Neural networks in bioinformatics

Handbook of Natural Computing

Volumn 1-4, Issue , 2012, Pages 565-583

  Chen, Ke ^a   Kurgan, Lukasz A ^a  

^a UNIVERSITY OF ALBERTA   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

BIOLOGY; COMPUTATIONAL METHODS; DNA SEQUENCES; FORECASTING; NETWORK ARCHITECTURE; NEURAL NETWORKS; NUCLEIC ACIDS; PROTEINS;

BIOLOGICAL PRINCIPLES; COMPUTATIONAL ANALYSIS; COMPUTATIONAL TOOLS; INDISPENSABLE TOOLS; MACROMOLECULAR STRUCTURES; NEURAL NETWORKS (NNS); PROTEIN SECONDARY STRUCTURE; SOLVENT ACCESSIBILITY;

BIOINFORMATICS;

EID: 84979658037     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1007/978-3-540-92910-9_18     Document Type: Chapter

References (65)

1. Adamczak R, Porollo A, Meller J (2005) Combining prediction of secondary structure and solvent accessibility in proteins. Proteins 59:467-475
2. Ahmad S, Gromiha MM, Sarai A (2003) Real value prediction of solvent accessibility from amino acid sequence. Proteins 50:629-635
3. Ahmad S, Gromiha MM, Sarai A (2004) Analysis and prediction of DNA-binding proteins and their binding residues based on composition, sequence and structural information. Bioinformatics 20:477-486
4. Ahmad S, Sarai A (2005) PSSM-based prediction of DNA binding sites in proteins. BMC Bioinformatics 6:33
5. Altschul SF, Madden TL, Schaffer AA, Zhang JH, Zhang Z, MillerW, Lipman DJ (1997) Gapped BLASTand PSIBLAST:a new generation of protein database search programs. Nucleic Acids Res 17:3389-3402
6. Blom N, Hansen J, Blaas D, Brunak S (1996) Cleavage site analysis in picornaviral polyproteins: discovering cellular targets by neural networks. Protein Sci 5:2203-2216
7. Boguski MS (1998) Bioinformatics-a new era. Trends Guide Bioinformatics (Suppl S):1-3
8. Byvatov E, Schneider G (2003) Support vector machine applications in bioinformatics. Appl Bioinformatics 2(2):67-77
9. Cai YD, Zhou GP (2000) Prediction of protein structural classes by neural network. Biochimie 82:783-785
10. Cai YD, Liu XJ, Chou KC (2002) Artificial neural network model for predicting protein subcellular location. Comput Chem 26:179-182
11. Cai YD, Liu XJ, Chou KC (2003) Prediction of protein secondary structure content by artificial neural network. J Comput Chem 24:727-731
12. Chandonia JM, Karplus M (1995) Neural networks for secondary structure and structural class predictions. Protein Sci 4:275-285
13. Chen J, Chaudhari N (2007) Cascaded bidirectional recurrent neural networks for protein secondary structure prediction. IEEE/ACM Trans Comput Biol Bioinform 4:572-582
14. Dor O, Zhou Y (2007a) Achieving 80% ten-fold crossvalidated accuracy for secondary structure prediction by large-scale training. Proteins 66:838-845
15. Dor O, Zhou Y (2007b) Real-SPINE: an integrated system of neural networks for real-value prediction of protein structural properties. Proteins 68:76-81
16. Emanuelsson O, Nielsen H, Brunak S, von Heijne G (2000) Predicting subcellular localization of proteins based on their N-terminal amino acid sequence. J Mol Biol 300:1005-1016
17. Fogel GB (2008) Computational intelligence approaches for pattern discovery in biological systems. Brief Bioinform 9(4):307-316
18. Fuchs A, Kirschner A, Frishman D (2009) Prediction of helix-helix contacts and interacting helices in polytopic membrane proteins using neural networks. Proteins 74:857-871
19. Garg A, Kaur H, Raghava GP (2005) Real value prediction of solvent accessibility in proteins using multiple sequence alignment and secondary structure. Proteins 61:318-324
20. Gromiha MM, Ahmad S, SuwaM (2005) TMBETA-NET:discrimination and prediction of membrane spanning beta-strands in outer membrane proteins. Nucleic Acids Res 33:W164-167
21. Hung LH, Samudrala R (2003) PROTINFO: secondary and tertiary protein structure prediction. Nucleic Acids Res 31:3296-3299
22. Ingrell CR, Miller ML, Jensen ON, Blom N (2007) Net-PhosYeast: prediction of protein phosphorylation sites in yeast. Bioinformatics 23:895-897
23. Jacoboni I, Martelli PL, Fariselli P, De Pinto V, Casadio R (2001) Prediction of the transmembrane regions of beta-barrel membrane proteins with a neural network-based predictor. Protein Sci 10:779-787
24. Jeong E, Chung IF, Miyano S (2004) A neural network method for identification of RNA-interacting residues in protein. Genome Inform 15:105-116
25. Jones DT (1999) Protein secondary structure prediction based on position-specific scoring matrices. J Mol Biol 292:195-202
26. Kabsch W, Sander C (1983) Dictionary of protein secondary structure: pattern recognition of hydrogenbonded and geometrical features. Biopolymers 22:2577-2637
27. Kapetanovic IM, Rosenfeld S, Izmirlian G (2004) Overview of commonly used bioinformatics methods and their applications. Ann NYAcad Sci 1020:10-21
28. Kaur H, Raghava GP (2003) A neural-network based method for prediction of gamma-turns in proteins from multiple sequence alignment. Protein Sci 12:923-929
29. Kaur H, Raghava GP (2004) A neural network method for prediction of beta-turn types in proteins using evolutionary information. Bioinformatics 20:2751-2758
30. Kirschner A, Frishman D (2008) Prediction of beta-turns and beta-turn types by a novel bidirectional Elmantype recurrent neural network with multiple output layers (MOLEBRNN). Gene 422:22-29
31. Kuang R, Leslie CS, Yang AS (2004) Protein backbone angle prediction with machine learning approaches. Bioinformatics 20:1612-1621
32. Kuznetsov IB, Gou Z, Li R, Hwang S (2006) Using evolutionary and structural information to predict DNA-binding sites on DNA-binding proteins. Proteins 64:19-27
33. Larranaga P, Calvo B, Santana R, Bielza C, Galdiano J, Inza I, Lozano JA, Armananzas R, Santafe G, Perez A, Robles V (2006) Machine learning in bioinformatics. Brief Bioinformatics 7(1):86-112
34. Lin CT, Lin KL, Yang CH, Chung IF, Huang CD, Yang YS (2005) Protein metal binding residue prediction based on neural networks. Int J Neural Syst 15:71-84
35. Lundegaard C, Lamberth K, HarndahlM, Buus S, Lund O, NielsenM (2008) NetMHC-3.0: accurate web accessible predictions of human, mouse and monkey MHC class I affinities for peptides of length 8-11. Nucleic Acids Res 36:W509-512
36. Luscombe NM, Greenbaum D, Gerstein M (2001) What is bioinformatics? A proposed definition and overview of the field. Methods Inf Med 40:346-358
37. Martelli PL, Fariselli P, Casadio R (2004) Prediction of disulfide-bonded cysteines in proteomes with a hidden neural network. Proteomics 4:1665-1671
38. Miller DJ, Wang Y, Kesidis G (2008) Emergent unsupervised clustering paradigms with potential application to bioinformatics. Front Biosci 13:677-690
39. Muskal SM, Kim SH (1992) Predicting protein secondary structure content A tandem neural network approach. J Mol Biol 225:713-727
40. Nantasenamat C, Isarankura-Na-Ayudhya C, Tansila N, Naenna T, Prachayasittikul V (2007) Prediction of GFP spectral properties using artificial neural network. J Comput Chem 28:1275-1289
41. Narayanan A, Keedwell EC, Olsson B (2002) Artificial intelligence techniques for bioinformatics. Appl Bioinformatics 1(4):191-222
42. Natt NK, Kaur H, Raghava GP (2004) Prediction of transmembrane regions of beta-barrel proteins using ANN-and SVM-based methods. Proteins 56:11-18
43. NIH Working Definition of Bioinformatics and Computational Biology (2000) BISTIC Definition Committee, http://www.bisti.nih.gov/
44. Niwa T (2004) Prediction of biological targets using probabilistic neural networks and atom-type descriptors. J Med Chem 47:2645-2650
45. Ofran Y, Rost B (2007) Protein-protein interaction hotspots carved into sequences. PLoS Comput Biol 3:e119
46. Petersen TN, Lundegaard C, Nielsen M, Bohr H, Bohr J, Brunak S, Gippert GP, Lund O (2000) Prediction of protein secondary structure at 80% accuracy. Proteins 41:17-20
47. Plewczynski D, Slabinski L, Ginalski K, Rychlewski L (2008) Prediction of signal peptides in protein sequences by neural networks. Acta Biochim Pol 55:261-267
48. Pollastri G, Baldi P, Fariselli P, Casadio R (2002a) Prediction of coordination number and relative solvent accessibility in proteins. Proteins 47:142-153
49. Pollastri G, Baldi P, Fariselli P, Casadio R (2002b) Prediction of coordination number and relative solvent accessibility in proteins. Proteins 47:142-153
50. Qian N, Sejnowski TJ (1988) Predicting the secondary structure of globular proteins using neural network models. J Mol Biol 202:865-884
51. Reinhardt A, Hubbard T (1998) Using neural networks for prediction of the subcellular location of proteins. Nucleic Acids Res 26:2230-2236
52. Rost B, Sander C (1994) Conservation and prediction of solvent accessibility in protein families. Proteins 20:216-226
53. Rost B, Sander C, Schneider R (1994) PHD-an automatic mail server for protein secondary structure prediction. Comput Appl Biosci 10:53-60
54. Ruan J, Wang K, Yang J, Kurgan LA, Cios KJ (2005) Highly accurate and consistent method for prediction of helix and strand content from primary protein sequences. Artif Intell Med 35:19-35
55. Saeys Y, Inza I, Larrañaga P (2007) A review of feature selection techniques in bioinformatics. Bioinformatics 23(19):2507-2517
56. Saha S, Raghava GP (2006) Prediction of continuous B-cell epitopes in an antigen using recurrent neural network. Proteins 65:40-48
57. Sidhu A, Yang ZR (2006) Prediction of signal peptides using bio-basis function neural networks and decision trees. Appl Bioinformatics 5:13-19
58. Vedani A, Dobler M (2000) Multi-dimensional QSAR in drug research. Predicting binding affinities, toxicity and pharmacokinetic parameters. Prog Drug Res 55:105-135
59. Wilkinson DJ (2007) Bayesian methods in bioinformatics and computational systems biology. Brief Bioinformatics 8(2):109-116
60. Xue B, Dor O, Faraggi E, Zhou Y (2008) Real-value prediction of backbone torsion angles. Proteins 72:427-433
61. Yang ZR, Thomson R (2005) Bio-basis function neural network for prediction of protease cleavage sites in proteins. IEEE Trans Neural Netw 16:263-274
62. Ye L, Liu T, Wu Z, Zhou R (2008) Sequence-based protein domain boundary prediction using BP neural network with various property profiles. Proteins 71:300-307
63. Zhang GZ, Huang DS (2004) Prediction of inter-residue contacts map based on genetic algorithm optimized radial basis function neural network and binary input encoding scheme. J Comput Aided Mol Des 18:797-810
64. Zhou HX, Shan Y (2001) Prediction of protein interaction sites from sequence profile and residue neighbor list. Proteins 44:336-343
65. Zou L, Wang Z, Huang J (2007) Prediction of subcellular localization of eukaryotic proteins using positionspecific profiles and neural network with weighted inputs. J Genet Genomics 34:1080-1087