Predicting structural class for protein sequences of 40% identity based on features of primary and secondary structure using Random Forest algorithm.
Apurva MehtaHimanshu S. MazumdarPublished in: Comput. Biol. Chem. (2020)
Keyphrases
- protein sequences
- secondary structure
- random forest
- learning algorithm
- structural features
- feature set
- amino acids
- feature extraction
- multiple sequence alignment
- protein secondary structure
- predicting protein
- sequence alignment
- computational biology
- protein structure
- molecular biology
- protein folding
- protein structure prediction
- rna sequences
- similarity measure
- protein secondary structure prediction
- neural network