| Method |
Dataset |
Features |
Description |
| Pancaldi and Bähler [15] |
5,166 mRNA-protein interacting pairs from immunopurification experiments |
Predicted protein secondary structure, localization, protein physical properties, gene physical properties, UTR properties, genetic interactions |
Protein and RNA sequences encoded using > 100 features are used to train SVM and RF classifiers |
| Bellucci et al. (catRAPID) [17] |
7,409 interacting pairs from 858 RNA-protein complexes from PDB |
Physicochemical properties including secondary structure propensities, hydrogen-bonding propensities, and van der Waals interaction propensities |
Propensities are calculated for each amino acid and ribonucleotide to generate an interaction profile
(http://service.tartaglialab.com/page/catrapid_group) |
| Muppirala et al. (RPISeq) [22] |
2,241 interacting pairs from 943 RNA-protein complexes from PRIDB (RPI2241) |
Sequence composition of proteins, represented as conjoint triads, and RNAs, represented as tetrads |
Protein and RNA sequences encoded sequence-composition-based features are used to train SVM and RF classifiers (http://pridb.gdcb.iastate.edu/RPISeq) |
| Wang et al. [26] |
RPI 2241 generated by Muppirala et al. & 367 interacting pairs from NPInter |
Sequence composition of protein and RNA |
Input to NB and ENB classifiers is a combination of protein triads and RNA triad features similar to those used in RPISeq |
