Learning node labels with multi-category Hopfield networks

Neural Computing and Applications

Volumn 27, Issue 6, 2016, Pages 1677-1692

  Frasca, Marco ^a   Bassis, Simone ^a   Valentini, Giorgio ^a  

^a UNIVERSITY OF MILAN   (Italy)

Author keywords

Binary classification; Biological networks; Multi category Hopfield network; Protein function prediction; Unbalanced graphs

Indexed keywords

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BIOINFORMATICS; FORECASTING; GRAPHIC METHODS; LEARNING ALGORITHMS; LEARNING SYSTEMS; PROTEINS; WORLD WIDE WEB;

BINARY CLASSIFICATION; BIOLOGICAL NETWORKS; HOPFIELD NETWORKS; PROTEIN FUNCTION PREDICTION; UNBALANCED GRAPHS;

CLASSIFICATION (OF INFORMATION);

EID: 84932097860     PISSN: 09410643     EISSN: None     Source Type: Journal    
DOI: 10.1007/s00521-015-1965-1     Document Type: Article

References (71)

1. Ashburner M et al (2000) Gene ontology: tool for the unification of biology. Gene ontology consortium. Nat Genet 25(1):25–29
2. Atencia M, Joya G, Sandoval F (2004) Parametric identification of robotic systems with stable time-varying Hopfield networks. Neural Comput Appl 13(4):270–280. doi:10.1007/s00521-004-0421-4
3. Attwood TK, Bradley P, Flower DR, Gaulton A, Maudling N, Mitchell A, Moulton G, Nordle A, Paine K, Taylor P et al (2003) Prints and its automatic supplement, preprints. Nucl Acids Res 31(1):400–402
4. Azran A (2007) The rendezvous algorithm: multi- class semi-supervised learning with Markov random walks. In: Proceedings of the 24th international conference on machine learning (ICML)
5. Bairoch A, Apweiler R (1997) the SWISS-PROT protein sequence data bank and its supplement TrEMBL. Nucl Acids Res 25(1):31–36
6. Bengio Y, Delalleau O, Le Roux N (2006) Label propagation and quadratic criterion. In: Chapelle O, Scholkopf B, Zien A (eds) Semi supervised learning. MIT Press, Cambridge, pp 193–216
7. Bertoni A, Frasca M, Valentini G (2011) Cosnet: a cost sensitive neural network for semi-supervised learning in graphs. In: ECML/PKDD (1), Lecture Notes in Computer Science, vol 6911, pp 219–234. Springer
8. Bhagat S, Cormode G, Muthukrishnan S (2011) Node classification in social networks. CoRR abs/1101.3291
9. Bogdanov P, Singh AK (2010) Molecular function prediction using neighborhood features. IEEE/ACM Trans Comput Biol Bioinform 7:208–217
10. Brent R (1973) Algorithms for minimization without derivatives. Prentice-Hall, New Jersey
11. Chaudhari G, Avadhanula V, Sarawagi S (2014) A few good predictions: selective node labeling in a social network. In: Proceedings of the 7th ACM international conference on web search and data mining, WSDM ’14, pp 353–362. ACM, New York. doi:10.1145/2556195.2556241
12. Chen RM, Huang YM (2001) Multiprocessor task assignment with fuzzy Hopfield neural network clustering technique. Neural Comput Appl 10(1):12–21. doi:10.1007/s005210170013
13. Cheng Z, Caverlee J, Lee K (2010) You are where you tweet: a content-based approach to geo-locating twitter users. In: Proceedings of the 19th ACM international conference on information and knowledge management., CIKM ’10ACM, New York, pp 759–768
14. Chua HN, Sung WK, Wong L (2006) Exploiting indirect neighbours and topological weight to predict protein function from protein-protein interactions. Bioinformatics 22:1623–1630
15. Deng M, Chen T, Sun F (2004) An integrated probabilistic model for functional prediction of proteins. J Comput Biol 11:463–475
16. Elkan C (2001) The foundations of cost-sensitive learning. In: Proceedings of the seventeenth international joint conference on artificial intelligence, pp 973–978
17. Erdem MH, Ozturk Y (1996) A new family of multivalued networks. Neural Netw 9(6):979–989
18. Ertoz L, Steinbach M, Kumar V (2002) A new shared nearest neighbor clustering algorithm and its applications. In: Workshop on clustering high dimensional data and its applications at 2nd SIAM international conference on data mining
19. Finn RD, Mistry J, Schuster-Böckler B, Griffiths-Jones S, Hollich V, Lassmann T, Moxon S, Marshall M, Khanna A, Durbin R et al (2006) Pfam: clans, web tools and services. Nucl Acids Res 34(suppl 1):D247–D251
20. Frasca M (2015) Automated gene function prediction through gene multifunctionality in biological networks. Neurocomputing. doi: 10.1016/j.neucom.2015.04.007. http://www.sciencedirect.com/science/article/pii/S0925231215004142. In press
21. Frasca M, Bertoni A et al (2013) A neural network algorithm for semi-supervised node label learning from unbalanced data. Neural Netw 43:84–98
22. Frasca M, Pavesi G (2013) A neural network based algorithm for gene expression prediction from chromatin structure. In: IJCNN, pp 1–8. IEEE
23. Gough J, Karplus K, Hughey R, Chothia C (2001) Assignment of homology to genome sequences using a library of hidden Markov models that represent all proteins of known structure. J Mol Biol 313(4):903–919
24. Guyon I, Cawley G, Dror G (eds) (2011) Hands-on pattern recognition: challenges in machine learning, challenges in machine learning, vol 1. Microtome Publishing, Brookline
25. Hebb DO (2002) The organization of behavior: a neuropsychological theory. Lawrence Erlbaum Associates Inc, US, Mahwah. http://www.loc.gov/catdir/enhancements/fy0659/2002018867-d.html
26. Hopfield J (1982) Neural networks and physical systems with emergent collective computational abilities. Proc Natl Acad Sci USA 79:2554–2558
27. Hulo N, Bairoch A, Bulliard V, Cerutti L, De Castro E, Langendijk-Genevaux PS, Pagni M, Sigrist CJ (2006) The PROSITE database. Nucl Acids Res 34(suppl 1):D227–D230
28. Jarvis RA, Patrick EA (1973) Clustering using a similarity measure based on shared near neighbors. IEEE Trans Comput 22(11):1025–1034
29. Karaoz U et al (2004) Whole-genome annotation by using evidence integration in functional-linkage networks. Proc Natl Acad Sci USA 101:2888–2893
30. Kohler S, Bauer S, Horn D, Robinson P (2008) Walking the interactome for prioritization of candidate disease genes. Am J Human Genet 82(4):948–958
31. Kordos M, Duch W (2008) Variable step search algorithm for feedforward networks. Neurocomputing 71(13–15):2470–2480. doi:10.1016/j.neucom.2008.02.019
32. Lan L et al (2013) MS-kNN: protein function prediction by integrating multiple data sources. BMC Bioinformatics 14(Suppl 3:S8)
33. Letunic I, Copley RR, Pils B, Pinkert S, Schultz J, Bork P (2006) Smart 5: domains in the context of genomes and networks. Nucl Acids Res 34(suppl 1):D257–D260
34. Ling C, Sheng V (2010) Class imbalance problem. In: Sammut C, Webb G (eds) Encyclopedia of machine learning, Springer, US, pp 171–171. doi:10.1007/978-0-387-30164-8_110
35. Ling C, Sheng V (2010) Cost-sensitive learning. In: Sammut C, Webb G (eds) Encyclopedia of machine learning, Springer, US, pp. 231–235. doi:10.1007/978-0-387-30164-8_181
36. Lovász L (1996) Random walks on graphs: a survey. In: Miklós D, Sós VT, Szőnyi T (eds) Combinatorics, Paul Erdős is eighty, vol 2. János Bolyai Mathematical Society, Budapest, pp 353–398
37. Ma J (1999) The object perceptron learning algorithm on generalised Hopfield networks for associative memory. Neural Comput Appl 8(1):25–32. doi:10.1007/s005210050004
38. Marcotte E, Pellegrini M, Thompson M, Yeates T, Eisenberg D (1999) A combined algorithm for genome-wide prediction of protein function. Nature 402:83–86
39. Mayer ML, Hieter P (2000) Protein networks-built by association. Nat Biotechnol 18(12):1242–3
40. Mérida-Casermeiro E, Galán-Marín G, Muñoz Pérez J (2001) An efficient multivalued Hopfield network for the traveling salesman problem. Neural Process Lett 14(3):203–216. doi:10.1023/A:1012751230791
41. Mesiti M, Re M, Valentini G (2014) Think globally and solve locally: secondary memory-based network learning for automated multi-species function prediction. Giga Sci 3:5. doi:10.1186/2047-217X-3-5
42. Mislove A, Viswanath B, Gummadi KP, Druschel P (2010) You are who you know: inferring user profiles in online social networks. In: Proceedings of the third ACM international conference on web search and data mining, WSDM ’10. ACM, New York, pp 251–260. doi:10.1145/1718487.1718519
43. Mostafavi S, Morris Q (2010) Fast integration of heterogeneous data sources for predicting gene function with limited annotation. Bioinformatics 26(14):1759–1765
44. Mostafavi S, Ray D, Farley DW, et al (2008) GeneMANIA: a real-time multiple association network integration algorithm for predicting gene function. Genome Biol 9(Suppl 1), S4+
45. Mulder NJ, Apweiler R, Attwood TK, Bairoch A, Bateman A, Binns D, Bork P, Buillard V, Cerutti L, Copley R et al (2007) New developments in the InterPro database. Nucl Acids Res 35(suppl 1):D224–D228
46. Muller J, Szklarczyk D, Julien P, Letunic I, Roth A, Kuhn M, Powell S, von Mering C, Doerks T, Jensen LJ et al (2010) eggnog v2. 0: extending the evolutionary genealogy of genes with enhanced non-supervised orthologous groups, species and functional annotations. Nucl Acids Res 38(suppl 1):D190–D195
47. Murali TM, Wu CJ, Kasif S (2006) The art of gene function prediction. Nat Biotechnol 24(12):1474–1475. doi:10.1038/nbt1206-1474
48. Muruganantham G, Bhakat RS (2013) A review of impulse buying behavior. Int J Mark Stud 5(3):p149
49. Nabieva E, Jim K, Agarwal A, Chazelle B, Singh M (2005) Whole-proteome prediction of protein function via graph-theoretic analysis of interaction maps. Bioinformatics 21(S1):302–310
50. Neagu D, Palade V (2003) A neuro-fuzzy approach for functional genomics data interpretation and analysis. Neural Comput Appl 12(3–4):153–159. doi:10.1007/s00521-003-0388-6
51. Nie F, Xiang S, Liu Y, Zhang C (2010) A general graph-based semi-supervised learning with novel class discovery. Neural Comput Appl 19(4):549–555. doi:10.1007/s00521-009-0305-8
52. Pena-Castillo L, Tasan M, Myers C et al (2008) A critical assessment of Mus musculus gene function prediction using integrated genomic evidence. Genome Biol 9:S1
53. Radivojac P et al (2013) A large-scale evaluation of computational protein function prediction. Nat Methods 10(3):221–227
54. Re M, Mesiti M, Valentini G (2012) A fast ranking algorithm for predicting gene functions in biomolecular networks. IEEE/ACM Trans Comput Biol Bioinform 9(6):1812–1818. doi:10.1109/TCBB.2012.114
55. Re M, Valentini G (2012) Cancer module genes ranking using kernelized score functions. BMC Bioinform 13(Suppl 14/S3). doi:10.1186/1471-2105-13-S14-S3. http://www.biomedcentral.com/bmcbioinformatics/supplements/13/S14/S3
56. Salavati AH, Kumar KR, Shokrollahi A (2013) A non-binary associative memory with exponential pattern retrieval capacity and iterative learning: Extended Results. CoRR abs/1302.1156
57. Schwikowski B, Uetz P, Fields S (2000) A network of protein-protein interactions in yeast. Nat Biotechnol 18(12):1257–1261
58. Silva I, Moody G, Scott DJ, Celi LA, Mark RG (2012) Predicting in-hospital mortality of icu patients: the physionet/computing in cardiology challenge 2012. Comput Cardiol 39:245–248. http://www.biomedsearch.com/nih/Predicting-In-Hospital-Mortality-ICU/24678516.html
59. Szummer M, Jaakkola T (2001) Partially labeled classification with Markov random walks. In: Advances in neural information processing systems (NIPS) 14:945–952. MIT Press
60. Tsuda K, Shin H, Scholkopf B (2005) Fast protein classification with multiple networks. Bioinformatics 21(Suppl 2):ii59–ii65
61. Valentini G, Paccanaro A, Caniza H, Romero A, Re M (2014) An extensive analysis of disease-gene associations using network integration and fast kernel-based gene prioritization methods. Artif Intell Med 61(2):63–78. doi:10.1016/j.artmed.2014.03.003
62. Vazquez A, Flammini A, Maritan A, Vespignani A (2003) Global protein function prediction from protein-protein interaction networks. Nat Biotechnol 21:697–700
63. Wilcoxon F (1945) Individual comparisons by ranking methods. Biometrics 1:80–83
64. Wolfram Research Inc: Mathematica (2012) http://www.wolfram.com/mathematica/. Version 9.0
65. Wong AK, Park CY, Greene CS, Bongo LA, Guan Y, Troyanskaya OG (2012) Imp: a multi-species functional genomics portal for integration, visualization and prediction of protein functions and networks. Nucl Acids Res 40(W1):W484–W490
66. Xue H, Chen S (2011) Glocalization pursuit support vector machine. Neural Comput Appl 20(7):1043–1053. doi:10.1007/s00521-010-0448-7
67. Yoon K, Kwek S (2007) A data reduction approach for resolving the imbalanced data issue in functional genomics. Neural Comput Appl 16(3):295–306. doi:10.1007/s00521-007-0089-7
68. Youngs N, Penfold-Brown D, Drew K, Shasha D, Bonneau R (2013) Parametric Bayesian priors and better choice of negative examples improve protein function prediction. Bioinformatics 29(9):btt110–1198. doi:10.1093/bioinformatics/btt110
69. Zhou D et al (2004) Learning with local and global consistency. In: Thrun S, Saul L, Schölkopf B (eds) Advances in neural information processing systems 16:321–328. MIT Press. http://papers.nips.cc/paper/2506-learning-with-local-and-global-consistency
70. Zhu X, Ghahramani Z, Lafferty J (2003) Semi-supervised learning using Gaussian fields and harmonic functions. In. In ICML, pp 912–919
71. Zurada JM, Cloete I, van der Poel E (1996) Generalized Hopfield networks for associative memories with multi-valued stable states. Neurocomputing 13(24):135–149