A pattern-oriented specification of gene network inference processes

Computers in Biology and Medicine

Volumn 43, Issue 10, 2013, Pages 1415-1427

  Trepode, Nestor W ^a   de Farias, Cléver R G ^a   Barrera, Junior ^a  

^a UNIVERSITY OF SÃO PAULO   (Brazil)

Author keywords

Dynamical system identification; Gene network inference; Genetic regulatory networks; Microarray data analysis; Patterns; Process modeling

Indexed keywords

GENE NETWORK INFERENCE; GENETIC REGULATORY NETWORKS; MICROARRAY DATA ANALYSIS; PATTERNS; PROCESS MODELING;

DYNAMICAL SYSTEMS; GENES;

SPECIFICATIONS;

ALGORITHM; ARTICLE; BIOLOGICAL FUNCTIONS; DNA MICROARRAY; GENE CONTROL; GENE INTERACTION; GENE REGULATORY NETWORK; HUMAN; INFORMATION SYSTEM; KNOWLEDGE DISCOVERY; PRIORITY JOURNAL; PROTEIN INTERACTION;

DYNAMICAL SYSTEM IDENTIFICATION; GENE NETWORK INFERENCE; GENETIC REGULATORY NETWORKS; MICROARRAY DATA ANALYSIS; PATTERNS; PROCESS MODELING;

COMPUTATIONAL BIOLOGY; GENE REGULATORY NETWORKS; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; PATTERN RECOGNITION, AUTOMATED;

EID: 84882796052     PISSN: 00104825     EISSN: 18790534     Source Type: Journal    
DOI: 10.1016/j.compbiomed.2013.07.008     Document Type: Article

References (60)

1. Schlitt T., Brazma A. Current approaches to gene regulatory network modelling. BMC Bioinformatics 2007, 8:S9+.
2. Dougherty E.R., Akutsu T., Cristea P.D., Tewfik A.H. Genetic regulatory networks. EURASIP J. Bioinformatics Syst. Biol. 2007, 2007:2. Article ID 17321.
3. Datta A., Pal R., Choudhary A., Dougherty E.R. Control approaches for probabilistic gene regulatory networks. IEEE Signal Process. Mag. 2007, 24:54-63.
4. Barillet F. Genetic improvement for dairy production in sheep and goats. Small Rumin. Res. 2007, 70:60-75.
5. Altarriba J., Yague G., Moreno C., Varona L. Exploring the possibilities of genetic improvement from traceability data. An example in the pirenaica beef cattle. Livest. Sci. 2009, 125:115-120.
6. T. Yamada, G. Spangenberg, J.H. Bouton, Molecular breeding to improve forages for use in animal and biofuel production systems, in: Molecular Breeding of Forage and Turf, Springer, New York, 2009, pp. 1-14.
7. Schauer N., Semel Y., Roessner U., Gur A., Balbo I., Carrari F., Pleban T., Perez-Melis A., Bruedigam C., Kopka J., Willmitzer L., Zamir D., Fernie A.R. Comprehensive metabolic profiling and phenotyping of interspecific introgression lines for tomato improvement. Nat. Biotechnol. 2006, 24:447-454.
8. Chen L., Zhou Z.-X., Yang Y.-J. Genetic improvement and breeding of tea plant (Camellia sinensis) in china. from individual selection to hybridization and molecular breeding. Euphytica 2007, 154:239-248.
9. S. Liang, S. Fuhrman, R. Somogyi, Reveal, a general reverse engineering algorithm for inference of genetic network architectures, Pacific Symposium on Biocomputing, 1998, pp. 18-29.
10. Shmulevich I., Dougherty E.R., Kim S., Zhang W. Probabilistic boolean networks. a rule-based uncertainty model for gene regulatory networks. Bioinformatics 2002, 18:261-274.
11. Soinov L.A., Krestyaninova M.A., Brazma A. Towards reconstruction of gene networks from expression data by supervised learning. Genome Biology 2003, 4:R6.
12. Zhou X., Wang X., Pal R., Ivanov I., Bittner M., Dougherty E.R. A Bayesian connectivity-based approach to constructing probabilistic gene regulatory networks. Bioinformatics 2004, 20:2918-2927.
13. Margolin A.A., Nemenman I., Basso K., Wiggins C., Stolovitzky G., Dalla Favera R., Califano A. Aracne. an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinformatics 2006, 7(Suppl. 1):S7.
14. Meyer P.E., Kontos K., Lafitte F., Bontempi G. Information-theoretic inference of large transcriptional regulatory networks. EURASIP J. Bioinformatics Syst. Biol. 2007, 2007:9. Article ID 79879.
15. Zhao W., Serpedin E., Dougherty E.R. Inferring connectivity of genetic regulatory networks using information-theoretic criteria. IEEE/ACM Trans. Comput. Biol. Bioinformatics 2008, 5:262-274.
16. J. Watkinson, K.C. Liang, X. Wang, T. Zheng, D. Anastassiou, Inference of regulatory gene interactions from expression data using three-way mutual information, Ann. N. Y. Acad. Sci. 1158 (2009) 302-313.
17. de Jong H. Modeling and simulation of genetic regulatory systems. literature review. J. Comput. Biol. 2002, 9:67-103.
18. Friedman N. Inferring cellular networks using probabilistic graphical models. Sci. 2004, 303:799-805.
19. Hecker M., Lambeck S., Toepfer S., van Someren E., Guthke R. Gene regulatory network inference. data integration in dynamic models - a review. Biol. Syst. 2009, 96:86-103.
20. Hache H., Lehrach H., Herwig R. Reverse engineering of gene regulatory networks. a comparative study. EURASIP J. Bioinformatics Syst. Biol. 2009, 2009:12. Article ID 617281.
21. Kumar S., Dudley J. Bioinformatics software for biologists in the genomics era. Bioinformatics 2007, 23:1713-1717.
22. Oinn T., Addis M., Ferris J., Marvin D., Senger M., Greenwood M., Carver T., Glover K., Pocock M.R., Wipat A., Li P. Taverna. a tool for the composition and enactment of bioinformatics workflows. Bioinformatics 2004, 20:3045-3054.
23. Hull D., Wolstencroft K., Stevens R., Goble C., Pocock M.R., Li P., Oinn T. Taverna. a tool for building and running workflows of services. Nucleic Acids Research 2006, 34:W729-W732.
24. Margaria T., Kubczak C., Steffen B. Bio-jeti. a service integration, design, and provisioning platform for orchestrated bioinformatics processes. BMC Bioinformatics 2008, 9:S12.
25. Lamprecht A.L., Margaria T., Steffen B. Bio-jeti. a framework for semantics-based service composition. BMC Bioinformatics 2009, 10:S8.
26. Ríos J., Karlsson J., Trelles O. Magallanes. a web services discovery and automatic workflow composition tool. BMC Bioinformatics 2009, 10. Article number: 334.
27. Konstantinides K., Rasure J.R. The Khoros software development environment for image and signal processing. IEEE Trans. Image Process. 1994, 3:243-252.
28. Young M., Argiro D., Kubica S. Cantata. visual programming environment for the khoros system. SIGGRAPH Comput. Graph. 1995, 29:22-24.
29. Barrera J., Cesar R.M., Martins D.C., Vencio R.Z.N., Merino E.F., Yamamoto M.M., Leonardi F.G., Pereira C.A.B., del Portillo H.A. Constructing probabilistic genetic networks of plasmodium falciparum from dynamical expression signals of the intraerythrocytic development cycle. Methods of Microarray Data Analysis V, 1st edition 2006, vol. 1:11-26. Springer, New York.
30. Hashimoto R.F., Kim S., Shmulevich I., Zhang W., Bittner M.L., Dougherty E.R. Growing genetic regulatory networks from seed genes. Bioinformatics 2004, 20:1241-1247.
31. M. Ris, Representacão de sistemas biologicos a partir de sistemas dinamicos: Controle da transcricão a partir do estrogeno (Biological Systems Representation by Dynamical Systems: Transcription Control from Estrogen), Ph.D. Thesis, Universidade de São Paulo, 2008.
32. Lima L.A., Ris M., Barrera J., Brentani M.M., Brentani H. Computing a predictor set influence zone through a multi-layer genetic network to explore the role of estrogen in breast cancer. Adv. Breast Cancer Res. 2012, 1:21-29.
33. Vahedi G., Ivanov I., Dougherty E. Inference of boolean networks under constraint on bidirectional gene relationships. IET Syst. Biol. 2009, 3:191-202.
34. Djebbari A., Quackenbush J. Seeded Bayesian networks. constructing genetic networks from microarray data. BMC Syst. Biol. 2008, 2:13.
35. Anastassiou D. Computational analysis of the synergy among multiple interacting genes. Mol. Syst. Biol. 2007, 3:83.
36. Cover T.M., Thomas J.A. Elements of Information Theory 2006, Wiley-Interscience. 2nd edition.
37. Dougherty E.R., Kim S., Chen Y. Coefficient of determination in nonlinear signal processing. Signal Process. 2000, 80:2219-2235.
38. Kim S., Dougherty E.R., Shmulevich I., Hess K.R., Hamilton S.R., Trent J.M., Fuller G.N., Zhang W. Identification of combination gene sets for glioma classification. Mol. Cancer Ther. 2002, 1:1229-1236.
39. Ashburner M., Ball C.A., Blake J.A., Botstein D., Butler H., Cherry J.M., Davis A.P., Dolinski K., Dwight S.S., Eppig J.T., Harris M.A., Hill D.P., Issel-Tarver L., Kasarskis A., Lewis S., Matese J.C., Richardson J.E., Ringwald M., Rubin G.M., Sherlock G. Gene ontology. tool for the unification of biology. the gene ontology consortium. Nat. Genet. 2000, 25:25-29.
40. Ogata H., Goto S., Sato K., Fujibuchi W., Bono H., Kanehisa M. Kegg. Kyoto encyclopedia of genes and genomes. Nucl. Acids Res. 1999, 27:29-34.
41. Joshi-Tope G., Gillespie M., Vastrik I., DD́ Eustachio P., Schmidt E., de Bono B., Jassal B., Gopinath G., Wu G., Matthews L., Lewis S., Birney E., Stein L. Reactome. a knowledge-base of biological pathways. Nucl. Acids Res. 2005, 33:D428-D432.
42. Vliet H.v. Software Engineering - Principles and Practice 2000, Wiley, New York.
43. Alexander C. The Timeless Way of Building 1979, Oxford University Press.
44. Alexander C., Ishikawa S., Silverstein M. A Pattern Language. Towns, Buildings, Construction 1977, Oxford University Press.
45. Alexander C., Silverstein M., Angel S., Ishikawa S., Abrams D. The Oregon Experiment 1975, Oxford University Press.
46. Gamma E., Helm R., Johnson R., Vlissides J. Design Patterns. Elements of Reusable Object-Oriented Software 1995, Addison-Wesley Professional.
47. Buschmann F., Meunier R., Rohnert H., Sommerlad P., Stal M. Pattern-Oriented Software Architecture Volume 1. A System of Patterns 1996, Wiley, New York.
48. Schmidt D., Stal M., Rohnert H., Buschmann F. Pattern-Oriented Software Architecture Volume 2. Patterns for Concurrent and Networked Objects 2000, Wiley.
49. Marinescu F. EJB Design Patterns. Advanced Patterns, Processes, and Idioms 2002, Wiley.
50. Object Management Group, Business Process Modeling Notation, V1.2, Needham, 2009. OMG Available Specification.
51. Kim S., Li H., Dougherty E.R. Can Markov chain models mimic biological regulation?. J. Biol. Syst. 2002, 10:337-357.
52. Datta A., Choudhary A., Bittner M.L., Dougherty E.R. External control in Markovian genetic regulatory networks. the imperfect information case. Bioinformatics 2004, 20:924-930.
53. C.R.G. de Farias, Architectural Design of Groupware Systems: a Component-Based Approach, Ph.D. Thesis, University of Twente, 2002.
54. Bozdech Z., Llinás M., Pulliam B.L., Wong E.D., Zhu J., DeRisi J.L. The transcriptome of the intraerythrocytic developmental cycle of plasmodium falciparum. PLoS Biol. 2003, 1. Article number: E5.
55. Lin C.Y., Ström A., Vega V.B., Kong S.L., Yeo A.L., Thomsen J.S., Chan W.C., Doray B., Bangarusamy D.K., Ramasamy A., Vergara L.A., Tang S., Chong A., Bajic V.B., Miller L.D., Gustafsson J.A., Liu E.T. Discovery of estrogen receptor alpha target genes and response elements in breast tumor cells. Genome Biol. 2004, 5. Article number: R66.
56. Martins D.C., Cesar R.M., Barrera J. W-operator window design by minimization of mean conditional entropy. Pattern Anal. Appl. 2006, 9:139-153.
57. Ris M., Barrera J., Martins D.C. U-curve. a branch-and-bound optimization algorithm for u-shaped cost functions on boolean lattices applied to the feature selection problem. Pattern Recognition 2009.
58. Liang K.-C., Wang X. Gene regulatory network reconstruction using conditional mutual information. EURASIP J. Bioinformatics Syst. Biol. 2008, 2008:14. Article ID 253894.
59. Watkinson J., Wang X., Zheng T., Anastassiou D. Identification of gene interactions associated with disease from gene expression data using synergy networks. BMC Syst. Biol. 2008, 2. Article number: 10.
60. Diehn M., Sherlock G., Binkley G., Jin H., Matese J.C., Hernandez-Boussard T., Rees C.A., Cherry M.J., Botstein D., Brown P.O., Alizadeh A.A. Source. a unified genomic resource of functional annotations, ontologies, and gene expression data. Nucl. Acids Res. 2003, 31:219-223.