Fusion methodologies for biomedical data

Journal of Proteomics

Volumn 74, Issue 12, 2011, Pages 2774-2785

  Tsiliki, Georgia ^a   Kossida, Sophia ^a  

^a BIOMEDICAL RESEARCH FOUNDATION   (Greece)

Author keywords

Bayesian networks; Data integration; Genome wide data; Kernel models; Proteome; Transcriptome

Indexed keywords

AMINO ACID; PROTEIN;

AMINO ACID SEQUENCE; BAYES THEOREM; BIOINFORMATICS; COMPUTATIONAL FLUID DYNAMICS; GENE EXPRESSION; GENE EXPRESSION PROFILING; GENE REGULATORY NETWORK; GENOME; GENOMICS; HUMAN; KERNEL METHOD; LEARNING ALGORITHM; MEDICAL RESEARCH; METHODOLOGY; MOLECULAR BIOLOGY; PRIORITY JOURNAL; PROTEIN DATABASE; PROTEIN DNA INTERACTION; PROTEIN FUNCTION; PROTEIN MICROARRAY; PROTEIN PROTEIN INTERACTION; PROTEOMICS; REVIEW; SIGNAL TRANSDUCTION; SUPPORT VECTOR MACHINE; TRANSCRIPTOMICS; WEB BROWSER;

ANIMALS; COMPUTATIONAL BIOLOGY; DATABASES, PROTEIN; GENE EXPRESSION REGULATION; HUMANS; MODELS, BIOLOGICAL; PROTEIN BIOSYNTHESIS; PROTEINS;

EID: 81055140498     PISSN: 18743919     EISSN: 18767737     Source Type: Journal    
DOI: 10.1016/j.jprot.2011.07.001     Document Type: Review

References (80)

1. Joyce A.R., Palsson B.Ø. The model organism as a system: integrating 'omics' data sets. Nat Rev Mol Cell Biol 2006, 7:198-210.
2. Sullivan D.E., Gabbard J.L., Shukla M., Sobral B. Data integration for dynamic and sustainable systems biology resources: challenges and lessons learned. Chem Biodivers 2010, 7(5):1124-1141.
3. Huttenhower C., Schroeder M., Chikina M.D., Troyanskaya O.G. The Sleipnir library for computational functional genomics. Bioinformatics 2008, 24(13):1559-1561.
4. Beagley N., Stratton K.G., Webb-Robertson B.-J.M. VIBE 2.0: visual integration for Bayesian evaluation. Bioinformatics 2010, 26(2):280-282.
5. Shen R., Ghosh D., Chinnaiyan A.M. Prognostic meta-signature of breast cancer developed by two-stage mixture modeling of microarray data. BMC Genom 2004, 5(94). 10.1186/1471-2164-5-94.
6. Xu L., Tan A.C., Winslow R.L., Geman D. Merging microarray data from separate breast cancer studies provides a robust prognostic test. BMC Bioinf 2008, 9(125). 10.1186/1471-2105-9-125.
7. Shabalin A.A., Tjelmeland H., Fan C., Perou C.M., Nobel A.B. Merging two gene-expression studies via cross-platform normalization. Bioinformatics 2008, 24(9):1154-1160.
8. Hester R.L., Iliescu R., Summers R., Coleman T.G. Systems biology and integrative physiological modelling. J Physiol 2011, 589(5):1053-1060.
9. Lu L.J., Xia Y., Paccanaro A., Yu H., Gerstein M. Assessing the limits of genomic data integration for predicting protein networks. Genome Res 2005, 15:945-953.
10. Nariai N., Kolaczyk E.D., Kasif S. Probabilistic protein function prediction from heterogeneous genome-wide data. PLoS One 2007, 2:e337.
11. Hwang D., Rust A.G., Ramsey S., Smith J.J., Leslie D.M., Weston A.D. A data integration methodology for systems biology. Proc Natl Acad Sci USA 2005, 102(48):17296-17301.
12. Lanckriet G.R.G., De Bie T., Cristianini N., Jordan M.I., Stafford Noble W. A statistical framework for genomic data fusion. Bioinformatics 2004, 20(16):2626-2635.
13. Zhang W., Li F., Nie L. Integrating multiple 'omics' analysis for microbial biology: application and methodologies. Microbiology 2010, 156:287-301.
14. Kint G., Fierro C., Marchal K., Vanderleyden J., De Keersmaecker S.C.J. Integration of 'omics' data: does it lead to new insights into host-microbe interactions?. Future Microb 2010, 5(2):313-328.
15. Fonville J.M., Richards S.E., Barton R.H., Boulange C.L., Ebbels T.M.D., Nicholson J.K., Holmes E., Dumas M.-E. The evolution of partial least squares models and related chemometric approaches in metabonomics and metabolic phenotyping. J Chemom 2010, 24:636-649.
16. Fagan A., Culhane A.C., Higgins D.G. A multivariate analysis approach to the integration of proteomic and gene expression data. Proteomics 2007, 7:2162-2171.
17. Mrowka R., Lieberneister W., Holste D. Does mapping reveal correlation between gene expression and protein-protein interaction?. Nat Genet 2003, 33:15-16.
18. Troyanskaya O.G., Dolinski K., Owen A.B., Altman R.B., Botstein D. A Bayesian framework for combining heterogeneous data sources for gene function prediction (in Saccharomyces cerevisiae). Proc Natl Acad Sci USA 2003, 100(14):8348-8353.
19. Webb-Robertson B.-J.M., McCue L.A., Beagley N., McDermott J.E., Wunschell D.S., Varnum S.M., Hu J.Z., Isern N.G., Buchko G.W., Mcateer K., Pounds J.G. A Bayesian integration model of high-throughput proteomics and metabolomics data for improved early detection of microbial infections. Pac Symp Biocomput 2009, 14:451-463.
20. Hwang D., Rust A.G., Ramsey S., Smith J.J., Leslie D.M., Weston A.D., Rust A.G., Ramsey S., de Atauri P., Siegel A.F., Bolouri H., Aitchison J.D., Hoodet L. A data integration methodology for systems biology: experimental verification. Proc Natl Acad Sci USA 2005, 102(48):17302-17307.
21. Wang Y.-C., Chen B.-S. Integrated cellular network of transcription regulations and protein-protein interactions. BMC Syst Biol 2010, 4:20.
22. Dotan-Cohen D., Letovsky S., Melkman A.A., Kasif S. Biological process linkage networks. PLoS One 2009, 4(4):e5313.
23. Martin A., Ochagavia M.E., Rabasa L.C., Miranda J., Fernandez-de-Cossio J., Bringas R. BisoGenet: a new tool for gene network building, visualization and analysis. BMC Bioinf 2010, 11:91.
24. Jansen R., Greenbaum D., Gerstein M. Relating whole-genome expression data with protein-protein interactions. Genome Res 2002, 12:37-46.
25. Jansen R., Lan N., Qian J., Gerstein M. Integration of genomic datasets to predict protein complexes in yeast. J Struct Funct Genomics 2002, 2:71-81.
26. Friedman N. Inferring cellular networks using probabilistic graphical models. Science 2004, 303:799-805.
27. Butte A.J., Kohane I.S. Creation and implications of a phenome-genome network. Nat Biotechnol 2006, 24(1):55-62.
28. Huttenhower C., Hibbs M., Myers C., Troyanskaya O.G. A scalable method for integration and functional analysis of multiple microarray. Bioinformatics 2006, 22(23):2890-2897.
29. Lee H., Tu Z., Deng M., Sun F., Chen T. Diffusion kernel-based logistic regression models for protein function prediction. OMICS. J Integr Biol 2006, 10:40-55.
30. Yeger-Lotem E., Margalit H. Detection of regulatory circuits by integrating the cellular networks of protein-protein interactions and transcription regulation. Nucleic Acids Res 2003, 31:6053-6061.
31. Yeger-Lotem E., Sattath S., Kashtan N., Itzkovitz S., Milo R., Pinter R.Y., Alon U., Margalit H. Network motifs in integrated cellular networks of transcription regulation and protein-protein interaction. Proc Natl Acad Sci USA 2004, 101:5934-5939.
32. Qiu J., Noble W.S. Predicting co-complex protein pairs from heterogeneous data. PLoS Comp Biol 2008, 4(4):e1000054.
33. Lee I., Date S.V., Adai A.T., Marcotte E.M. A probabilistic functional network of yeast genes. Science 2004, 306:1555-1558.
34. Blom E.J., Breitling R., Hofstede K.J., Roerdink J.B.T.M., van Hijum S.A.F.T., Kuipers O.P. Prosecutor: parameter-free inference of gene function for prokaryotes using DNA microarray data, genomic context and multiple gene annotation sources. BMC Genomics 2008, 9:495.
35. Pandey G., Myers C.L., Kumar V. Incorporating functional inter-relationships into protein function prediction algorithms. BMC Bioinf 2009, 10:142.
36. Gehlenborg N., O'Donoghue S.I., Baliga N.S., Goesmann A., Hibbs M.A., Kitano H., Kohlbacher O., Neuweger H., Schneider R., Tenenbaum D., Gavin A.-C. Visualization of omics data for systems biology. Nat Methods Rev 2010, 7(3s):S57-S68.
37. Stark C., Breitkreutz B.J., Reguly T., Bouche L., Breitkreutz A., Tyers M. BioGRID: a general repository for interaction datasets. Nucleic Acids Res 2006, 34:D535-D539.
38. Imoto S., Takao G., Satoru M. Estimation of genetic networks and functional structures between genes by using Bayesian networks and nonparametric regression. Pac Symp Biocomput 2002, 175-186.
39. Baitaluk M., Sedova M., Ray A., Gupta A. BiologicalNetworks: visualization and analysis tool for systems biology. Nucleic Acids Res 2006, 34:W466-W471.
40. Wu W.H., Wang F.S., Chang M.S. Sensitivity analysis of dynamic biological systems with time-delays. BMC Bioinf 2010, 11(Suppl 7):S12.
41. Narayanan M., Vetta A., Schadt E.E., Zhu J. Simultaneous clustering of multiple gene expression and physical interaction datasets. PLoS Comp Biol 2010, 6(4):e1000742.
42. Mazurie A., Bottani S., Vergassola M. An evolutionary and functional assessment of regulatory network motifs. Genome Biol 2005, 6:R35.
43. Zhang L.V., Wong S.L., King O.D., Roth F.P. Predicting co-complex protein pairs using genomic and proteomic data integration. BMC Bioinf 2004, 5:38.
44. Torres-García W., Zhang W., Runger G.C., Johnson R.H., Meldrum D.R. Integrative analysis of transcriptomic and proteomic data of Desulfovibrio vulgaris: a non-linear model to predict abundance of undetected proteins. Bioinformatics 2009, 25(15):1905-1914.
45. Klamt S., Haus U.-U., Theis F. Hypergraphs and cellular networks. PLoS Comp Biol 2009, 5(5):e1000385.
46. Lee T.J., Pouliot Y., Wagner V., Gupta P., Stringer-Calvert D.W., Tenenbaum J.D., Karp P.D. BioWarehouse: a bioinformatics database warehouse toolkit. BMC Bioinf 2006, 7:170.
47. Küntzer J., Backes C., Blum T., Gerasch A., Kaufmann M., Kohlbacher O., Lenhof H.P. BNDB-the Biochemical Network Database. BMC Bioinf 2007, 8:367.
48. Birkland A., Yona G. BIOZON: a hub of heterogeneous biological data. Nucleic Acids Res 2006, 34:D235-D242.
49. Yu E.Z., Counterman Burba A.E., Gerstein M. PARE: a tool for comparing protein abundance and mRNA expression data. BMC Bioinf 2007, 8:309.
50. Huttenhower C., Mehmood S.O., Troyanskaya O.G. Graphle: interactive exploration of large, dense graphs. BMC Bioinf 2009, 10:417.
51. Smoot M.E., Ono K., Ruscheinski J., Wang P.-L., Ideker T. Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics 2011, 27(3):431-432.
52. Yi M., Horton J.D., Cohen J.C., Hobbs H.H., Stephens R.M. WholePathwayScope: a comprehensive pathway-based analysis tool for high throughput data. BMC Bioinf 2006, 7. 10.1186/1471-2105-7-30.
53. Baitaluk M., Qian X., Godbole S., Raval A., Ray A., Guptaet A. PathSys: integrating molecular interaction graphs for systems biology. BMC Bioinf 2006, 7:55.
54. Pearl J. Probabilistic reasoning in intelligent systems. Networks of Plausible Inference 1988, Morgan Kaufmann, San Mateo, CA.
55. Lin N., Wu B., Jansen R., Gerstein M., Zhao H. Information assessment on predicting protein-protein interactions. BMC Bioinf 2004, 5:154.
56. Huttenhower C., Haley E.M., Hibbs M.A., Dumeaux V., Barrett D.R., Coller H.A., Troyanskaya O.G. Exploring the human genome with functional maps. Genome Res 2009, 19:1093-1106.
57. Husmeier D., Werhli A.V. Bayesian integration of biological prior knowledge into the reconstruction of gene regulatory networks with Bayesian networks. Tech. Report, Biomathematics and Statistics Scotland, Edinburgh 2007.
58. Jensen F.V. Bayesian Networks and Decision Graphs 2001, Springer, New York.
59. Ripley B.D. Stochastic models, in stochastic simulation 2008, John Wiley & Sons Inc, Hoboken, NJ.
60. Jansen R., Yu H., Greenbaum D., Kluger Y., Krogan N.J., Chung S., Emili A., Snyder M., Greenblatt J.F., Gerstein M. A Bayesian networks approach for predicting protein-protein interactions from genomic data. Science 2003, 302:449-453.
61. Myers C.L., Robson D., Wible A., Hibbs M.A., Chiriac C., Theesfeld C.L., Dolinski K., Troyanskaya O.G. Discovery of biological networks from diverse functional genomic data. Genome Biol 2005, 6:R114.
62. Interdisciplinary Statistics Statistical analysis of gene expression microarray data 2003, Speed T, Chapman & Hall/ CRC.
63. Chikina M.D., Troyanskaya O.G. Accurate quantification of functional analogy among close homologs. PLoS Comp Biol 2011, 7(2):e1001074.
64. Brown M.P., Grundy W.N., Lin D., Cristianini N., Sugnet C.W., Furey T.S., Ares J.M., Haussler D. Knowledge-based analysis of microarray gene expression data by using support vector machines. Proc Natl Acad Sci 2000, 97:262-267.
65. Cristianini N., Shawe-Taylor J. An introduction to support vector machines 2000, Cambridge University Press, Cambridge, UK.
66. Noble W.S. Support vector machine applications in computational biology. Kernel methods in computational biology 2004, 71-92. MIT Press, Cambridge, Massachusetts. B. Schoelkopf, K. Tsuda, J.P. Vert (Eds.).
67. Pavlidis P., Weston J., Cai J., Noble W.S. Learning gene functional classifications from multiple data types. J Comput Biol 2002, 9:401-411.
68. Ko S., Lee H. Integrative approaches to the prediction of protein functions based on the feature selection. BMC Bioinf 2009, 10:455.
69. Quackenbush J. Computational analysis of microarray data. Nat Rev Genet 2001, 2:418-427.
70. Aerts S., Lambrechts D., Maity S., Loo P.V., Coessens B., Smet F.D., Tranchevent L.C., Moor B.D., Marynen P., Hassan B., Carmeliet P., Moreau Y. Gene prioritization through genomic data fusion. Nat Biotechnol 2006, 24:537-544.
71. Lagerstedt K.K., Kristiansson E., Lönnroth C., Andersson M., Iresjö B.M., Gustafsson A., Hansson E., Kressner U., Nordgren S., Enlund F., Lundholm K. Genes with relevance for early to late progression of colon carcinoma based on combined genomic and transcriptomic information from the same patients. Cancer Inf 2010, 9:79-91.
72. Lemmens K., Dhollander T., De Bie T., Monsieurs P., Engelen K., Smets B., Winderick J., Moor D.B., Marchal K. Inferring transcriptional modules from ChIP-chip, motif and microarray data. Genome Biol 2006, 7(5):R37.
73. Wolfe C.J., Kohane I.S., Butte A.J. Systematic survey reveals general applicability of "guilt-by-association" within gene co-expression networks. BMC Bioinf 2005, 6:227.
74. Pico A.R., Kelder T., van Iersel M.P., Hanspers K., Conklin B.R., Evelo C. WikiPathways: pathway editing for the people. PLoS Biol 2008, 6:e184.
75. Heer J., Agrawala M. Design considerations for collaborative visual analytics. Inf Vis 2008, 7:49-62.
76. Burrage K., Hood L., Ragan M.A. Advanced computing for systems biology. Brief Bioinform 2006, 7:390-398.
77. Weber G.H., Rübel O., Huang M.Y., DePace A.H., Fowlkes C.C., Keränen S.V., Luengo Hendriks C.L., Hagen H., Knowles D.W., Malik J., Biggin M.D., Hamann B. Visual exploration of three-dimensional gene expression using physical views and linked abstract views. IEEE/ACM Trans Comput Biol Bioinform 2009, 6:296-309.
78. Kennedy M.C., O'Hagan A. Bayesian calibration of computer models. J R Stat Soc Ser B Stat Methodol 2001, 63:425-450.
79. Tarantola A. Inverse problem theory and methods for model parameter estimation 2005, SIAM, Philadelphia USA.
80. Members of Toxicogenomics Research Consortium Standardizing global gene expression analysis between laboratories and across platforms. Nat Methods 2005, 2(5):351-356.