A novel feature extraction method with feature selection to identify golgi-resident protein types from imbalanced data

International Journal of Molecular Sciences

Volumn 17, Issue 2, 2016, Pages

  Yang, Runtao ^a   Zhang, Chengjin ^a,b   Gao, Rui ^a   Zhang, Lina ^a  

^a SHANDONG UNIVERSITY   (China)

^b SHANDONG UNIVERSITY   (China)

Author keywords

Common spatial patterns; Golgi apparatus proteins; Random forest; Recursive feature elimination; Synthetic minority over sampling technique

Indexed keywords

CELL PROTEIN; GOLGI RESIDENT PROTEIN; UNCLASSIFIED DRUG; AMINO ACID; PEPTIDE; PROTEIN;

AMINO ACID COMPOSITION; AREA UNDER THE CURVE; ARTICLE; COMMON SPATIAL PATTERN; CORRELATION COEFFICIENT; EXTRACTION; INFORMATION PROCESSING; MATHEMATICAL MODEL; MEASUREMENT ACCURACY; POSITION WEIGHT MATRIX; PROTEIN ANALYSIS; RANDOM FOREST; RECEIVER OPERATING CHARACTERISTIC; RECURSIVE FEATURE ELIMINATION; SENSITIVITY AND SPECIFICITY; STATISTICAL ANALYSIS; SYNTHETIC MINORITY OVER SAMPLING TECHNIQUE; ALGORITHM; ANIMAL; BIOLOGY; CHEMISTRY; DISEASE PREDISPOSITION; EVOLUTION; GOLGI COMPLEX; HUMAN; METABOLISM; PROCEDURES; PROTEIN DATABASE; REPRODUCIBILITY; SYSTEMS BIOLOGY;

ALGORITHMS; AMINO ACIDS; ANIMALS; BIOLOGICAL EVOLUTION; COMPUTATIONAL BIOLOGY; DATABASES, PROTEIN; DISEASE SUSCEPTIBILITY; GOLGI APPARATUS; HUMANS; PEPTIDES; PROTEINS; REPRODUCIBILITY OF RESULTS; ROC CURVE; SYSTEMS BIOLOGY;

EID: 84957575309     PISSN: 16616596     EISSN: 14220067     Source Type: Journal    
DOI: 10.3390/ijms17020218     Document Type: Article

References (62)

1. Hu, Z.; Zeng, L.; Xie, L.; Lu, W.; Zhang, J.; Li, T.; Wang, X. Morphological alteration of golgi apparatus and subcellular compartmentalization of TGF-b 1 in Golgi apparatus in gerbils following transient forebrain ischemia. Neurochem. Res. 2007, 32, 1927-1931.
2. Fujita, Y.; Ohama, E.; Takatama, M.; Al-Sarraj, S.; Okamoto, K. Fragmentation of Golgi apparatus of nigral neurons with a-synuclein-positive inclusions in patients with Parkinson’s disease. Acta Neuropathol. 2006, 112, 261-265.
3. Jiao, Y.S.; Du, P.F. Predicting Golgi-resident protein types using pseudo amino acid compositions: Approaches with positional specific physicochemical properties. J. Theor. Biol. 2015, 391, 35-42.
4. Ding, H.; Guo, S.H.; Deng, E.Z.; Yuan, L.F.; Guo, F.B.; Huang, J.; Rao, N.; Chen, W.; Lin, H. Prediction of Golgi-resident protein types by using feature selection technique. Chemom. Intell. Lab. Syst. 2013, 124, 9-13.
5. Cooper, G.M.; Hausman, R.E. The Cell: A Molecular Approach, 4th ed.; Sinauer Associates, Inc.: Sunderland, MA, USA, 2006.
6. Ding, H.; Liu, L.; Guo, F.B.; Huang, J.; Lin, H. Identify Golgi protein types with modified mahalanobis discriminant algorithm and pseudo amino acid composition. Protein Pept. Lett. 2011, 18, 58-63.
7. Pfeffer, S.R. Constructing a Golgi complex. J. Cell Biol. 2001, 155, 873-875.
8. Pavelk, M.; Mironov, A.A. The Golgi Apparatus: State of the Art 110 yEars after Camillo Golgi’s Discovery; Springer: Berlin, Germany: 2008.
9. Day, K.J.; Staehelin, L.A.; Glick, B.S. A three-stage model of Golgi structure and function. Histochem. Cell Biol. 2013, 140, 239-249.
10. Fujita, Y.; Okamoto, K. Golgi apparatus of the motor neurons in patients with amyotrophic lateral sclerosis and in mice models of amyotrophic lateral sclerosis. Neuropathology 2005, 25, 388-394.
11. Gonatas, N.K.; Gonatas, J.O.; Stieber, A. The involvement of the Golgi apparatus in the pathogenesis of amyotrophic lateral sclerosis, Alzheimer’s disease, and ricin intoxication. Histochem. Cell Biol. 1998, 109, 591-600.
12. Leung, C.H.; Zhong, H.J.; Chan, D.S.H.; Ma, D.L. Bioactive iridium and rhodium complexes as therapeutic agents. Coord. Chem. Rev. 2013, 257, 1764-1776.
13. Ma, D.L.; He, H.Z.; Leung, K.H.; Chan, D.S.; Leung, C.H. Bioactive luminescent transition-metal complexes for biomedical applications. Angew. Chem. Int. Ed. Engl. 2013, 52, 7666-7682.
14. Man, B.Y.W.; Chan, H.M.; Leung, C.H.; Chan, D.S.H.; Bai, L.P.; Jiang, Z.H.; Li, H.W.; Ma, D.L. Group 9 metal-based inhibitors of b-amyloid (1-C 40) fibrillation as potential therapeutic agents for Alzheimer’sdisease. R. Soc. Chem. 2011, 2, 917-921.
15. Nakamura, T.; Lipton, S.A. Protein S-nitrosylation as a therapeutic target for neurodegenerative diseases. Trends Pharmacol. Sci. 2016, 37, 73-84.
16. Brettschneider, J.; Del Tredici, K.; Lee, V.M.; Trojanowski, J.Q. Spreading of pathology in neurodegenerative diseases: A focus on human studies. Nat. Rev. Neurosci. 2015, 16, 109-120.
17. Ungar, D. Golgi linked protein glycosylation and associated diseases. Semin. Cell Dev. Biol. 2009, 20, 762-769.
18. Fujita, Y.; Okamoto, K. Golgi apparatus of the motor neurons in patients with amyotrophic lateral sclerosis and in mice models of amyotrophic lateral sclerosis. Neuropathology 2005, 255, 388-394.
19. Nakano, A.; Luini, A. Passage through the Golgi. Curr. Opin. Cell Biol. 2010, 22, 471-478.
20. Yu, D.; Wu, X.; Shen, H.; Yang, J.; Tang, Z.; Qi, Y.; Yang, J. Enhancing membrane protein subcellular localization prediction by parallel fusion of multi-view features. IEEE Trans. Nanobiosci. 2012, 4, 375-385.
21. Fan, G.L.; Li, Q.Z. Predicting protein submitochondria locations by combining different descriptors into the general form of Chou’s pseudo amino acid composition. Amino Acids 2012, 43, 545-555.
22. Huang, C.; Yuan, J.Q. Predicting protein subchloroplast locations with both single and multiple sites via three different modes of Chou’s pseudo amino acid compositions. J. Theor. Biol. 2013, 335, 205-212.
23. Asadabadi, E.B.; Abdolmaleki, P. Predictions of protein-protein interfaces within membrane protein complexes. Avicenna J. Med. Biotechnol. 2013, 5, 148-157.
24. Zhang, S.L.; Ye, F.; Yuan, X.G. Using principal component analysis and support vector machine to predict protein structural class for lowsimilarity sequences via PSSM. J. Biomol. Struct. Dyn. 2012, 29, 1138-1146.
25. Zou, L.Y.; Nan, C.H.; Hu, F.Q. Accurate prediction of bacterial type IV secreted effectors using amino acid composition and PSSM profiles. Bioinformatics 2013, 29, 3135-3142.
26. Paliwal, K.K.; Sharma, A.; Lyons, J.; Dehzangi, A. A tri-gram based feature extraction technique using linear probabilities of position specific scoring matrix for protein fold recognition. IEEE Trans. Nanobiosci. 2014, 8, 44-50.
27. Bernardes, J.S.; Pedreira, C.E. A review of protein function prediction under machine learning perspective. Recent. Pat. Biotechnol. 2013, 7, 122-141.
28. Altschul, S.F.; Madden, T.L.; Schaffer, A.A.; Zhang, J.; Zhang, Z.; Miller, W.; Lipman, D.J. Gapped BLAST and PSI-BLAST: A new generation of protein database search programs. Nucleic Acids Res. 1997, 25, 3389-3402.
29. Magrane, M.; Consortium, U. UniProt knowledgebase: A hub of integrated protein data. Database 2011, doi:10.1093/database/bar009.
30. Huang, Y.; Niu, B.; Gao, Y.; Fu, L.; Li, W. CD-HIT Suite: A web server for clustering and comparing biological sequences. Bioinformatics 2010, 26, 680-682.
31. Chou, K.C. Some remarks on protein attribute prediction and pseudo amino acid composition. J. Theor. Biol. 2011, 273, 236-247.
32. Han, G.S.; Yu, Z.G.; Anh, V.; Krishnajith, A.P.; Tian, Y.C. An ensemble method for predicting subnuclear localizations from primary protein structures. PLoS ONE 2013, 8, e57225.
33. Shi, S.P.; Qiu, J.D.; Sun, X.Y.; Suo, S.B.; Huang, S.Y.; Liang, R.P. A method to distinguish between lysine acetylation and lysine methylation from protein sequences. J. Theor. Biol. 2012, 310, 223-230.
34. Zhang, C.T.; Chou, K.C. An optimization approach to predicting protein structural class from amino acid composition. Protein Sci. 1992, 1, 401-408.
35. Kaundal, R.; Saini, R.; Zhao, P.X. Combining Machine Learning and Homology-Based Approaches to Accurately Predict Subcellular Localization in arabidopsis. Plant Physiol. 2010, 154, 36-54.
36. Lin, H.; Ding, H. Predicting ion channels and their types by the dipeptide mode of pseudo amino acid composition. J. Theor. Biol. 2011, 269, 64-69.
37. Chou, K.C. Prediction of protein cellular attributes using pseudo-amino acid composition. Proteins 2001, 43, 246-255.
38. Nieto, J.J.; Torres, A.; Georgiou, D.N.; Karakasidis, T.E. Fuzzy polynucleotide spaces and metrics. Bull. Math. Biol. 2006, 68, 703-725.
39. Georgiou, D.N.; Karakasidis, T.E.; Nieto, J.J.; Torres, A. A study of entropy/clarity of genetic sequences using metric spaces and fuzzy sets. J. Theor. Biol. 2010, 267, 95-105.
40. Zhao, X.; Li, X.; Ma, Z.; Yin, M. Prediction of lysine ubiquitylation with ensemble classifier and feature selection. Int. J. Mol. Sci. 2011, 12, 8347-8361.
41. Liu, B.; Xu, J.; Zou, Q.; Xu, R.; Wang, X.; Chen, Q. Using distances between Top-n-gram and residue pairs for protein remote homology detection. BMC Bioinform. 2014, 15, 1-10.
42. Magnan, C.N.; Randall, A.; Baldi, P. SOLpro: Accurate sequence-based prediction of protein solubility. Bioinformatics 2009, 25, 2200-2207.
43. John, A.C.; Mona, S. Predicting functionally important residues from sequence conservation. Bioinformatics 2007, 23, 1875-1882.
44. Schaffer, A.A.; Aravind, L.; Madden, T.L.; Shavirin, S.; Spouge, J.L.; Wolf, Y.I.; Koonin, E.V.; Altschul, S.F. Improving the accuracy of PSI-BLAST protein database searches with composition-based statistics and other refinements. Nucleic Acids Res. 2001, 29, 2994-3005.
45. Tao, P.; Liu, T.; Li, X.; Chen, L. Prediction of protein structural class using tri-gram probabilities of position-specific scoring matrix and recursive feature elimination. Amino Acids 2015, 47, 461-468.
46. Zhang, L.; Zhang, C.; Gao, R.; Yang, R. An ensemble method to distinguish bacteriophage virion from non-virion proteins based on protein sequence characteristics. Int. J. Mol. Sci. 2015, 16, 21734-21758.
47. Zuo, Y.C.; Peng, Y.; Liu, L.; Chen, W.; Yang, L.; Fan, G.L. Predicting peroxidase subcellular location by hybridizing different 4 descriptors of Chou’s pseudo amino acid patterns. Anal. Biochem. 2014, 458, 14-19.
48. Eichner, J.; Topf, F.; Drager, A.; Wrzodek, C.; Wanke, D.; Zell, A. TFpredict and SABINE: Sequence-based prediction of structural and functional characteristics of transcription factors. PLoS ONE 2013, 8, e82238.
49. Zhang, J.; Zhao, X.; Sun, P.; Ma, Z. PSNO: Predicting cysteine s-nitrosylation sites by incorporating various sequence-derived features into the general form of Chou’s PseAAC. Int. J. Mol. Sci. 2014, 15, 11204-11219.
50. Hayat, M.; Tahir, M.; Khan, S. Prediction of protein structure classes using hybrid space of multi-profile Bayes and bi-gram probability feature spaces. J. Theor. Biol. 2014, 346, 8-15.
51. Sharma, A.; Lyonsm, J.; Dehzangi, A.; Paliwal, K.K. A feature extraction technique using bi-gram probabilities of position specific scoring matrix for protein fold recognition. J. Biomol. Struct. Dyn. 2013, 320, 41-46.
52. Ramoser, H.; Muller-Gerking, J.; Pfurtscheller, G. Optimal spatial filtering of single trial EEG during imagined hand movement. IEEE Trans. Rehabil. Eng. 2000, 8, 441-446.
53. Chawla, N.V.; Bowyer, K.W.; Hall, L.O.; Kegelmeyer, W.P. SMOTE: Synthetic minority over-sampling technique. J. Artif. Intell. Res. 2002, 16, 321-357.
54. Ding, C.; Yuan, L.F.; Guo, S.H.; Lin, H.; Chen, W. Identification of mycobacterial membrane proteins and their types using over-represented tripeptide compositions. J. Proteom. 2012, 77, 321-328.
55. Ebina, T.; Suzuki, R.; Tsuji, R.; Kuroda, Y. H-DROP: An SVM based helical domain linker predictor trained with features optimized by combining random forest and stepwise selection. J. Comput. Aided Mol. Des. 2014, 28, 831-839.
56. Ebina, T.; Toh, H.; Kuroda, Y. DROP: An SVM domain linker predictor trained with optimal features selected by random forest. Bioinformatics 2011, 27, 487-494.
57. Breiman, L. Random forests. Mach. Learn. 2001, 45, 5-32.
58. Kandaswamy, K.K.; Pugalenthi, G.; Hartmann, E.; Kalies, K.U.; Moller, S.; Suganthan, P.N.; Martinetz, T. SPRED: A machine learning approach for the identification of classical and non-classical secretory proteins in mammalian genomes. Biochem. Biophys. Res. Commun. 2010, 391, 1306-1311.
59. Mohamed, T.P.; Carbonell, J.G.; Ganapathiraju, M.K. Active learning for human protein-protein interaction prediction. BMC Bioinform. 2010, 11, 1-9.
60. Witten, I.H.; Frank, E. Data Mining: Practical Machine Learning Tools and Techniques; Morgan Kaufmann: San Francisco, CA, USA 2005.
61. Chou, K.C.; Zhang, C.T. Prediction of protein structural classes. Crit. Rev. Biochem. Mol. Biol. 1995, 30, 275-349.
62. Fawcett, T. An introduction to ROC analysis. Pattern Recognit. Lett. 2006, 27, 861-874.