Predicting Golgi-resident proteins in plants by incorporating N-terminal transmembrane domain information in the general form of Chou's pseudoamino acid compositions

International Conference on Systems Biology, ISB

Volumn , Issue , 2014, Pages 226-229

  Jiao, Yasen ^a   Du, Pufeng ^a   Su, Xiaoquan ^b  

^a TIANJIN UNIVERSITY   (China)

^b QINGDAO INSTITUTE OF BIOENERGY AND BIOPROCESS TECHNOLOGY   (China)

Author keywords

Golgi apparatus; subcellular localization; SVM; transmembrane domain

Indexed keywords

ACTIVATION ANALYSIS; AMINO ACIDS; PHYSICOCHEMICAL PROPERTIES; PLANTS (BOTANY);

BIOLOGICAL FUNCTIONS; GOLGI APPARATUS; PHYSICO-CHEMICAL PROPERTIES OF AMINO ACIDS; PSEUDO AMINO ACID COMPOSITIONS; STATE-OF-THE-ART METHODS; SUBCELLULAR LOCALIZATIONS; SVM-BASED CLASSIFIERS; TRANS-MEMBRANE DOMAINS;

PROTEINS;

EID: 84920135741     PISSN: 23250704     EISSN: 23250712     Source Type: Journal    
DOI: 10.1109/ISB.2014.6990759     Document Type: Conference Paper

References (13)

1. Fabene PF, Bentivoglio M (1998) 1898-1998: Camillo Golgi and "the Golgi": one hundred years of terminological clones. Brain Res Bull 47: 195-198.
2. Prydz K, Dalen KT (2000) Synthesis and sorting of proteoglycans. J Cell Sci 113 Pt 2: 193-205.
3. Chou W-C, Yin Y, Xu Y (2010) GolgiP: prediction of Golgi-resident proteins in plants. Bioinformatics 26: 2464-2465. doi:10.1093/bioinformatics/btq446.
4. Du P, Xu C (2013) Predicting multisite protein subcellular locations: progress and challenges. Expert Rev Proteomics 10: 227-237. doi:10.1586/epr.13.16.
5. Chou K-C (2011) Some remarks on protein attribute prediction and pseudo amino acid composition. J Theor Biol 273: 236-247. doi:10.1016/j.jtbi.2010.12.024.
6. The UniProt Consortium (2012) Update on activities at the Universal Protein Resource (UniProt) in 2013. Nucleic Acids Research 41: D43- D47. doi:10.1093/nar/gks1068.
7. Fu L, Niu B, Zhu Z, Wu S, Li W (2012) CD-HIT: Accelerated for clustering the next-generation sequencing data. Bioinformatics 28: 3150-3152. doi:10.1093/bioinformatics/bts565.
8. Chou K-C (2013) Some remarks on predicting multi-label attributes in molecular biosystems. Mol Biosyst 9: 1092-1100. doi:10.1039/c3mb25555g.
9. Yuan Z, Teasdale RD (2002) Prediction of Golgi Type II membrane proteins based on their transmembrane domains. Bioinformatics 18: 1109-1115.
10. Krogh A, Larsson B, von Heijne G, Sonnhammer EL (2001) Predicting transmembrane protein topology with a hidden Markov model: Application to complete genomes. J Mol Biol 305: 567-580. doi:10.1006/jmbi.2000.4315.
11. Chang C-C, Lin C-J (2011) LIBSVM: A library for support vector machines. ACM Trans Intell Syst Technol 2: 27:1-27:27. doi:10.1145/1961189.1961199.
12. SUN Y, WONG AKC, KAMEL MS (2009) CLASSIFICATION OF IMBALANCED DATA: A REVIEW. Int J Patt Recogn Artif Intell 23: 687-719. doi:10.1142/S0218001409007326.
13. He J, Gu H, Liu W (2012) Imbalanced multi-modal multi-label learning for subcellular localization prediction of human proteins with both single and multiple sites. PLoS ONE 7: e37155. doi:10.1371/journal.pone.0037155.