Prediction of protein solvent accessibility using Support Vector Machines

Proteins: Structure, Function and Genetics

Volumn 48, Issue 3, 2002, Pages 566-570

  Yuan, Zheng ^a   Burrage, Kevin ^a   Mattick, John S ^a  

^a UNIVERSITY OF QUEENSLAND   (Australia)

Author keywords

Computer simulation; Machine learning; Protein structure prediction; Solvent accessibility

Indexed keywords

SOLVENT;

ACCURACY; AMINO ACID SEQUENCE; ARTICLE; COMPUTER SIMULATION; INFORMATION; INTERMETHOD COMPARISON; PRIORITY JOURNAL; PROTEIN ANALYSIS; PROTEIN FUNCTION; PROTEIN STRUCTURE; RELIABILITY; SEQUENCE ANALYSIS;

BAYES THEOREM; COMPUTATIONAL BIOLOGY; COMPUTER SIMULATION; INFORMATION THEORY; LINEAR MODELS; NEURAL NETWORKS (COMPUTER); PROTEINS; REPRODUCIBILITY OF RESULTS; SEQUENCE ALIGNMENT; SEQUENCE ANALYSIS, PROTEIN; SOLVENTS;

EID: 0037103004     PISSN: 08873585     EISSN: None     Source Type: Journal    
DOI: 10.1002/prot.10176     Document Type: Article

References (32)

1. Adaptation of protein surfaces to subcellular location
2. Predicting surface exposure of amino acids from protein sequence
3. Conservation and prediction of solvent accessibility in protein families
4. Predicting solvent accessibility: Higher accuracy using Bayesian statistics and optimized residue substitution classes
5. Easy method to predict solvent accessibility from multiple protein sequence alignments
6. PredAcc: Prediction of solvent accessibility
7. Predicting residue solvent accessibility from protein sequence by considering the sequence environment
8. New methods for accurate prediction of solvent accessibility from protein sequence
9. Prediction of protein surface accessibility with information theory
10. Hybrid system for protein secondary structure prediction
11. Evaluation and improvement of multiple sequence methods for protein secondary structure prediction
12. Support vector networks
13. Support vector machine classification and validation of cancer tissue samples using microarray expression data
14. Identifying marker genes in transcription profiling data using a mixture of feature relevance experts
15. Multi-class protein fold recognition using support vector machines and neural networks
16. Predicting protein-protein interaction from primary structure
17. A novel method of protein secondary structure prediction with high segment overlap measure: Support vector machine approach
18. Multiclass cancer diagnosis using tumor gene expression signatures
19. Dictionary of protein secondary structure: Pattern recognition of hydrogen bonded and geometrical features
20. Comparison of the predicted and observed secondary structure of T4 phage lysozyme
21. Making large-scale SVM learning practical
22. A comparison of machine learning methods for the diagnosis of pigmented skin lesions
23. Support vector machine approach for protein subcellular localization prediction
24. A 3D-1D substitution matrix for protein fold recognition that includes predicted secondary structure of the sequence
25. Seeking an ancient enzyme in Methanococcus jannaschii using ORF, a program based on predicted secondary structure comparisons
26. Successful recognition of protein folds using threading methods biased by sequence similarity and predicted secondary structure
27. Hidden Markov models that use predicted secondary structures for fold recognition
28. Enhanced genome annotation using structural profiles in the program 3D-PSSM
29. Identification of related proteins with weak sequence identity using secondary structure information
30. Environmental features are important in determining protein secondary structure
31. Identification of functional surfaces of zinc binding domains of intracellular receptors