Prediction of protein subcellular localization by incorporating multiobjective PSO-based feature subset selection into the general form of Chou’s PseAAC

Medical and Biological Engineering and Computing

Volumn 53, Issue 4, 2015, Pages 331-344

  Mandal, Monalisa ^a   Mukhopadhyay, Anirban ^a   Maulik, Ujjwal ^b  

^a UNIVERSITY OF KALYANI   (India)

^b JADAVPUR UNIVERSITY   (India)

Author keywords

Amino acid composition; Feature selection; Multiobjective optimization; Particle swarm optimization; Protein subcellular localization

Indexed keywords

AMINO ACIDS; ENTROPY; MULTIOBJECTIVE OPTIMIZATION; PARTICLE SWARM OPTIMIZATION (PSO); PROTEINS; REDUNDANCY;

AMINO ACID COMPOSITIONS; AMINO ACIDS COMPOSITIONS; FEATURE SUBSET SELECTION; MINIMUM REDUNDANCY-MAXIMUM RELEVANCES; MULTI OBJECTIVE PARTICLE SWARM OPTIMIZATION; PROTEIN SUBCELLULAR LOCALIZATION; PSEUDO AMINO ACID COMPOSITIONS; SEQUENTIAL FORWARD SEARCH;

FEATURE EXTRACTION;

AMINO ACID; PROTEIN;

ACCURACY; ALGORITHM; AMINO ACID COMPOSITION; AMINO ACID SEQUENCE; ARTICLE; BIOINFORMATICS; CELL NUCLEUS; CELL VACUOLE; CELLULAR DISTRIBUTION; CHLOROPLAST; CLUSTER ANALYSIS; CONTROLLED STUDY; CORRELATION ANALYSIS; CYTOSKELETON; ENDOPLASMIC RETICULUM; ENTROPY; F SCORE; FISHER EXACT TEST; GOLGI COMPLEX; LYSOSOME; MITOCHONDRION; MULTIOBJECTIVE OPTIMIZATION; PEROXISOME; PREDICTION; PRIORITY JOURNAL; PROBABILITY; PROCESS OPTIMIZATION; PROTEIN ANALYSIS; PSEUDO AMINO ACID COMPOSITION; SCORING SYSTEM; BIOLOGICAL MODEL; BIOLOGY; CHEMISTRY; INTRACELLULAR SPACE; PROCEDURES; STATISTICAL MODEL;

ALGORITHMS; AMINO ACID SEQUENCE; AMINO ACIDS; COMPUTATIONAL BIOLOGY; INTRACELLULAR SPACE; MODELS, BIOLOGICAL; MODELS, STATISTICAL; PROTEINS;

EID: 84925498137     PISSN: 01400118     EISSN: 17410444     Source Type: Journal    
DOI: 10.1007/s11517-014-1238-7     Document Type: Article

References (51)

1. Algoul S, Alam MS, Hossain MA, Majumder MAA (2011) Multi-objective optimal chemotherapy control model for cancer treatment. Med Biol Eng Comput 49:51–65
2. Bulashevska A, Eils R (2006) Predicting protein subcellular locations using hierarchical ensemble of bayesian classifiers based on markov chains. BMC Bioinform 7(298):44–48
3. Cai YD, Chou KC (2004) Predicting subcellular localization of proteins in a hybridization space. Bioinformatics 20:1151–1156
4. Cao DS, Xu QS, Liang YZ (2013) propy: a tool to generate various modes of Chou’s pseAAC. Bioinformatics 29:960–962
5. Cheok MH, Yang W, Pui CH, Downing JR, Cheng C, Naeve CW, Relling MV, Evans WE (2003) Characterization of pareto dominance. Oper Res Lett 31(1):7–11
6. Chi SM (2010) Prediction of protein subcellular localization by weighted gene ontology terms. Biochem Biophys Res Commun 399(3):402–405
7. Chou K (2011) Some remarks on protein attribute prediction and pseudo amino acid composition. J Theor Biol 273(1):236–247
8. Chou K, Wu Z, Xiao X (2012) iLoc-Hum: Using accumulation-label scale to predict subcellular locations of human proteins with both single and multiple sites. Mol Biosyst 8:629–641
9. Chou KC (2001) Prediction of protein cellular attributes using pseudo-amino acid composition. Proteins Struct Funct Bioinform 43(3):246–255
10. Chou KC (2013) Some remarks on predicting multi-label attributes in molecular biosystems. Mol Biosyst 9:1092–1100
11. Chou KC, Cai YD (2002) Using functional domain composition and support vector machines for prediction of protein subcellular location. J Biol Chem 277(48):45765–45769
12. Chou KC, Elrod DW (1999) Protein subcellular location prediction. Protein Eng 12(2):107–118
13. Chou KC, Shen HB (2006) Predicting eukaryotic protein subcellular location by fusing optimized evidence-theoretic k-nearest neighbor classifiers. J Proteome Res 5:1888–1897
14. Chou KC, Shen HB (2007) Review: recent progresses in protein subcellular location prediction. Anal Biochem 370(1):1–16
15. Cover TM, Thomas JA (2006) Entropy, relative entropy and mutual information. Elements of information theory. Wiley, New York
16. Dash M., Liu H.(2000) Unsupervised feature selection. In: Proceedings of the Pacific Asia conference knowledge discovery and data mining
17. Deb K (2001) Multi-objective optimization using evolutionary algorithms. Wiley, Oxford
18. Deb K, Pratap A, Agrawal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6:182–197
19. Devijver PA, Kittler J (1982) Pattern recognition: a statistical approach. Prentice Hall, Englewood Cliffs
20. Ding C., Peng H.(2003) Minimum redundancy feature selection from microarray gene expression data. In: Proceedings of the computational systems bioinformatics (CSB03)
21. Du P, Gu S, Jiao Y (2014) Pseaac-general: fast building various modes of general form of Chou’s pseudo-amino acid composition for large-scale protein datasets. Int J Mol Sci 15:3495–3506
22. Du P, Wang X, Xu C, Gao Y (2012) PseAAC-builder: a cross-platform stand-alone program for generating various special chou’s pseudo-amino acid compositions. Anal Biochem 425:117–119
23. Garg A, Bhasin M, Raghava GPS (2005) Support vector machine-based method for subcellular location of human proteins using amino acid compositions, their order and similarity search. J Biol Chem 280:14,427–14,432
24. Hall MA, Smith LA(1999) Feature selection for machine learning: comparing a correlation-based filter approach to the wrapper. In: The twelfth international Florida artificial intelligence research society conference, Orlando, Florida, USA
25. Hou J, Shi W, Li G, Zhou W (2007) An effective non-parametric method for globally clustering genes from expression profiles. Med Biol Eng Comput 45:1175–1185
26. Kamandar M, Ghassemian H (2009) A cluster-based feature selection approach. In: International conference on hybrid artificial intelligence systems
27. Kamandar M, Ghassemian H (2011) Maximum relevance, minimum redundancy band selection for hyperspectral images. In: 19th Iranian conference on electrical engineering (ICEE)
28. Khanesar MA, Teshnehlab M, Shoorehdeli MA (2007) A novel binary particle swarm optimization. In: Mediterranean conference on control and automation
29. Lin WZ, Fang JA, Xiao X (2013) iLoc-animal: a multi-label learning classifier for predicting subcellular localization of animal proteins. Mol Biosyst 9:634–644
30. Lustgarten J, Gopalakrishnan V, Visweswaran S (2009) Measuring stability of feature selection in biomedical datasets. In: Annual symposium proceedings/AMIA symposium, pp 406–410
31. Marcano-Cedeno A, Quintanilla-Dominguez J, Cortina-Januchs M, Andina D (2010) Feature selection using sequential forward selection and classification applying artificial metaplasticity neural network. In: 36th annual conference on IEEE industrial electronics society, pp 2845–2850
32. Mohamad MS, Omatu S, Deris S, Yoshioka M (2011) An improved binary particle swarm optimization algorithm for gene selection and classification of colon cancer data. In: Advances in bioinformatics. Penerbit UTM
33. Nakai K, Kanehisa M (1992) A knowledge base for predicting protein localization sites in eukaryotic cells. Genomics 14(4):897–911
34. Park KJ, Kanehisa M (2003) Prediction of protein subcellular locations by support vector machines using compositions of amino acids and amino acid pairs. Bioinformatics 19:1656–1663
35. Parsopoulos KE (2010) Particle swarm optimization and intelligence: advances and applications. Information science reference, Hershey, New York
36. Pirogova E, Vojisavljevic V, Caceres J, Cosic I (2010) Ataxin active site determination using spectral distribution of electron ion interaction potentials of amino acids. Med Biol Eng Comput 48(4):303–309
37. Reinhardt A, Hubbard T (1998) Using neural networks for prediction of the subcellular location of proteins. Nucleic Acids Res 26:2230–2236
38. Shen HB, Chou KC (2007) Hum-mPLoc: an ensemble classifier for large-scale human protein subcellular location prediction by incorporating samples with multiple sites. Biochem Biophys Res Commun 355:1006–1011
39. Sierra MR, Coello CC (2006) Multi-objective particle swarm optimizers: A survey of the state-of-the-art. Int J Comput Intell Res 2(3):287–308
40. Song C, Shi F, Ma X (2009) Prediction of the subcellular location of apoptosis proteins based on approximate entropy. J Converg Inf Technol 4(4):118–122
41. Su ECY, Chiu HS, Lo A, Hwang JK, Sung TY, Hsu WL (2007) Protein subcellular localization prediction based on compartment-specific features and structure conservation. BMC Bioinform 8(330)
42. Wan S, Mak M, Kung S (2012) mGOASVM: multi-label protein subcellular localization based on gene ontology and support vector machines. BMC Bioinform 13(290)
43. Wu ZC, Xiao X (2011) iLoc-Euk: a multi-label classifier for predicting the subcellular localization of singleplex and multiplex eukaryotic proteins. PLoS One 6:629–641
44. Xiao X, Wu ZC (2011) iLoc-Virus: a multi-label learning classifier for identifying the subcellular localization of virus proteins with both single and multiple sites. J Theor Biol 284:42–51
45. Yang W, Lu B, Yang Y (2006) A comparative study on feature extraction from protein sequences for subcellular localization prediction. In: IEEE symposium on computational intelligence and bioinformatics and computational biology, pp 1–8
46. Yu CS, Lin CJ, Huwang JK (2004) Predicting subcellular localization of proteins for gram-negative bacteria by support vector machines based on n-peptide compositions. Protein Sci 13:1402–1406
47. Yu L, Liu H (2003) Feature selection for high-dimensional data: a fast correlation-based filter solution. In: 12th international conference on machine learning (ICML-2003)
48. Yu X, Zheng X, Liu T, Dou Y, Wang J (2012) Predicting subcellular location of apoptosis proteins with pseudo amino acid composition: approach from amino acid substitution matrix and auto covariance transformation. Amino Acids 42:1619–1625
49. Zhou GP, Doctor K (2003) Subcellular location prediction of apoptosis proteins. Proteins Struct Funct Bioinform 50(1):44–48
50. Zhou XB, Chen C, Li ZC, Zou XY (2008) Improved prediction of subcellular location for apoptosis proteins by the dual-layer support vector machine. Amino Acids 35:383–388
51. Zitzler E, Thiele L (1998) An evolutionary algorithm for multiobjective optimization: the strength pareto approach. Tech. Rep. 43, Zurich, Switzerland