rpiCOO

rpiCOO predicts RNA–protein interactions (RPIs) from sequence data using a random forest classifier trained on motif and nucleotide composition features.

Key Features:

• Random Forest–Based RPI Prediction: Utilizes motif information, repetitive sequence patterns from experimentally validated RNA–protein interactions, and nucleotide composition descriptors to classify interacting pairs.
• Feature Selection and Cross-Validation: Selects approximately 20% of informative sequence motif and composition features and evaluates performance using 10-fold cross-validation on three non-redundant benchmark datasets.

Scientific Applications:

• Post-Transcriptional Regulation Analysis: Supports identification of RNA–protein interactions involved in gene regulation and disease-related molecular mechanisms.

Methodology:

rpiCOO extracts sequence motifs, repetitive patterns, and nucleotide composition features from validated RPI datasets, applies feature selection to retain informative descriptors, and trains a random forest classifier evaluated by 10-fold cross-validation across benchmark datasets.