A statistical approach for 5′ splice site prediction using short sequence motifs and without encoding sequence data

Lignende værker

• Prediction of donor splice sites using random forest with a new sequence encoding approach
  af: Meher, Prabina Kumar, et al.
  Udgivet: (2016)
• Identification of donor splice sites using support vector machine: a computational approach based on positional, compositional and dependency features
  af: Meher, Prabina Kumar, et al.
  Udgivet: (2016)
• Predicting antimicrobial peptides with improved accuracy by incorporating the compositional, physico-chemical and structural features into Chou’s general PseAAC
  af: Meher, Prabina Kumar, et al.
  Udgivet: (2017)
• DIRProt: a computational approach for discriminating insecticide resistant proteins from non-resistant proteins
  af: Meher, Prabina Kumar, et al.
  Udgivet: (2017)
• funbarRF: DNA barcode-based fungal species prediction using multiclass Random Forest supervised learning model
  af: Prabina Kumar Meher, et al.
  Udgivet: (2019-01-01)