction 5: 283-315.
24. Desmarais MC, Meshkinfam P, Gagnon M. 2006. Learned Student Models with Item to Item Knowledge Structures. User Modeling and User-Adapted Interaction 16: 403-434. (Pubitemid 44808135)
25. Liu J, Desmarais MC. 1997. A Method of Learning Implication Networks from Empirical Data: Algorithm and Monte-Carlo Simulation-Based Validation. IEEE Transactions on Knowledge and Data Engineering 9: 990-1004.
26. Liu J, Maluf D, Desmarais MC. 2001. A New Uncertainty Measure for Belief Networks with Applications to Optimal Evidential Inferencing. IEEE Transactions on Knowledge and Data Engineering 13: 416-425.
27. Falmagne JC, Doignon JP, Koppen M, Villano M, Johannesen L. 1990. Introduction to knowledge spaces: how to build, test and search them. Psychological Review 97: 201-224.
28. Guo L, Cukic B, Singh H. 2003. Predicting Fault Prone Modules by the Dempster-Shafer Belief Networks. 18th IEEE International Conference on Automated Software Engineering (ASE'03) 249-252.
29. Hildebrand, D. K., Laing, J. D., and Rosenthal, H. 1977. Prediction Analysis of Cross Classifications. John Wiley & Sons.
30. Witten, I. H. and Frank, E. 2005. Data Mining: Practical Machine Learning Tools and Techniques (2nd Edition). Morgan Kaufmann.