A review of protein function prediction under machine learning perspective

Recent Patents on Biotechnology

Volumn 7, Issue 2, 2013, Pages 122-141

  Bernardes, Juliana S ^a   Pedreira, Carlos E ^a,b  

^a FEDERAL UNIVERSITY OF RIO DE JANEIRO   (Brazil)

^b University Hospital and School of Medicine   (Brazil)

Author keywords

Classification; Gene ontology; High throughput techniques; Machine learning; Pattern recognition; Protein

Indexed keywords

ARTIFICIAL INTELLIGENCE; FORECASTING; GENE ONTOLOGY; LEARNING SYSTEMS; MOLECULAR BIOLOGY; PATTERN RECOGNITION; THROUGHPUT;

COMPUTATIONAL TECHNIQUE; DATA REPRESENTATIONS; DIVERGENTS; FUNCTIONAL CHARACTERIZATION; GENE ONTOLOGY; GENOMIC ERA; HIGH-THROUGHPUT TECHNIQUE; MACHINE-LEARNING; PROTEIN FUNCTION PREDICTION; PROTEIN FUNCTIONS;

PROTEINS;

ACCURACY; ALGORITHM; AMINO ACID SEQUENCE; CATALYSIS; CELLULAR DISTRIBUTION; CLASSIFIER; GENE CONTROL; GENE EXPRESSION; MACHINE LEARNING; MATHEMATICAL COMPUTING; PREDICTION; PRIORITY JOURNAL; PROTEIN ANALYSIS; PROTEIN FAMILY; PROTEIN FOLDING; PROTEIN FUNCTION; PROTEIN PROTEIN INTERACTION; PROTEIN STRUCTURE; PROTEOMICS; REVIEW; SEQUENCE ALIGNMENT; SEQUENCE HOMOLOGY; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; CHEMISTRY; HIGH THROUGHPUT SCREENING; HUMAN; METABOLISM; PROCEDURES; STATISTICAL MODEL;

PROTEIN;

ARTIFICIAL INTELLIGENCE; HIGH-THROUGHPUT SCREENING ASSAYS; HUMANS; MODELS, STATISTICAL; PATTERN RECOGNITION, AUTOMATED; PROTEINS; PROTEOMICS;

EID: 84884262140     PISSN: 18722083     EISSN: None     Source Type: Journal    
DOI: 10.2174/18722083113079990006     Document Type: Review

References (313)

1. Altschul S, Gish W, Miller W, Myers E, Lipman D. Basic Local Alignment Search Tool. Journal Molecular Biology. 1990; 215: 403-410.
2. Pearson W. Rapid and sensitive sequence comparison with FASTP and FASTA. Methods Enzymol. 1985; 183: 63-98.
3. Rost B. Enzyme Function Less Conserved than Anticipated. Journal of Molecular Biology. 2002; 318(2): 595-608.
4. Schnoes A, Brown S, Dodevski I, Babbitt P. Annotation Error in Public Databases: Misannotation of Molecular Function in Enzyme Superfamilies. PLoS Computational Biology. 2009; 5(12): e1000605.
5. Gilks W, Audit B, Angelis D, Tsoka S, Ouzounis C. Percolation of annotation errors through hierarchically structured protein sequence databases. Mathematical Biosciences. 2005; 193(2): 223-234.
6. Andorf C, Dobbs D, Honavar V. Exploring inconsistencies in genome-wide protein function annotations: a machine learning approach. BMC Bioinformatics. 2007; 8(1): 284.
7. Ashburner M, Ball C, Blake J, et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nature genetics. 2000; 25(1): 25-29.
8. Larranaga P, Calvo B, Santana R, et al. Machine learning in bioinformatics. Briefings in Bioinformatics. 2006; 7(1): 86-112.
9. Abu-Mostafa Y, Magdon-Ismail M, H L. Learning from data. AMLBook; 2012.
10. Rost B, Liu J, Nair R, Wrzeszczynski K, Ofran Y. Automatic prediction of protein function. Cellular and Molecular Life Sciences. 2003; 60: 2637-2650.
11. Galea C, High A, Obenauer J, et al. Large-Scale Analysis of Thermostable, Mammalian Proteins Provides Insights into the Intrinsically Disordered Proteome. Journal of Proteome Research. 2009; 8(1): 211-226.
12. Mohan A, Uversky V, Radivojac P. Influence of Sequence Changes and Environment on Intrinsically Disordered Proteins. PLoS Computational Biology. 2009; 5(9): e1000497.
13. Chou K, Shen H. Recent progress in protein subcellular location prediction. Analytical Biochemistry. 2007; 370(1): 1-16.
14. Bairoch A. The ENZYME database in 2000. Nucleic Acids Research. 2000; 28(1): 304-305.
15. Ruepp A, Zollner A, Maier D, et al. The FunCat, a functional annotation scheme for systematic classification of proteins from whole genomes. Nucleic Acids Research. 2004; 32(18): 5539-5545.
16. Devos D, Valencia A. Practical limits of function prediction. Proteins: Structure, Function, and Bioinformatics. 2000; 41(1): 98-107.
17. Wilson CA, Kreychman J, Gerstein M. Assessing annotation transfer for genomics: quantifying the relations between protein sequence, structure and function through traditional and probabilistic scores. Journal of Molecular Biology. 2000; 297(1): 233-249.
18. Brennan RG, Matthews BW. The helix-turn-helix DNA binding motif. Journal of Biological Chemistry. 1989; 264(4): 1903-1906.
19. Han L, Cui J, Lin H, et al. Recent progresses in the application of machine learning approach for predicting protein functional class independent of sequence similarity. Proteomics. 2006; 6(14): 4023-4037.
20. Du P, Li T, Wang X. Recent progress in predicting protein subsubcellular locations. Expert Review of Proteomics. 2011; 8(3): 391-404.
21. Ferrer L, Dale J, Karp P. A systematic study of genome context methods: calibration, normalization and combination. BMC Bioinformatics. 2010; 11(1): 493.
22. Overbeek R, Fonstein M, D'Souza M, Pusch GD, Maltsev N. Use of Contiguity on the Chromosome to Predict Functional Coupling. In Silico Biology. 1999; 1(2): 93-108.
23. Enright AJ, Iliopoulos I, Kyrpides NC, Ouzounis CA. Protein interaction maps for complete genomes based on gene fusion events. Nature. 1999; 402(6757): 86-90.
24. Pellegrini M, Marcotte EM, Thompson MJ, Eisenberg D, Yeates TO. Assigning protein functions by comparative genome analysis: Protein phylogenetic profiles. Proceedings of the National Academy of Sciences. 1999; 96(8): 4285-4288.
25. Berman H, Battistuz T, Bhat T, et al. The Protein Data Bank. Acta Crystallographica Section D. 2002; 58: 899-907.
26. Gavin A, Bosche M, Krause R, et al. Functional organization of the yeast proteome by systematic analysis of protein complexes. Nature. 2002; 415(6868): 141-147.
27. Eisen MB, Spellman PT, Brown PO, Botstein D. Cluster analysis and display of genome-wide expression patterns. Proceedings of the National Academy of Sciences. 1998; 95(25): 14863-14868.
28. Saeys Y, Inza I, Larranaga P. A review of feature selection techniques in bioinformatics. Bioinformatics. 2007; 23(19): 2507-2517.
29. Haykins S. Neural Networks: A Comprehensive Foundation. Prentice Hall; 1998.
30. Boser B, Guyon I, Vapnik V. A training algorithm for optimal margin classifiers. In: Proceedings of the fifth annual ACM workshop on Computational learning theory. ACM Press; 1992. p. 144-152.
31. Cover T, Hart P. Nearest neighbor pattern classification. Information Theory, IEEE Transactions on. 1967; 13(1): 21-27.
32. Quinlan JR. C4.5: programs for machine learning. Morgan Kaufmann Publishers Inc.; 1993.
33. Kleinbaum DG, Kupper LL, Chambless LE. Logistic regression analysis of epidemiologic data: theory and practice. Communications in Statistics-Theory and Methods. 1982; 11(5): 485-547.
34. Friedman N, Geiger D, Goldszmidt M. Bayesian network classifiers. Machine Learning. 1997; 29: 131-163.
35. Dietterich T. Ensemble methods in machine learning. In: First International Workshop on Multiple Classifier Systems. London, UK: Springer-Verlag; 2000. p. 1-15.
36. Forgy E. Cluster analysis of multivariate data: efficiency versus interpretability of classifications. Biometrics. 1965; 21: 768-780.
37. Chou K. A novel approach to predicting protein structural classes in a (20-1)-D amino acid composition space. Proteins: Structure, Function, and Bioinformatics. 1995; 21(4): 319-344.
38. Chou K. Prediction of protein cellular attributes using pseudoamino acid composition. Proteins: Structure, Function, and Bioinformatics. 2001; 43(3): 246-255.
39. Scholkopf B, Smola AJ. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. Cambridge, MA: MIT Press; 2002.
40. Xie H, Wasserman A, Levine Z, Novik A, Grebinskiy V, Shoshan A, et al. Large-Scale Protein Annotation through Gene Ontology. Genome Research. 2002; 12(5): 785-794.
41. Abascal F, Valencia A. Automatic annotation of protein function based on family identification. Proteins: Structure, Function, and Bioinformatics. 2003; 53(3): 683-692.
42. Rappoport N, Karsenty S, Stern A, Linial N, Linial M. ProtoNet 6.0: organizing 10 million protein sequences in a compact hierarchical family tree. Nucleic Acids Research. 2012; 40: D313-D320.
43. Altschul S, Madden T, Schaffer A, et al. Gapped Blast and Psi-Blast: A New Generation of Protein Database Search Programs. Nucleic Acids Research. 1997; 25: 3389-3402.
44. Karwath A, King R. Homology Induction: the use of machine learning to improve sequence similarity searches. BMC Bioinformatics. 2002; 3: 11.
45. Muggleton S, De Raedt L. Inductive logic programming: Theory and methods. Journal of Logic Programming. 1994; 19/20: 629-679.
46. Shah AR, Oehmen CS, Webb-Robertson B. SVM-HUSTLE-An iterative semi-supervised machine learning approach for pairwise protein remote homology detection. Bioinformatics. 2008; 24(6): 783-790.
47. Webb-Robertson BJ, Ratuiste K, Oehmen C. Physicochemical property distributions for accurate and rapid pairwise protein homology detection. BMC Bioinformatics. 2010; 11(1): 145.
48. Clark WT, Radivojac P. Analysis of protein function and its prediction from amino acid sequence. Proteins: Structure, Function, and Bioinformatics. 2011; 79(7): 2086-2096.
49. Sara OS, Atalay V, Cetin-Atalay R. GOPred: GO Molecular Function Prediction by Combined Classifiers. PLoS ONE. 2010; 5(8): e12382.
50. Ouzounis CA, Coulson RMR, Enright AJ, Kunin V, Pereira-Leal JB. Classification schemes for protein structure and function. Nat Rev Genet. 2003; 4(7): 508-519.
51. Servant F, Bru C, Carrere S, et al. ProDom: Automated clustering of homologous domains. Briefings in Bioinformatics. 2002; 3(3): 246-251.
52. Yan Y, Moult J. Protein Family Clustering for Structural Genomics. Journal of Molecular Biology. 2005; 353(3): 744-759.
53. Enright AJ, Van Dongen S, Ouzounis CA. An efficient algorithm for large-scale detection of protein families. Nucleic Acids Research. 2002; 30(7): 1575-1584.
54. Li L, Stoeckert CJ, Roos DS. OrthoMCL: Identification of Ortholog Groups for Eukaryotic Genomes. Genome Research. 2003; 13(9): 2178-2189.
55. Yona G, Linial N, Linial M. ProtoMap: automatic classification of protein sequences and hierarchy of protein families. Nucleic Acids Research. 2000; 28(1): 49-55.
56. Miele V, Penel S, Daubin V, Picard F, Kahn D, Duret L. Highquality sequence clustering guided by network topology and multiple alignment likelihood. Bioinformatics. 2012; 28(8): 1078-1085.
57. Nepusz T, Sasidharan R, Paccanaro A. SCPS: a fast implementation of a spectral method for detecting protein families on a genome-wide scale. BMC Bioinformatics. 2010; 11(1): 120.
58. Apeltsin L, Morris JH, Babbitt PC, Ferrin TE. Improving the quality of protein similarity network clustering algorithms using the network edge weight distribution. Bioinformatics. 2011; 27(3): 326-333.
59. Uchiyama I. Hierarchical clustering algorithm for comprehensive orthologous-domain classification in multiple genomes. Nucleic Acids Research. 2006; 34(2): 647-658.
60. Kriventseva EV, Fleischmann W, Zdobnov EM, Apweiler R. CluSTr: a database of clusters of SWISS-PROT+TrEMBL proteins. Nucleic Acids Research. 2001; 29(1): 33-36.
61. Sasson O, Linial N, Linial M. The metric space of proteinscomparative study of clustering algorithms. Bioinformatics. 2002; 18(suppl 1): S14-S21.
62. Ma Q, Chirn GW, Cai R, Szustakowski J, Nirmala N. Clustering protein sequences with a novel metric transformed from sequence similarity scores and sequence alignments with neural networks. BMC Bioinformatics. 2005; 6(1): 242.
63. Chen C, Chung W, Su C. Exploiting homogeneity in protein sequence clusters for construction of protein family hierarchies. Pattern Recognition. 2006; 39(12): 2356-2369.
64. Tetko I, Facius A, Ruepp A, Mewes HW. Super paramagnetic clustering of protein sequences. BMC Bioinformatics. 2005; 6(1): 82.
65. Fayech S, Essoussi N, Limam M. Partitioning clustering algorithms for protein sequence data sets. BioData Mining. 2009; 2(1): 3.
66. Eddy S. Profile hidden Markov models. Bioinformatics. 1998; 14: 755-763.
67. Shindyalov I, Bourne P. Protein structure alignment by incremental combinatorial extension (CE) of the optimal path. Protein Engineering. 1998; 11(9): 739-747.
68. Holm L, Sander C. Protein Structure Comparison by Alignment of Distance Matrices. Journal of Molecular Biology. 1993; 233(1): 123-138.
69. Madej T, Gibrat J, Bryant S. Threading a database of protein cores. Proteins. 1995; 23(3): 356-369.
70. Bernardes J, Davila A, Costa V, Zaverucha G. Improving model construction of profile HMMs for remote homology detection through structural alignment. BMC Bioinformatics. 2007; 435: 1-12.
71. Frith MC, Forrest AR, Nourbakhsh E, et al. The Abundance of Short Proteins in the Mammalian Proteome. PLoS Genetic. 2006; 2(4): e52.
72. Wu C, Berry M, Shivakumar S, McLarty J. Neural Networks for Full-Scale Protein Sequence Classification: Sequence Encoding with Singular Value Decomposition. Machine Learning. 1995; 21: 177-193.
73. De Souza Rodrigues T, Cardoso FC, Teixeira SMR, Oliveira SC, Braga AP. Protein Classification with Extended-Sequence Coding by Sliding Window. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 2011; 8: 1721-1726.
74. Pasquier C, Promponas VJ, Hamodrakas SJ. PRED-CLASS: Cascading neural networks for generalized protein classification and genome-wide applications. Proteins: Structure, Function, and Bioinformatics. 2001; 44(3): 361-369.
75. Blekas K, Fotiadis DI, Likas A. Motif-Based Protein Sequence Classification Using Neural Networks. Journal of Computational Biology. 2005; 12(1): 64-82.
76. Hsi-Lien CW, Wu CH, lien Chen H, ju Lo C, Mclarty JW. Motif Identification Neural Design For Rapid And Sensitive Protein Family Search. CABIOS. 1996; 12: 109-118.
77. Hochreiter S, Heusel M, Obermayer K. Fast model-based protein homology detection without alignment. Bioinformatics. 2007; 23(14): 1728-1736.
78. Marla S, Bharatiya D, Bala M, Singh V, Kumar A. Classification of rice seed storage proteins using neural networks. Journal of Plant Biochemistry and Biotechnology. 2010; 19(1): 123-126.
79. Weinert W, Lopes H. Neural networks for protein classification. Appl Bioinformatics. 2004; 3: 41-48.
80. Shahbaba B, Neal R. Gene function classification using Bayesian models with hierarchy-based priors. BMC Bioinformatics. 2006; 7(1): 448.
81. Cai Y, Doig AJ. Prediction of Saccharomyces cerevisiae protein functional class from functional domain composition. Bioinformatics. 2004; 20(8): 1292-1300.
82. Cheng BYM, Carbonell JG, Klein-Seetharaman J. Protein classification based on text document classification techniques. Proteins: Structure, Function, and Bioinformatics. 2005; 58(4): 955-970.
83. Leslie C, Eskin E, Cohen A, Weston J, Noble W. Mismatch String Kernels for Discriminative Protein Classification. Bioinformatics. 2004; 20: 467-476.
84. Kuang R, Ie E, Wang K, et al. Profile-based string kernels for remote homology detection and motif extraction. Journal of bioinformatics and computational biology. 2005; 3: 527-550.
85. Lingner T, Meinicke P. Remote homology detection based on oligomer distances. Bioinformatics. 2006; 22: 2224-2231.
86. Ben-Hur A, Brutlag D. Remote homology detection: a motif based approach. Bioinformatics. 2003; 19(suppl 1): i26-i33.
87. Handstad T, Hestnes A, Saetrom P. Motif kernel generated by genetic programming improves remote homology and fold detection. BMC Bioinformatics. 2007; 8(1): 23.
88. Hou Y, Hsu W, Lee ML, Bystroff C. Efficient remote homology detection using local structure. Bioinformatics. 2003; 19(17): 2294-2301.
89. Hou Y, Hsu W, Lee ML, Bystroff C. Remote homolog detection using local sequence-structure correlations. Proteins: Structure, Function, and Bioinformatics. 2004; 57(3): 518-530.
90. Jaakkola T, Diekhans M, Haussler D. A Discriminative Framework for Detecting Remote Protein Homologies. Journal of Computational Biology. 2000; 7: 95-114.
91. Liao L, Noble WS. Combining Pairwise Sequence Similarity and Support Vector Machines for Detecting Remote Protein Evolutionary and Structural Relationships. Journal of Computational Biology. 2004; 10: 857-868.
92. Varshavsky R, Fromer M, Man A, Linial M. When Less Is More: Improving Classification of Protein Families with a Minimal Set of Global Features. In: Algorithms in Bioinformatics. vol. 4645 of Lecture Notes in Computer Science. Springer Berlin / Heidelberg; 2007. p. 12-24.
93. Huixiao H, Qilong H, Roger P, et al. The accurate prediction of protein family from amino acid sequence by measuring features of sequence fragments. Journal of Computational Biology. 2009; 16(12): 1671-1688.
94. Huang DS, Zhao XM, Huang GB, Cheung YM. Classifying protein sequences using hydropathy blocks. Pattern Recognition. 2006; 39(12): 2293-2300.
95. Mohabatkar H, Beigi MM, Esmaeili A. Prediction of GABA(A) receptor proteins using the concept of Chou's pseudo-amino acid composition and support vector machine. Journal of Theoretical Biology. 2011; 281(1): 18-23.
96. Strope PK, Moriyama EN. Simple alignment-free methods for protein classification: A case study from G-protein-coupled receptors. Genomics. 2007; 89(5): 602-612.
97. Wang Y, Wang X, Yang Z, Deng N. Prediction of Enzyme Subfamily Class via Pseudo Amino Acid Composition by Incorporating the Conjoint Triad Feature. Protein and Peptide Letters. 2010; 17(11): 1441-1449.
98. Bernardes J, Carbone A, Zaverucha G. A discriminative method for family-based protein remote homology detection that combines inductive logic programming and propositional models. BMC Bioinformatics. 2011; 12: 83.
99. Cai C, Wang W, Sun L, Chen Y. Protein function classification via support vector machine approach. Mathematical Biosciences. 2003; 185: 111-122.
100. Cai C, Han L, Ji Z, Chen X, Chen Y. SVM-Prot: web-based support vector machine software for functional classification of a protein from its primary sequence. Nucleic Acids Research. 2003; 31(13): 3692-3697.
101. Cai CZ, Han LY, Ji ZL, Chen YZ. Enzyme family classification by support vector machines. Proteins: Structure, Function, and Bioinformatics. 2004; 55(1): 66-76.
102. Kalita MK, Nandal UK, Pattnaik A, et al. CyclinPred: A SVMBased Method for Predicting Cyclin Protein Sequences. PLoS ONE. 2008; 3(7): e2605.
103. Nanni L, Brahnam S, Lumini A. Wavelet images and Chou's pseudo amino acid composition for protein classification. Amino Acids. 2011; p. 443-451.
104. Freund Y, Schapire RE. A decision-theoretic generalization of online learning and an application to boosting. In: Proceedings of the Second European Conference on Computational Learning Theory. Springer-Verlag; 1995. p. 23-37.
105. Syed U, Yona G. Using a mixture of probabilistic decision trees for direct prediction of protein function. In: Proceedings of the seventh annual international conference on Research in computational molecular biology. RECOMB '03. ACM; 2003. p. 289-300.
106. Camoglu O, Can T, Singh A, Wang Y. Decision tree based information integration for automated protein classification. Journal of Bioinformatics and Computational Biology. 2005; 3(3): 717-742.
107. Koussounadis A, Redfern O, Jones D. Improving classification in protein structure databases using text mining. BMC Bioinformatics. 2009; 10(1): 129.
108. Nanni L, Brahnam S, Lumini A. High performance set of PseAAC and sequence based descriptors for protein classification. Journal of Theoretical Biology. 2010; 266(1): 1-10.
109. Shen H, Chou K. EzyPred: A top-down approach for predicting enzyme functional classes and subclasses. Biochemical and Biophysical Research Communications. 2007; 364(1): 53-59.
110. Nair R, Rost B. LOC3D: annotate sub-cellular localization for protein structures. Nucleic Acids Research. 2003; 31(13): 3337-3340.
111. Millar AH, Carrie C, Pogson B, Whelan J. Exploring the Function-Location Nexus: Using Multiple Lines of Evidence in Defining the Subcellular Location of Plant Proteins. The Plant Cell Online. 2009; 21(6): 1625-1631.
112. Park K, Kanehisa M. Prediction of protein subcellular locations by support vector machines using compositions of amino acids and amino acid pairs. Bioinformatics. 2003; 19(13): 1656-1663.
113. Chou K, Cai Y. Using Functional Domain Composition and Support Vector Machines for Prediction of Protein Subcellular Location. Journal of Biological Chemistry. 2002; 277(48): 45765-45769.
114. Wang W, Geng X, Dou Y, Liu T, Zheng X. Predicting protein subcellular localization by pseudo amino acid composition with a segment-weighted and features-combined approach. Protein and Peptide Letters. 2011; 18(5): 480-487.
115. Liao B, Jiang J, Zeng Q, Zhu W. Predicting apoptosis protein subcellular location with PseAAC by incorporating tripeptide composition. Protein and Peptide Letters. 2011; 18(11): 1086-1092.
116. Lin H, Ding H, Guo F, Huang J. Prediction of subcellular location of mycobacterial protein using feature selection techniques. Molecular Diversity. 2010; 14: 667-671.
117. Li F, Li Q. Predicting Protein Subcellular Location Using Chou's Pseudo Amino Acid Composition and Improved Hybrid Approach. Protein and Peptide Letters. 2008; 15(6): 612.
118. Yu X, Zheng X, Liu T, Dou Y, Wang J. Predicting subcellular location of apoptosis proteins with pseudo amino acid composition: approach from amino acid substitution matrix and auto covariance transformation. Amino Acids. 2012; 42: 1619-1625.
119. Cai Y, Liu X, Xu X, Chou K. Support vector machines for prediction of protein subcellular location by incorporating quasisequence-order effect. Journal of Cellular Biochemistry. 2002; 84(2): 343-348.
120. Matsuda S, Vert JP, Saigo H, Ueda N, Toh H, Akutsu T. A novel representation of protein sequences for prediction of subcellular location using support vector machines. Protein Science. 2005; 14(11): 2804-2813.
121. Pierleoni A, Martelli PL, Fariselli P, Casadio R. BaCelLo: a balanced subcellular localization predictor. Bioinformatics. 2006; 22(14): e408-e416.
122. Mak M, Guo J, Kung SY. PairProSVM: Protein Subcellular Localization Based on Local Pairwise Profile Alignment and SVM. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 2008; 5: 416-422.
123. Guo J, Pu X, Lin Y, Leung H. Protein subcellular localization based on PSI-BLAST and machine learning. Journal of Bioinformatics and Computational Biology. 2006; 4(6): 1181-1195.
124. Zou L, Wang Z, Huang J. Prediction of Subcellular Localization of Eukaryotic Proteins Using Position-Specific Profiles and Neural Network with Weighted Inputs. Journal of Genetics and Genomics. 2007; 34(12): 1080-1087.
125. Cai Y, Liu X, Chou K. Artificial neural network model for predicting protein subcellular location. Computers & Chemistry. 2002; 26(2): 179-182.
126. Mooney C, Wang Y, Pollastri G. SCLpred: protein subcellular localization prediction by N-to-1 neural networks. Bioinformatics. 2011; 27(20): 2812-2819.
127. Jia P, Qian Z, Zeng Z, Cai Y, Li Y. Prediction of subcellular protein localization based on functional domain composition. Biochemical and Biophysical Research Communications. 2007; 357(2): 366-370.
128. T W, J Y. Predicting subcellular localization of gram-negative bacterial proteins by linear dimensionality reduction method. Protein and Peptide Letters. 2010; 17(1): 32-37.
129. Jiang X, Wei R, Zhang T, Gu Q. Using the concept of Chou's pseudo amino acid composition to predict apoptosis proteins subcellular location: an approach by approximate entropy. Protein and Peptide Letters. 2008; 15(4): 392-396.
130. Zheng X, Liu T, Wang J. A complexity-based method for predicting protein subcellular location. Amino Acids. 2009; 37: 427-433.
131. Huang Y, Li Y. Prediction of protein subcellular locations using fuzzy k-NN method. Bioinformatics. 2004; 20(1): 21-28.
132. Gu Q, Ding YS, Jiang XY, Zhang TL. Prediction of subcellular location apoptosis proteins with ensemble classifier and feature selection. Amino Acids. 2010; 38: 975-983.
133. Li L, Zhang Y, Zou L, Li C, Yu B. An Ensemble Classifier for Eukaryotic Protein Subcellular Location Prediction Using Gene Ontology Categories and Amino Acid Hydrophobicity. PLoS ONE. 2012; 7(1): e31057.
134. Shen H, Chou K. Virus-mPLoc: a fusion classifier for viral protein subcellular location prediction by incorporating multiple sites. Journal of Biomolecular Structure & Dynamics. 2010; 28: 175-186.
135. Chou K, Shen HB. Predicting Eukaryotic Protein Subcellular Location by Fusing Optimized Evidence-Theoretic K-Nearest Neighbor Classifiers. Journal of Proteome Research. 2006; 5(8): 1888-1897.
136. Denoeux T. A k-nearest neighbor classification rule based on Dempster-Shafer theory. Systems, Man and Cybernetics, IEEE Transactions on. 1995; 25(5): 804-813.
137. Chou K, Shen HB. Large-scale plant protein subcellular location prediction. Journal of Cellular Biochemistry. 2007; 100(3): 665-678.
138. Zhou X, Chen C, Li Z, Zou X. Improved prediction of subcellular location for apoptosis proteins by the dual-layer support vector machine. Amino Acids. 2008; 35: 383-388.
139. Chou K. Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes. Bioinformatics. 2005; 21(1): 10-19.
140. Guo J, Lin Y, Liu X. GNBSL: A new integrative system to predict the subcellular location for Gram-negative bacteria proteins. Proteomics. 2006; 6(19): 5099-5105.
141. Bulashevska A, Eils R. Predicting protein subcellular locations using hierarchical ensemble of Bayesian classifiers based on Markov chains. BMC Bioinformatics. 2006; 7(1): 298.
142. Khan A, Majid A, Hayat M. CE-PLoc: An ensemble classifier for predicting protein subcellular locations by fusing different modes of pseudo amino acid composition. Computational Biology and Chemistry. 2011; 35(4): 218-229.
143. Shen Y, Burger G. Unite and conquer': enhanced prediction of protein subcellular localization by integrating multiple specialized tools. BMC Bioinformatics. 2007; 8(1): 420.
144. Levitt M, Chothia C. Structural patterns in globular proteins. Nature. 1976; 261(5561): 552-558.
145. Tanaka T, Yokoyama S, Kuroda Y. Improvement of domain linker prediction by incorporating loop-length-dependent characteristics. Peptide Science. 2006; 84(2): 161-168.
146. Orengo C, Michie A, Jones S, Jones D, Swindells M, Thornton J. CATH a hierarchic classification of protein domain structures. Structure. 1997; 5(8): 1093-1109.
147. Lo Conte L, Ailey B, Hubbard T, Brenner S, Murzin A, Chothia C. SCOP: a Structural Classification of Proteins database. Nucleic Acids Research. 2000; 28(1): 257-259.
148. Liu J, Rost B. Sequence-based prediction of protein domains. Nucleic Acids Research. 2004; 32(12): 3522-3530.
149. Miyazaki S, Kuroda Y, Yokoyama S. Characterization and prediction of linker sequences of multi-domain proteins by a neural network. Journal of Structural and Functional Genomics. 2002; 2: 37-51.
150. Nagarajan N, Yona G. Automatic prediction of protein domains from sequence information using a hybrid learning system. Bioinformatics. 2004; 20(9): 1335-1360.
151. Cheng J, Sweredoski M, Baldi P. DOMpro: Protein Domain Prediction Using Profiles, Secondary Structure, Relative Solvent Accessibility, and Recursive Neural Networks. Data Mining and Knowledge Discovery. 2006; 13: 1-10.
152. Schuster M, Paliwal KK. Bidirectional recurrent neural networks. IEEE Transactions on Signal Processing. 1997; 45(11): 2673-2681.
153. Walsh I, Martin A, Mooney C, Rubagotti E, Vullo A, Pollastri G. Ab initio and homology based prediction of protein domains by recursive neural networks. BMC Bioinformatics. 2009; 10(1): 195.
154. Sim J, Kim SY, Lee J. PPRODO: Prediction of protein domain boundaries using neural networks. Proteins: Structure, Function, and Bioinformatics. 2005; 59(3): 627-632.
155. Ye L, Liu T, Wu Z, Zhou R. Sequence-based protein domain boundary prediction using BP neural network with various property profiles. Proteins: Structure, Function, and Bioinformatics. 2008; 71(1): 300-307.
156. Yoo P, Sikder A, Zhou B, Zomaya A. Improved general regression network for protein domain boundary prediction. BMC Bioinformatics. 2008; 9(Suppl 1): S12.
157. Suyama M, Ohara O. DomCut: prediction of inter-domain linker regions in amino acid sequences. Bioinformatics. 2003; 19(5): 673-674.
158. Ebina T, Toh H, Kuroda Y. Loop-length-dependent SVM prediction of domain linkers for high-throughput structural proteomics. Peptide Science. 2009; 92(1): 1-8.
159. Sikder A, Zomaya A. Improving the performance of DomainDiscovery of protein domain boundary assignment using inter-domain linker index. BMC Bioinformatics. 2006; 7(Suppl 5): S6.
160. Chen P, Liu C, Burge L, et al. DomSVR: domain boundary prediction with support vector regression from sequence information alone. Amino Acids. 2010; 39: 713-726.
161. Kawashima S, Kanehisa M. AAindex: Amino Acid index database. Nucleic Acids Research. 2000; 28(1): 374.
162. Ebina T, Toh H, Kuroda Y. DROP: an SVM domain linker predictor trained with optimal features selected by random forest. Bioinformatics. 2011; 27(4): 487-494.
163. Breiman L. Random Forests. Machine Learning. 2001; 45: 5-32.
164. Zou S, Huang Y, Wang Y, Hu C, Liang Y, Zhou C. A Novel Method for Prediction of Protein Domain Using Distance-Based Maximal Entropy. In: Advances in Neural Networks. vol. 4492 of Lecture Notes in Computer Science. Springer Berlin / Heidelberg; 2007. p. 1264-1272.
165. Zhang B, Rychlewski L, Pawlowski K, Fetrow JS, Skolnick J, Godzik A. From fold predictions to function predictions: Automation of functional site conservation analysis for functional genome predictions. Protein Science. 1999; 8(5): 1104-1115.
166. Berezin C, Glaser F, Rosenberg J, et al. ConSeq: the identification of functionally and structurally important residues in protein sequences. Bioinformatics. 2004; 20(8): 1322-1324.
167. Bernardes J, Fernandez J, Vasconcelos A. Structural descriptor database: a new tool for sequence-based functional site prediction. BMC Bioinformatics. 2008; 9(1): 492.
168. Marchler-Bauer A, Panchenko AR, Shoemaker BA, Thiessen PA, Geer LY, Bryant SH. CDD: a database of conserved domain alignments with links to domain three-dimensional structure. Nucleic Acids Research. 2002; 30(1): 281-283.
169. Thomson R, Hodgman TC, Yang ZR, Doyle AK. Characterizing proteolytic cleavage site activity using bio-basis function neural networks. Bioinformatics. 2003; 19(14): 1741-1747.
170. Nielsen M, Lundegaard C, Worning P, et al. Reliable prediction of T-cell epitopes using neural networks with novel sequence representations. Protein Science. 2003; 12(5): 1007-1017.
171. Gutteridge A, Bartlett GJ, Thornton JM. Using A Neural Network and Spatial Clustering to Predict the Location of Active Sites in Enzymes. Journal of Molecular Biology. 2003; 330(4): 719-734.
172. Sankararaman S, Sha F, Kirsch JF, Jordan MI, Sjolander K. Active site prediction using evolutionary and structural information. Bioinformatics. 2010; 26(5): 617-624.
173. Ota M, Kinoshita K, Nishikawa K. Prediction of Catalytic Residues in Enzymes Based on Known Tertiary Structure, Stability Profile, and Sequence Conservation. Journal of Molecular Biology. 2003; 327(5): 1053-1064.
174. Petrova N, Wu C. Prediction of catalytic residues using Support Vector Machine with selected protein sequence and structural properties. BMC Bioinformatics. 2006; 7(1): 312.
175. Yahalom R, Reshef D, Wiener A, et al. Structure-based identification of catalytic residues. Proteins: Structure, Function, and Bioinformatics. 2011; 79(6): 1952-1963.
176. Bartlett GJ, Porter CT, Borkakoti N, Thornton JM. Analysis of Catalytic Residues in Enzyme Active Sites. Journal of Molecular Biology. 2002; 324(1): 105-121.
177. Dou Y, Geng X, Gao H, Yang J, Zheng X, Wang J. Sequence Conservation in the Prediction of Catalytic Sites. The Protein Journal. 2011; 30: 229-239.
178. Capra JA, Singh M. Predicting functionally important residues from sequence conservation. Bioinformatics. 2007; 23(15): 1875-1882.
179. Dou Y, Zheng X, Yang J, Wang J. Prediction of catalytic residues based on an overlapping amino acid classification. Amino Acids. 2010; 39: 1353-1361.
180. Sonavane S, Chakrabarti P. Prediction of Active Site Cleft Using Support Vector Machines. Journal of Chemical Information and Modeling. 2010; 50(12): 2266-2273.
181. Zhang T, Zhang H, Chen K, Shen S, Ruan J, Kurgan L. Accurate sequence-based prediction of catalytic residues. Bioinformatics. 2008; 24(20): 2329-2338.
182. Tong W, Williams RJ, Wei Y, Murga LF, Ko J, Ondrechen MJ. Enhanced performance in prediction of protein active sites with THEMATICS and support vector machines. Protein Science. 2008; 17(2): 333-341.
183. Tong W, Wei Y, Murga LF, Ondrechen MJ, Williams RJ. Partial Order Optimum Likelihood (POOL): Maximum Likelihood Prediction of Protein Active Site Residues Using 3D Structure and Sequence Properties. PLoS Comput Biol. 2009; 5(1): e1000266.
184. Chou K, Zhang C. A correlation-coefficient method to predicting protein-structural classes from amino acid compositions. European Journal of Biochemistry. 1992; 207(2): 429-433.
185. Yang J, Peng Z, Chen X. Prediction of protein structural classes for low-homology sequences based on predicted secondary structure. BMC Bioinformatics. 2010; 11(Suppl 1): S9.
186. Klein P, Delisi C. Prediction of protein structural class from the amino acid sequence. Biopolymers. 1986; 25(9): 1659-1672.
187. Zhang C, Chou K. An optimization approach to predicting protein structural class from amino acid composition. Protein Science. 1992; 1(3): 401-408.
188. Chou K, Liu W, Maggiora GM, Zhang C. Prediction and classification of domain structural classes. Proteins: Structure, Function, and Bioinformatics. 1998; 31(1): 97-103.
189. Cai Y, Liu X, Xu X, Zhou G. Support Vector Machines for predicting protein structural class. BMC Bioinformatics. 2001; 2(1): 3.
190. Anand A, Pugalenthi G, Suganthan P. Predicting protein structural class by SVM with class-wise optimized features and decision probabilities. Journal of Theoretical Biology. 2008; 253(2): 375-380.
191. Kurgan L, Cios K, Chen K. SCPRED: Accurate prediction of protein structural class for sequences of twilight-zone similarity with predicting sequences. BMC Bioinformatics. 2008; 9(1): 226.
192. Dai Q, Wu L, Li L. Improving protein structural class prediction using novel combined sequence information and predicted secondary structural features. Journal of Computational Chemistry. 2011; 32(16): 3393-3398.
193. Qin Y, Wang C, Yu X, Zhu J, Liu T, Zheng X. Predicting Protein Structural Class by Incorporating Patterns of Over-Represented kmers into the General form of Chou's PseAAC. Protein and Peptide Letters. 2012; 19(4): 388-397.
194. Sahu SS, Panda G. A novel feature representation method based on Chou's pseudo amino acid composition for protein structural class prediction. Computational Biology and Chemistry. 2010; 34(5): 320-327.
195. Xiao X, Wang P, Chou K. Predicting protein structural classes with pseudo amino acid composition: An approach using geometric moments of cellular automaton image. Journal of Theoretical Biology. 2008; 254(3): 691-696.
196. Zhang T, Ding Y. Using pseudo amino acid composition and binary-tree support vector machines to predict protein structural classes. Amino Acids. 2007; 33(4): 623-629.
197. Chen C, Tian Y, Zou X, Cai P, Mo J. Using pseudo-amino acid composition and support vector machine to predict protein structural class. Journal of Theoretical Biology. 2006; 243(3): 444-448.
198. Liu T, Geng X, Zheng X, Li R, Wang J. Accurate prediction of protein structural class using auto covariance transformation of PSI-BLAST profiles. Amino Acids. 2012; 42(6): 2243-2249.
199. Zhang S, Ye F, Yuan X. Using principal component analysis and support vector machine to predict protein structural class for lowsimilarity sequences via PSSM. Journal of Biomolecular Structure and Dynamics. 2012; 29(6): 1138-1146.
200. Liu T, Zheng X, Wang J. Prediction of protein structural class for low-similarity sequences using support vector machine and PSIBLAST profile. Biochimie. 2010; 92(10): 1330-1334.
201. Chen K, Kurgan LA, Ruan J. Prediction of protein structural class using novel evolutionary collocation-based sequence representation. Journal of Computational Chemistry. 2008; 29(10): 1596-1604.
202. Ding S, Zhang S, Li Y, Wang T. A novel protein structural classes prediction method based on predicted secondary structure. Biochimie. 2012; 94(5): 1166-1171.
203. Ahmadi Adl A, Nowzari-Dalini A, Xue B, Uversky VN, Qian X. Accurate prediction of protein structural classes using functional domains and predicted secondary structure sequences. Journal of Biomolecular Structure and Dynamics. 2012; 29(6): 1127-1137.
204. Mohammad T, Nagarajaram H. SVM-based method for protein structural class prediction using secondary structural content and structural information of amino acids. Journal of Bioinformatics and Computational Biology. 2011; 9(4): 489-502.
205. Liu T, Jia C. A high-accuracy protein structural class prediction algorithm using predicted secondary structural information. Journal of Theoretical Biology. 2010; 267(3): 272-275.
206. Chou K, Cai YD. Predicting protein structural class by functional domain composition. Biochemical and Biophysical Research Communications. 2004; 321(4): 1007-1009.
207. Arai H, Tochio N, Kato T, Kigawa T, Yamamura M. An Accurate Prediction Method for Protein Structural Class from Signal Patterns of NMR Spectra in the Absence of Chemical Shift Assignments. In: Proceedings of the 2010 IEEE International Conference on Bioinformatics and Bioengineering. Washington, DC, USA: IEEE Computer Society; 2010. p. 32-37.
208. Cai Y, Zhou G. Prediction of protein structural classes by neural network. Biochimie. 2000 01; 82: 1-3.
209. Jahandideh S, Hoseini S, Jahandideh M, Davoodi M. A hybrid genetic-neural model for predicting protein structural classes. Biologia. 2009; 64: 649-654.
210. Jahandideh S, Abdolmaleki P, Jahandideh M, Asadabadi EB. Novel two-stage hybrid neural discriminant model for predicting proteins structural classes. Biophysical Chemistry. 2007; 128(1): 87-93.
211. Dubchak I, Muchnik I, Mayor C, Dralyuk I, Kim S. Recognition of a protein fold in the context of the SCOP classification. Proteins: Structure, Function and Genetics. 1999; 35(4): 401-407.
212. Wang Z, Yuan Z. How good is prediction of protein structural class by the component-coupled method? Proteins: Structure, Function, and Bioinformatics. 2000; 38(2): 165-175.
213. Zhang T, Ding Y, Chou K. Prediction protein structural classes with pseudo-amino acid composition: Approximate entropy and hydrophobicity pattern. Journal of Theoretical Biology. 2008; 250(1): 186-193.
214. Zheng X, Li C, Wang J. An information-theoretic approach to the prediction of protein structural class. Journal of Computational Chemistry. 2010; 31(6): 1201-1206.
215. Liu T, Zheng X, Wang J. Prediction of protein structural class using a complexity-based distance measure. Amino Acids. 2010; 38(3): 721-728.
216. Wu J, Li M, Yu L, Wang C. An Ensemble Classifier of Support Vector Machines Used to Predict Protein Structural Classes by Fusing Auto Covariance and Pseudo-Amino Acid Composition. The Protein Journal. 2010; 29: 62-67.
217. Chen L, Lu L, Feng K, et al. Multiple classifier integration for the prediction of protein structural classes. Journal of Computational Chemistry. 2009; 30(14): 2248-2254.
218. Kedarisetti KD, Kurgan L, Dick S. Classifier ensembles for protein structural class prediction with varying homology. Biochemical and Biophysical Research Communications. 2006; 348(3): 981-988.
219. Drmota M. Random Trees: An Interplay between Combinatorics and Probability. Springer Publishing Company, Incorporated; 2009.
220. Dong L, Yuan Y, Cai Y. Using Bagging classifier to predict protein domain structural class. Journal of Biomolecular Structure and Dynamics. 2006; 24(3): 239-242.
221. Niu B, Cai Y, Lu W, Li G, Chou K. Predicting protein structural class with AdaBoost learner. Protein and Peptide Letters. 2006; 13(5): 489-492.
222. Cai Y, Feng K, Lu W, Chou K. Using LogitBoost classifier to predict protein structural classes. Journal of Theoretical Biology. 2006; 238(1): 172-176.
223. Needleman SB, Wunsch CD. A general method applicable to the search for similarities in the amino acid sequence of two proteins. Journal of Molecular Biology. 1970; 48(3): 443-453.
224. Koretke KK, Russell RB, Lupas AN. Fold recognition from sequence comparisons. Proteins: Structure, Function, and Bioinformatics. 2001; 45(S5): 68-75.
225. Wallner B, Fang H, Ohlson T, Frey-Skott J, Elofsson A. Using evolutionary information for the query and target improves fold recognition. Proteins: Structure, Function, and Bioinformatics. 2004; 54(2): 342-350.
226. David R, Korenberg M, Hunter I. 3D-1D threading methods for protein fold recognition. Pharmacogenomics. 2000; 1(4): 445-455.
227. Juan D, Grana O, Pazos F, Fariselli P, Casadio R, Valencia A. A neural network approach to evaluate fold recognition results. Proteins: Structure, Function, and Bioinformatics. 2003; 50(4): 600-608.
228. Mishra P, Pandey PN. Recursive Neural Networks for Predicting Protein Folds from Their Pseudo Amino Acid Composition. Advanced Science Letters. 2012; 11(1): 63-66.
229. Okun O. K-Local Hyperplane Distance Nearest-Neighbor Algorithm and Protein Fold Recognition. Pattern Recognition and Image Analysis. 2006; 16(1): 19-22.
230. Melvin I, Ie E, Kuang R, Weston J, Noble W, Leslie C. SVM-Fold: a tool for discriminative multi-class protein fold and superfamily recognition. BMC Bioinformatics. 2007; 8(Suppl 4): S2.
231. Mohammad T, Nagarajaram H. A hierarchical approach to protein fold prediction. Journal of Integrative Bioinformatics. 2011; 8(1): 185.
232. Shamim MTA, Anwaruddin M, Nagarajaram HA. Support Vector Machine-based classification of protein folds using the structural properties of amino acid residues and amino acid residue pairs. Bioinformatics. 2007; 23(24): 3320-3327.
233. Dong Q, Zhou S, Guan J. A new taxonomy-based protein fold recognition approach based on autocross-covariance transformation. Bioinformatics. 2009; 25(20): 2655-2662.
234. Ding C, Dubchak I. Multi-class protein fold recognition using support vector machines and neural networks. Bioinformatics. 2001; 17(4): 349-358.
235. Landwehr N, Hall M, Frank E. Logistic Model Trees. Machine Learning. 2005; 59(1-2): 161-205.
236. Chen W, Liu X, Huang Y, Jiang Y, Zou Q, C L. Improved method for predicting protein fold patterns with ensemble classifiers. Genetics and Molecular Research. 2012; 11(1): 174-181.
237. Shen H, Chou K. Ensemble classifier for protein fold pattern recognition. Bioinformatics. 2006; 22(14): 1717-1722.
238. Yellaboina S, Goyal K, Mande SC. Inferring genome-wide functional linkages in E. coli by combining improved genome context methods: Comparison with high-throughput experimental data. Genome Research. 2007; 17(4): 527-535.
239. Chang G, Hong Y, Ko K, et al. Phylogenetic profiles reveal evolutionary relationships within the "twilight zone" of sequence similarity. Proceedings of the National Academy of Sciences. 2008; 105(36): 13474-13479.
240. Wu J, Kasif S, DeLisi C. Identification of functional links between genes using phylogenetic profiles. Bioinformatics. 2003; 19(12): 1524-1530.
241. Date SV, Marcotte EM. Discovery of uncharacterized cellular systems by genome-wide analysis of functional linkages. Nature Biotechnology. 2003; 21(9): 1055-1062.
242. Lin FPY, Lan R, Sintchenko V, Gilbert GL, Kong F, Coiera E. Computational Bacterial Genome-Wide Analysis of Phylogenetic Profiles Reveals Potential Virulence Genes of Streptococcus agalactiae. PLoS ONE. 2011; 6(4): e17964.
243. Nicotra L, Micheli A. Modeling adaptive kernels from probabilistic phylogenetic trees. Artificial Intelligence in Medicine. 2009; 45(2-3): 125-134.
244. Vert J. A tree kernel to analyse phylogenetic profiles. Bioinformatics. 2002; 18(suppl 1): S276-S284.
245. Tavazoie S, Hughes JD, Campbell MJ, Cho RJ, Church GM. Systematic determination of genetic network architecture. Nature Genetics. 1999; 22(3): 281-285.
246. Alter O, Brown PO, Botstein D. Singular value decomposition for genome-wide expression data processing and modeling. Proceedings of the National Academy of Sciences. 2000; 97(18): 10101-10106.
247. Tamayo P, Slonim D, Mesirov J, et al. Interpreting patterns of gene expression with self-organizing maps: Methods and application to hematopoietic differentiation. Proceedings of the National Academy of Sciences. 1999; 96(6): 2907-2912.
248. Toronen P, Kolehmainen M, Wong G, Castren E. Analysis of gene expression data using self-organizing maps. FEBS Letters. 1999; 451(2): 142-146.
249. Nikkila J, Toronen P, Kaski S, Venna J, Castren E, Wong G. Analysis and visualization of gene expression data using Self-Organizing Maps. Neural Networks. 2002; 15(8-9): 953-966.
250. Herrero J, Valencia A, Dopazo J. A hierarchical unsupervised growing neural network for clustering gene expression patterns. Bioinformatics. 2001; 17(2): 126-136.
251. Brown MPS, Grundy WN, Lin D, Cristianini N, Sugnet CW, Furey TS, et al. Knowledge-based analysis of microarray gene expression data by using support vector machines. Proceedings of the National Academy of Sciences. 2000; 97(1): 262-267.
252. Mateos A, Dopazo J, Jansen R, Tu Y, Gerstein M, Stolovitzky G. Systematic Learning of Gene Functional Classes From DNA Array Expression Data by Using Multilayer Perceptrons. Genome Research. 2002; 12(11): 1703-1715.
253. Pandey G, Myers C, Kumar V. Incorporating functional interrelationships into protein function prediction algorithms. BMC Bioinformatics. 2009; 10(1): 142.
254. Legrain P, Wojcik J, Gauthier JM. Protein-protein interaction maps: a lead towards cellular functions. Trends in Genetics. 2001; 17(6): 346-352.
255. Chen Y, Xu D. Computational analyses of high-throughput proteinprotein interaction data. Current Protein and Peptide Science. 2003; 4(3): 159-181.
256. Stark C, Breitkreutz BJ, Chatr-aryamontri A, et al. The BioGRID Interaction Database: 2011 update. Nucleic Acids Research. 2011; 39(suppl 1): D698-D704.
257. Salwinski L, Miller CS, Smith AJ, Pettit FK, Bowie JU, Eisenberg D. The Database of Interacting Proteins: 2004 update. Nucleic Acids Research. 2004; 32(suppl 1): D449-D451.
258. Schwikowski B, Uetz P, Fields S. A network of protein-protein interactions in yeast. Nature Biotechnology. 2000; 18(12): 1257-1261.
259. Chua H, Sung W, Wong L. Exploiting indirect neighbours and topological weight to predict protein function from protein-protein interactions. Bioinformatics. 2006; 22(13): 1623-1630.
260. Vazquez A, Flammini A, Maritan A, Vespignani A. Global protein function prediction from protein-protein interaction networks. Nature Biotechnology. 2003; 21(6): 697-700.
261. Nabieva E, Jim K, Agarwal A, Chazelle B, Singh M. Wholeproteome prediction of protein function via graph-theoretic analysis of interaction maps. Bioinformatics. 2005; 21(suppl 1): i302-i310.
262. Bader G, Hogue C. An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics. 2003; 4(1): 2.
263. Watts DJ, Strogatz SH. Collective dynamics of small-worldnetworks. Nature. 1998; 393(6684): 440-442.
264. Spirin V, Mirny LA. Protein complexes and functional modules in molecular networks. Proceedings of the National Academy of Sciences. 2003; 100(21): 12123-12128.
265. Blatt M, Wiseman S, Domany E. Superparamagnetic Clustering of Data. Phys Rev Lett. 1996; 76: 3251-3254.
266. Pereira-Leal JB, Enright AJ, Ouzounis CA. Detection of functional modules from protein interaction networks. Proteins: Structure, Function, and Bioinformatics. 2004; 54(1): 49-57.
267. Hartuv E, Shamir R. A clustering algorithm based on graph connectivity. Information Processing Letters. 2000; 76(4-6): 175-181.
268. Glover F. Tabu Search-Part I. ORSA Journal on Computing. 1989; 1(3): 190-206.
269. King A, Przulj N, Jurisica I. Protein complex prediction via costbased clustering. Bioinformatics. 2004; 20(17): 3013-3020.
270. Samanta MP, Liang S. Predicting protein functions from redundancies in large-scale protein interaction networks. Proceedings of the National Academy of Sciences. 2003; 100(22): 12579-12583.
271. Maciag K, Altschuler SJ, Slack MD, et al. Systems-level analyses identify extensive coupling among gene expression machines. Mol Syst Biol. 2006; 2.
272. Goldberg DS, Roth FP. Assessing experimentally derived interactions in a small world. Proceedings of the National Academy of Sciences. 2003; 100(8): 4372-4376.
273. Arnau V, Mars S, Marin I. Iterative Cluster Analysis of Protein Interaction Data. Bioinformatics. 2005; 21(3): 364-378.
274. Brun C, Chevenet F, Martin D, Wojcik J, Guénoche A, Jacq B. Functional classification of proteins for the prediction of cellular function from a protein-protein interaction network. Genome biology. 2003; 5(1).
275. Shi M, Xia J, Li X, Huang D. Predicting protein-protein interactions from sequence using correlation coefficient and highquality interaction dataset. Amino Acids. 2010; 38: 891-899.
276. Zaki N, Lazarova-Molnar S, El-Hajj W, Campbell P. Proteinprotein interaction based on pairwise similarity. BMC Bioinformatics. 2009; 10(1): 150.
277. Craig R, Liao L. Phylogenetic tree information aids supervised learning for predicting protein-protein interaction based on distance matrices. BMC Bioinformatics. 2007; 8(1): 6.
278. Chen X, Han B, Fang J, Haasl RJ. Large-scale Protein-Protein Interaction prediction using novel kernel methods. Int J Data Min Bioinformatics. 2008; 2(2): 145-156.
279. Yamanishi Y, Vert JP, Kanehisa M. Protein network inference from multiple genomic data: a supervised approach. Bioinformatics. 2004; 20(suppl 1): i363-i370.
280. Ben-Hur A, Noble WS. Kernel methods for predicting proteinprotein interactions. Bioinformatics. 2005; 21(suppl 1): i38-i46.
281. Ma Z, Zhou C, Lu L, Ma Y, Sun P, Cui Y. Predicting proteinprotein interactions based on BP neural network. In: Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine Workshops; 2007. p. 3-7.
282. Eom J, Zhang B. Prediction of Protein Interaction with Neural Network-Based Feature Association Rule Mining. In: Neural Information Processing. vol. 4234 of Lecture Notes in Computer Science. Springer Berlin/Heidelberg; 2006. p. 30-39.
283. Jansen R, Gerstein M. Analyzing protein function on a genomic scale: the importance of gold-standard positives and negatives for network prediction. Current Opinion in Microbiology. 2004; 7(5): 535-545.
284. Zhang L, Wong S, King O, Roth F. Predicting co-complexed protein pairs using genomic and proteomic data integration. BMC Bioinformatics. 2004; 5(1): 38.
285. Singh R, Xu J, Berger B. Struct2Net: Integrating Structure into Protein-Protein Interaction Prediction. Pacific Symposium on Biocomputing. 2006; p. 403-414.
286. Lin N, Wu B, Jansen R, Gerstein M, Zhao H. Information assessment on predicting protein-protein interactions. BMC Bioinformatics. 2004; 5(1): 154.
287. Qi Y, Klein-Seetharaman J, Bar-Joseph Z. Random forest similarity for protein-protein interaction prediction from multiple sources. Pacific Symposium on Biocomputing. 2005; 10: 531-542.
288. Qi Y, Bar-Joseph Z, Klein-Seetharaman J. Evaluation of different biological data and computational classification methods for use in protein interaction prediction. Proteins: Structure, Function, and Bioinformatics. 2006; 63(3): 490-500.
289. Jensen LJ, Gupta R, Blom N, et al. Prediction of Human Protein Function from Post-translational Modifications and Localization Features. Journal of Molecular Biology. 2002; 319(5): 1257-1265.
290. Lobley A, Swindells MB, Orengo CA, Jones DT. Inferring Function Using Patterns of Native Disorder in Proteins. PLoS Computational Biology. 2007; 3(8): e162.
291. Iakoucheva LM, Dunker AK. Order, Disorder, and Flexibility: Prediction from Protein Sequence. Structure. 2003; 11(11): 1316-1317.
292. Wass MN, Barton G, Sternberg MJE. CombFunc: predicting protein function using heterogeneous data sources. Nucleic Acids Research. 2012; 40(W1): W466-W470.
293. Wolpert D. Stacked generalization. Neural Networks. 1992; 5(2): 241-259.
294. Ré M, Valentini G. Simple ensemble methods are competitive with state-of-the-art data integration methods for gene function prediction. Journal of Machine Learning Research-Proceedings Track. 2010; 8: 98-111.
295. Kuncheva L, Bezdek J, Duin R. Decision templates for multiple classifier fusion: an experimental comparison. Pattern Recognition. 2001; 34: 299-314.
296. Guan Y, Myers C, Hess D, Barutcuoglu Z, Caudy A, Troyanskaya O. Predicting gene function in a hierarchical context with an ensemble of classifiers. Genome Biology. 2008; 9(Suppl 1): S3.
297. Obozinski G, Lanckriet G, Grant C, Jordan M, Noble W. Consistent probabilistic outputs for protein function prediction. Genome Biology. 2008; 9(Suppl 1): S6.
298. Valentini G. True Path Rule Hierarchical Ensembles for Genome-Wide Gene Function Prediction. Computational Biology and Bioinformatics, IEEE/ACM Transactions on. 2011; 8(3): 832-847.
299. Schietgat L, Vens C, Struyf J, Blockeel H, Kocev D, Dzeroski S. Predicting gene function using hierarchical multi-label decision tree ensembles. BMC Bioinformatics. 2010; 11(1): 2.
300. Lanckriet GRG, Deng M, Cristianini N, Jordan MI, Noble WS. Kernel-Based Data Fusion and Its Application to Protein Function Prediction in Yeast. In: Pacific Symposium on Biocomputing; 2004. p. 300-311.
301. Pavlidis P, Cai J, Weston J, Noble WS. Learning Gene Functional Classifications From Multiple Data Types. Journal of Computational Biology. 2002; 9: 401-411.
302. Lanckriet GRG, De Bie T, Cristianini N, Jordan MI, Noble WS. A statistical framework for genomic data fusion. Bioinformatics. 2004; 20(16): 2626-2635.
303. Sonnenburg S, Ratsch G, Schafer C, Scholkopf B. Large Scale Multiple Kernel Learning. Journal of Machine Learning Research. 2006; 7: 1531-1565.
304. Lee H, Tu Z, Deng M, Sun F, Chen T. Diffusion Kernel-Based Logistic Regression Models for Protein Function Prediction. OMICS: A Journal of Integrative Biology. 2006; 10(1): 40-55.
305. Jaimovich A, Elidan G, Margalit H, Friedman N. Towards an Integrated Protein-Protein Interaction Network: A Relational Markov Network Approach. Journal of Computational Biology. 2006; 13(2): 145-164.
306. Troyanskaya OG, Dolinski K, Owen AB, Altman RB, Botstein D. A Bayesian framework for combining heterogeneous data sources for gene function prediction (in Saccharomyces cerevisiae. Proceedings of the National Academy of Sciences. 2003; 100(14): 8348-8353.
307. Nariai N, Kolaczyk ED, Kasif S. Probabilistic Protein Function Prediction from Heterogeneous Genome-Wide Data. PLoS ONE. 2007; 2(3): e337.
308. Hanisch D, Zien A, Zimmer R, Lengauer T. Co-clustering of biological networks and gene expression data. Bioinformatics. 2002; 18(suppl 1): S145-S154.
309. Tornow S, Mewes HW. Functional modules by relating protein interaction networks and gene expression. Nucleic Acids Research. 2003; 31(21): 6283-6289.
310. Tanay A, Sharan R, Kupiec M, Shamir R. Revealing modularity and organization in the yeast molecular network by integrated analysis of highly heterogeneous genomewide data. Proceedings of the National Academy of Sciences of the United States of America. 2004; 101(9): 2981-2986.
311. Shiga M, Takigawa I, Mamitsuka H. Annotating gene function by combining expression data with a modular gene network. Bioinformatics. 2007; 23(13): i468-i478.
312. Jeffery C. Moonlighting proteins. Trends in Biochemical Sciences. 1999; 24(1): 8-11.
313. Jeffery C. Proteins with neomorphic moonlighting functions in disease. IUBMB Life. 2011; 63(7): 489-494.