Statler College of Engineering and Mining Resources
Lane Department of Computer Science and Electrical Engineering
In this work, we study two approaches for the problem of RNA-Protein Interaction (RPI). In the first approach, we use a feature-based technique by combining extracted features from both sequences and secondary structures. The feature-based approach enhanced the prediction accuracy as it included much more available information about the RNA-protein pairs. In the second approach, we apply search algorithms and data structures to extract effective string patterns for prediction of RPI, using both sequence information (protein and RNA sequences), and structure information (protein and RNA secondary structures). This led to different string-based models for predicting interacting RNA-protein pairs. We show results that demonstrate the effectiveness of the proposed approaches, including comparative results against leading state-of-the-art methods.
Digital Commons Citation
Adjeroh, Donald; Allaga, Maen; Tan, Jun; Lin, Jie; Jiang, Yue; Abbasi, Ahmed; and Zhou, Xiaobo, "Feature-Based and String-Based Models for Predicting RNA-Protein Interaction" (2018). Faculty & Staff Scholarship. 1778.
Adjeroh, D., Allaga, M., Tan, J., Lin, J., Jiang, Y., Abbasi, A., & Zhou, X. (2018). Feature-Based and String-Based Models for Predicting RNA-Protein Interaction. Molecules, 23(3), 697. https://doi.org/10.3390/molecules23030697