Support vector machine approach for protein subcellular localization prediction

Bioinformatics

Volumn 17, Issue 8, 2001, Pages 721-728

  Hua, S ^a   Sun, Z ^a  

^a TSINGHUA UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

AMINO ACID;

ACCURACY; ALGORITHM; AMINO TERMINAL SEQUENCE; ANALYTIC METHOD; ANALYTICAL ERROR; ARTICLE; AUTOMATION; CELLULAR DISTRIBUTION; CONTROLLED STUDY; EUKARYOTE; GENE SEQUENCE; PREDICTION; PRIORITY JOURNAL; PROKARYOTE; PROTEIN LOCALIZATION; RELIABILITY; SUPPORT VECTOR MACHINE;

EUKARYOTA; PROKARYOTA;

EID: 0034843744     PISSN: 13674803     EISSN: 13674811     Source Type: Journal    
DOI: 10.1093/bioinformatics/17.8.721     Document Type: Article

References (33)

1. Adaption of protein surfaces to subcellular location
2. Knowledge-based analysis of microarray gene expression data by using support vector machines
3. Relation between amino acid composition and cellular location of proteins
4. Protein subcellular location prediction
5. Support vector networks
6. A Bayesian system integrating expression data with sequence patterns for localizing proteins: Comprehensive application to the yeast genome
7. Support vector machines for spam categorization
8. Wanted: Subcellular localization of proteins based on sequence
9. Predicting subcellular localization of proteins based on their N-terminal amino acid sequence
10. Starts of bacterial genes: Estimating the reliability of computer predictions
11. Identification of prokaryotic and eukaryotic signal peptides and prediction of their cleavage sites
12. SOSUI: Classification and secondary structure prediction system for membrane proteins
13. A novel method of protein secondary structure prediction with high segment overlap measure: Support vector machine approach
14. Making large-scale SVM learning practical
15. Wavelet change-point prediction of transmembrane proteins
16. Comparison of predicted and observed secondary structure of T4 phage lysozyme
17. Towards a systematics for protein subcellular location: Quantitative description of protein localization patterns and automated analysis of fluorescence microscope images
18. Protein sorting signals and prediction of subcellular localization
19. Better prediction of protein cellular localization sites with the k nearest neighbors classifier
20. Expert system for predicting protein localization sites in Gram-negative bacteria
21. A knowledge base for predicting protein localization sites in eukaryotic cells
22. Discrimination of intracellular and extracellular proteins using amino acid composition and residue-pair frequencies
23. A neural network method for identification of prokaryotic and eukaryotic signal perptides and prediction of their cleavage sites
24. Machine learning approaches for the prediction of signal peptides and other protein sorting signals
25. Using neural networks for prediction of the subcellular location of proteins
26. View-based 3D object recognition with support vector machines
27. Prediction of secondary structure at better than 70% accuracy
28. Topology prediction for helical transmembrane proteins at 86% accuracy
29. Extracting support data for a given task
30. Interior point methods: Algorithms and formulations
31. Prediction of protein subcellular locations using Markov chain models