Protein subcellular localization of fluorescence microscopy images: Employing new statistical and Texton based image features and SVM based ensemble classification

Information Sciences

Volumn 345, Issue , 2016, Pages 65-80

  Tahir, Muhammad ^a,b   Khan, Asifullah ^b  

^a CITY UNIVERSITY OF SCIENCE AND INFORMATION TECHNOLOGY   (Pakistan)

^b PAKISTAN INSTITUTE OF ENGINEERING AND APPLIED SCIENCES   (Pakistan)

Author keywords

Ensemble classification; Fluorescence microscopy; Majority voting; Protein; Subcellular localization; SVM

Indexed keywords

EXTRACTION; FEATURE EXTRACTION; FLUORESCENCE; FLUORESCENCE MICROSCOPY; FORECASTING; IMAGE PROCESSING; IMAGE SEGMENTATION; OPTICAL CHARACTER RECOGNITION; PATTERN RECOGNITION; PROTEINS;

DISCRIMINATIVE FEATURE EXTRACTION; ENSEMBLE CLASSIFICATION; FLUORESCENCE MICROSCOPY IMAGES; GRAY-LEVEL CO-OCCURRENCE MATRIX; MAJORITY VOTING; OVERLAPPING INFORMATION; PROTEIN SUBCELLULAR LOCALIZATION; SUBCELLULAR LOCALIZATIONS;

CLASSIFICATION (OF INFORMATION);

EID: 84960172820     PISSN: 00200255     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ins.2016.01.064     Document Type: Article

References (51)

1. T.H. Afridi, A. Khan, Y.S. Lee Mito-GSAAC: mitochondria prediction using genetic ensemble classifier and split amino acid composition Amino Acids 42 2012 1443 1454
2. F. Albregtsen Statistical texture measures computed from gray level coocurrence matrices, Image processing laboratory, department of informatics 2008 1 14
3. M.R. Bakhtiarizadeh, M. Moradi-Shahrbabak, M. Ebrahimi, E. Ebrahimie Neural network and SVM classifiers accurately predict lipid binding proteins, irrespective of sequence homology J. Theory Biol. 356 2014 213 222
4. D. Baumgartner, G. Serpen Performance of global-local hybrid ensemble versus boosting and bagging ensembles Int. J. Mach. Learn. Cybernet. 4 2013 301 317
5. A. Ben-hur, J. Weston, A User's Guide to Support Vector Machines, in, Department of Computer Science, Colorado State University, 2007.
6. M.V. Boland, R.F. Murphy A neural network classifier capable of recognizing the patterns of all major subcellular structures in fluorescence microscope images of HeLa cells Bioinform. 17 2001 1213 1223
7. A. Chaddad, C. Tanougast, A. Dandache, A. Bouridane, J. Charara, A.A. Houseini Classification of cancer cells based on Haralick's Coefficients using Multi-spectral images 7th European Symposium on Biomedical Engineering, International Federation for Medical and Biological Engineering, Springer Verlag IFBME Proceedings Series Porto Carras, Chalkidiki, Greece 2010
8. C.C. Chang, C.J. Lin LIBSVM: a library for support vector machines ACM Trans. Intell. Syst. Technol. 2 2011
9. A. Chebira, Y. Barbotin, C. Jackson, T. Merryman, G. Srinivasa, R.F. Murphy, J. Kovacevic A multiresolution approach to automated classification of protein subcellular location images BMC Bioinform. 8 2007 210
10. S.-C. Chen, T. Zhao, G.J. Gordon, R.F. Murphy Automated image analysis of protein localization in budding yeast Bioinformatics 23 2007 i66 i71
11. T.-T. Chen, S.-J. Lee A weighted LS-SVM based learning system for time series forecasting Inf. Sci. 299 2015 99 116
12. K.-C. Chou some remarks on protein attribute prediction and pseudo amino acid composition (50th Anniversary Year Review) J. Theor. Biol. 273 2011 236 247
13. A. Eleyan, H. Demirel Co-occurrence matrix and its statistical features as a new approach for face recognition Turk J. Electr. Eng. Comput. Sci. 19 2011 97 107
14. D.N. Georgiou, T.E. Karakasidis, J.J. Nieto, A. Torres Use of fuzzy clustering technique and matrices to classify amino acids and its impact to Chou's pseudo amino acid composition J. Theory Biol. 257 2009 17 26
15. S.R. Gunn Support vector machines for classification and regression Faculty of Engineering, Science and Mathematics, School of Electronics and Computer Science, University of Southampton 1998
16. N.A. Hamilton, R.S. Pantelic, K. Hanson, R.D. Teasdale Fast automated cell phenotype image classification BMC Bioinform 8 2007 110
17. M. Han, B. Liu Ensemble of extreme learning machine for remote sensing image classification Neurocomput. 149 2015 65 70
18. M. Hayat, A. Khan MemHyb: Predicting membrane protein types by hybridizing SAAC and PSSM J. Theoret. Biol. 292 2012 93 102
19. M. Hayat, A. Khan Predicting membrane protein types by fusing composite protein sequence features into pseudo amino acid composition J. Theory Biol. 271 2011 10 17
20. M. Hayat, A. Khan, M. Yeasin Prediction of membrane proteins using split amino acid and ensemble classification Amino Acids 42 2012 2447 2460
21. Q. He, C. Wu Separating theorem of samples in Banach space for support vector machine learning Int. J. Mach. Learn. Cybernet. 2 2011 49 54
22. J. Hu, J. Qi, Y. Peng, Q. Ren Predicting electrical evoked potential in optic nerve visual prostheses by using support vector regression and case-based prediction Inf. Sci. 290 2015 7 21
23. B. Julesz A theory of preattentive texture discrimination based on first-order statistics of textons Biol. Cybernet. 41 1981 131 138
24. R. Kumar, S. Jain, B. Kumari, M. Kumar Protein sub-nuclear localization prediction using SVM and Pfam domain information PLoS One 9 2014
25. S. Li, J.T. Kwok, H. Zhu, Y. Wang Texture classification using the support vector machines Pattern Recognit. 36 2003 2883 2893
26. C.-C. Lin, Y.-S. Tsai, Y.-S. Lin, T.-Y. Chiu, C.-C. Hsiung, M.-I. Lee, J.C. Simpson, C.-N. Hsu Boosting multiclass learning with repeating codes and weak detectors for protein subcellular localization Bioinform. 23 2007 3374 3381
27. Y.-S. Lin, C.-C. Lin, Y.-S. Tsai, T.-C. Ku, Y.-H. Huang, C.-N. Hsu A spectral graph theoretic approach to quantification and calibration of collective morphological differences in cell images Bioinformatics 26 2010 i29 i37
28. S. Maldonado, J. López Imbalanced data classification using second-order cone programming support vector machines Pattern Recognit. 47 2014 2070 2079
29. S. Mei Multi-kernel transfer learning based on Chou's PseAAC formulation for protein submitochondria localization J. Theory Biol. 293 2012 121 130
30. H. Mohabatkar, M.M. Beigi, A. Esmaeili Prediction of GABA(A) receptor proteins using the concept of Chou's pseudo-amino acid composition and support vector machine J. Theory Biol. 281 2011 18 23
31. L. Nanni, A. Lumini, Y.-S. Lin, C.-N. Hsu, C.-C. Lin Fusion of systems for automated cell phenotype image Classification Expert Syst. Appl. 37 2010 1556 1562
32. L. Nanni, S. Brahnam, A. Lumini Novel features for automated cell phenotype image classification Adv. Comput. Biol. 680 2010 207 213
33. L. Nanni, A. Lumini A reliable method for cell phenotype image classification Artif. Intell. Med. 43 2008 87 97
34. L. Nanni, S. Brahnam, A. Lumini Selecting the best performing rotation invariant patterns in local binary/ternary patterns Proceedings of the International Conference on Image Processing, Computer Vision, & Pattern Recognition (IPCV'10) Las Vegas, Nevada, USA 2010 369 375
35. L. Nanni, S. Brahnam, A. Lumini A simple method for improving local binary patterns by considering non-uniform patterns Pattern Recognit. 45 2012 3844 3852
36. M. Pulido, P. Melin, O. Castillo Particle swarm optimization of ensemble neural networks with fuzzy aggregation for time series prediction of the Mexican Stock Exchange Inf. Sci. 280 2014 188 204
37. Z.u. Rehman, M.T. Mirza, A. Khan, H. Xhaard Chapter four - Predicting G-protein-coupled receptors families using different physiochemical properties and pseudo amino acid composition Methods Enzymol. 522 2013 61 79
38. Z.u. Rehman, A. Khan G-protein coupled receptor prediction using pseudo-amino-acid composition and multiscale energy representation of different physiochemical properties Anal. Biochem. 412 2011 173 182
39. H. Sahbi Kernel PCA for similarity invariant shape recognition Neurocomput. 70 2007 3034 3045
40. L. Shamir Assessing the efficacy of low-level image content descriptors for computer-based fluorescence microscopy image analysis J. Microsc. 243 2011 284 292
41. J.-Y. Shi, S.-W. Zhang, Q. Pan, Y.-M. Cheng, J. Xie Prediction of protein subcellular localization by support vector machines using multi-scale energy and pseudo amino acid composition Amino Acids 33 2007 69 74
42. M. Tahir, A. Khan, A. Majid Protein subcellular localization of fuorescence imagery using spatial and transform domain features Bioinform. 28 2012 91 97
43. M. Tahir, A. Khan, A. Majid, A. Lumini Subcellular localization using fluorescence imagery: Utilizing ensemble classification with diverse feature extraction strategies and data balancing Appl. Soft Comput. 13 2013 4231 4243
44. V.N. Vapnik An overview of statistical learning theory IEEE Trans. Neural Netw. 10 1999 988 999
45. V.N. Vapnik, V. Vapnik Statistical Learning Theory 1998 Wiley New York
46. G. Wang, J. Sun, J. Ma, K. Xu, J. Gu Sentiment classification: The contribution of ensemble learning Decis. Support Syst. 57 2014 77 93
47. Z. Wang, Q. Zou, Y. Jiang, Y. Ju, X. Zeng Review of protein subcellular localization prediction Curr. Bioinform. 9 2014 331 342
48. B. Zhang, Y. Zhang, W. Lu, G. Han Phenotype recognition by curvelet transform and random subspace ensemble J. Appl. Math. Bioinform. 1 2011 79 103
49. S.-W. Zhang, Y.-L. Zhang, H.-F. Yang, C.-H. Zhao, Q. Pan Using the concept of Chou's pseudo amino acid composition to predict protein subcellular localization: an approach by incorporating evolutionary information and von Neumann entropies Amino Acids 34 2008 565 572
50. S. Zheng A fast algorithm for training support vector regression via smoothed primal function minimization Int. J. Mach. Learn. Cybernet. 6 2015 155 166
51. Y.-C. Zuo, Y. Peng, L. Liu, W. Chen, L. Yang, G.-L. Fan Predicting peroxidase subcellular location by hybridizing different descriptors of Chou' pseudo amino acid patterns Anal. Biochem. 458 2014 14 19