Minimum curvilinearity to enhance topological prediction of protein interactions by network embedding

Bioinformatics

Volumn 29, Issue 13, 2013, Pages

  Cannistraci, Carlo Vittorio ^a,b   Alanis Lobato, Gregorio ^a,b   Ravasi, Timothy ^a,b  

^a KING ABDULLAH UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Saudi Arabia)

^b UNIVERSITY OF CALIFORNIA SAN DIEGO   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ARTICLE; GENE ONTOLOGY; METHODOLOGY; PROTEIN ANALYSIS; SYSTEMS BIOLOGY; PROCEDURES;

ALGORITHMS; GENE ONTOLOGY; PROTEIN INTERACTION MAPPING; SYSTEMS BIOLOGY;

EID: 84879935027     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btt208     Document Type: Conference Paper

References (31)

1. Ammirati, E. et al. (2012) Identification and predictive value of interleukin-6+ interleukin-10+ and interleukin-6- interleukin-10+ cytokine patterns in ST-elevation acute myocardial infarction. Circ. Res., 111, 1336-1348.
2. Basnet, K. (1993) Centering of data in principal component analysis in ecological ordination. Tribhuvan Univ. J., 16, 29-34.
3. Ben-Hur, A. and Noble, W.S. (2005) Kernel methods for predicting protein-protein interactions. Bioinformatics, 21, i38-i46.
4. Brun, C. et al. (2003) Functional classification of proteins for the prediction of cellular function from a protein-protein interaction network. Genome Biol., 5, R6-R6.
5. Cannistraci, C.V. et al. (2009) Median-modified Wiener filter provides efficient denoising, preserving spot edge and morphology in 2-DE image processing. Proteomics, 9, 4908-4919.
6. Cannistraci, C.V. et al. (2010) Nonlinear dimension reduction and clustering by Minimum Curvilinearity unfold neuropathic pain and tissue embryological classes. Bioinformatics (Oxford, England), 26, i531-539.
7. Cannistraci, C.V. et al. (2013) From link-prediction in brain connectomes and protein interactomes to the local-community-paradigm in complex networks. Sci. Rep., 3, 1613.
8. Cannistraci, C.V. et al. (2013) Pivotal role of the muscle-contraction pathway in cryptorchidism and evidence for genomic connections with cardiomyopathy pathways in RASopathies. BMC Med. Genomics, 6, 5.
9. Chen, J. et al. (2005) Discovering reliable protein interactions from high-throughput experimental data using network topology. Artif. Intell. Med., 35, 37-47.
10. Chen, J. et al. (2006) Increasing confidence of protein-protein interactomes. Genome Inform., 17, 284-297.
11. Chua, H.N. et al. (2006) Exploiting indirect neighbours and topological weight to predict protein function from protein-protein interactions. Bioinformatics, 22, 1623-1630.
12. Hinton, G. and Roweis, S. (2003) Stochastic neighbor embedding. Adv. Neural Inf. Proc. Syst., 15, 857-864.
13. Huang da, W. et al. (2009) Bioinformatics enrichment tools: Paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res., 37, 1-13.
14. Huang da, W. et al. (2009) Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc., 4, 44-57.
15. Jolliffe, I.T. (2002) Principal Component Analysis. Springer Series in Statistics, Springer-Verlag, New York.
16. Kuchaiev, O. et al. (2009) Geometric de-noising of protein-protein interaction networks. PLoS Comput. Biol., 5, e1000454.
17. Liu, Y.Y. et al. (2011) Controllability of complex networks. Nature, 473, 167-173.
18. Przulj, N. et al. (2004) Modeling interactome: Scale-free or geometric?. Bioinformatics, 20, 3508-3515.
19. Rhee, S.Y. et al. (2008) Use and misuse of the gene ontology annotations. Nat. Rev. Genet., 9, 509-515.
20. Saito, R. et al. (2002) Interaction generality, a measurement to assess the reliability of a protein-protein interaction. Nucleic Acids Res., 30, 1163-1168.
21. Saito, R. et al. (2003) Construction of reliable protein-protein interaction networks with a new interaction generality measure. Bioinformatics, 19, 756-763.
22. Sammon, J.W. (1969) Sammon Mapping.pdf. IEEE Trans. Comput., C-18, 401-409.
23. Shaw, B. and Jebara, T. (2009) Structure preserving embedding. In: Proceedings of the 26th International Conference on Machine Learning (ACM, Montreal).
24. Shieh, A.D. et al. (2011) Tree preserving embedding. Proc. Natl Acad. Sci. USA, 108, 16916-16921.
25. Szklarczyk, D. et al. (2011) The STRING database in 2011: Functional interaction networks of proteins, globally integrated and scored. Nucleic Acids Res., 39, D561-D568.
26. Tenenbaum, J.B. et al. (2000) A global geometric framework for nonlinear dimensionality reduction. Science, 290, 2319-2323.
27. van der Maaten, L. and Hinton, G. (2008) Visualizing data using t-SNE. J. Mach. Learn. Res., 9, 2579-2605.
28. Venna, J. and Kaski, S. (2006) Local multidimensional scaling. Neural Netw., 19, 889-899.
29. Venna, J. et al. (2010) Information retrieval perspective to nonlinear dimensionality reduction for data visualization. J. Mach. Learn. Res., 11, 451-490.
30. You, Z.-H. et al. (2010) Using manifold embedding for assessing and predicting protein interactions from high-throughput experimental data. Bioinformatics, 26, 2744-2751.
31. Zagar, L. et al. (2011) Stage prediction of embryonic stem cell differentiation from genome-wide expression data. Bioinformatics, 27, 2546-2553.