Sequence-Based Prediction of microRNA-Binding Residues in Proteins Using Cost-Sensitive Laplacian Support Vector Machines.
Jian-Sheng WuZhi-Hua ZhouPublished in: IEEE ACM Trans. Comput. Biol. Bioinform. (2013)
Keyphrases
- cost sensitive
- protein tertiary structure
- binary classification
- multiple sequence alignments
- protein structure
- secondary structure
- protein sequences
- support vector
- amino acid sequences
- amino acids
- contact map
- disordered regions
- protein chains
- protein structure prediction
- multi class
- protein data bank
- protein function
- sequence analysis
- support vector machine
- misclassification costs
- protein structural
- hiv protease
- binding sites
- protein families
- cost sensitive classification
- cost sensitive learning
- naive bayes
- class distribution
- protein interaction
- experimentally determined
- computational methods
- classification accuracy
- prediction accuracy
- sequence data
- multi class classification
- feature selection
- active learning
- pairwise
- protein folding
- boosting algorithms
- physicochemical properties
- transcription factor binding sites
- protein protein interactions
- class imbalance
- semi supervised
- loss function
- mhc class ii