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RAM-PGK: Prediction of Lysine Phosphoglycerylation Based on Residue Adjacency Matrix.

Abel Avitesh ChandraAlok SharmaAbdollah DehzangiTatushiko Tsunoda
Published in: Genes (2020)
RAM-PGK, which is based on sequential features and support vector machine classifiers, has shown a noteworthy improvement in terms of performance in comparison to some of the recent prediction methods. The performance metrics of the RAM-PGK predictor are: 0.5741 sensitivity, 0.6436 specificity, 0.0531 precision, 0.6414 accuracy, and 0.0824 Mathews correlation coefficient.
Keyphrases
  • deep learning
  • magnetic resonance imaging
  • magnetic resonance
  • clinical evaluation
  • contrast enhanced