Improving protein-ATP binding residues prediction by boosting SVMs with random under-sampling.
Dong-Jun YuJun HuZhenmin TangHong-Bin ShenJian YangJing-Yu YangPublished in: Neurocomputing (2013)
Keyphrases
- contact map
- protein structure
- contact maps
- amino acids
- protein tertiary structure
- protein sequences
- protein structure prediction
- protein chains
- experimentally determined
- feature selection
- support vector
- protein function
- amino acid sequences
- subcellular localization
- multiple sequence alignments
- physicochemical properties
- protein structure alignment
- protein secondary structure
- protein folding
- tertiary structure
- early stopping
- drug design
- mhc class ii
- soft margin
- molecular biology
- neural networks and support vector machines
- feature ranking
- cost sensitive
- predicting protein
- multi class
- machine learning
- amino acid composition
- kernel function
- boosting algorithms
- secondary structure
- automated theorem proving
- support vector machine svm
- generalization ability
- prediction accuracy
- solvent accessibility
- support vector machine
- ensemble learning
- molecular dynamics simulations
- knn
- multiple sequence alignment
- decision trees
- multi class classification
- base classifiers
- svm classifier
- theorem prover
- protein secondary structure prediction