RPITER: A Hierarchical Deep Learning Framework for ncRNA–Protein Interaction Prediction
importance 4/10
ai assisted
Machine Learning in Bioinformatics RNA bioinformatics stage: research
Problem
Identifying interactions between non-coding RNAs and RNA-binding proteins experimentally is expensive and time-consuming, and computational prediction remains challenging.
Why it matters
ncRNA-protein interactions are key to understanding ncRNA function, which is implicated in many complex human diseases.
What the AI did
Proposed RPITER, a hierarchical deep learning framework that learns sequence features to predict RNA-protein interactions computationally.
Results
not stated in source
Impact
Offers a computational route to predict RNA-protein interactions without costly experiments.
Record
| Venue | International Journal of Molecular Sciences |
|---|---|
| Published | 2019 |
| Work type | article |
| Authors | Cheng Peng, Siyu Han, Hui Zhang, Ying Li |
| Citations | 96 (OpenAlex, at import time) |
| References | 67 |
| Model or method | RPITER (hierarchical deep learning framework) |
| Organization | not stated in source |
| Primary source | https://doi.org/10.3390/ijms20051070 |
| Open access | gold |
| Keywords | ncRNA, protein interaction, deep learning, prediction, lncRNA |
Importance rating
4
real but narrower advance
ncRNAs implicated in many complex human diseases per abstract, but no scale stated; a computational alternative to costly experiments, narrower advance.