Table 2 Summary of different features of kinases used in our study
From: Predicting a small molecule-kinase interaction map: A machine learning approach
Short-hand | Full Name | # Features | Feature Type |
|---|---|---|---|
STTK | Serine/Threonine, Tyrosine Kinases | 1 | nominal |
Summary Partitioning into Serine, Threonine and Tyrosine kinases | |||
PC | Phylogenetic Clustering | 2 | nominal |
Summary Partitioning into kinase groups and kinase families | |||
PRO | PROSITE patterns | 12 | numeric |
Summary Find PROSITE patterns in the kinases | |||
Apri | Apriori patterns | 14 | numeric |
Summary Find frequently occurring amino acid sequence patterns | |||
glAli | global alignment scores | 113 | numeric |
Summary Calculate global alignment scores for all pairs of kinases | |||
locAli | local alignment scores | 113 | numeric |
Summary Calculate local alignment scores for all pairs of kinases | |||
PSF | Position Specific Features | 98 | nominal |
Summary Use amino acids at the active center directly as features | |||
abPSF | abstract Postition Specific Features | 98 | nominal |
Summary Use amino acid classes at the active center directly as features | |||