EDLMFC: an ensemble deep learning framework with multi-scale features combination for ncRNA–protein interaction prediction
Problem
Predicting interactions between non-coding RNAs and proteins computationally, since experimental methods are time-consuming and labor-intensive.
Why it matters
ncRNA-protein interactions underpin many physiological and pathological processes, and experimental detection is slow and costly.
What the AI did
EDLMFC, an ensemble deep learning model combining CNN and BLSTM, predicted interactions from multi-scale sequence, secondary structure, and tertiary structure features.
Results
not stated in source
Impact
A computational predictor was built and presented, but the abstract gives no performance figures.
Record
| Venue | BMC Bioinformatics |
|---|---|
| Published | 2021 |
| Work type | article |
| Authors | Jingjing Wang, Yanpeng Zhao, Weikang Gong, Yang Liu, Mei Wang, Xiaoqian Huang, Jianjun Tan |
| Citations | 42 (OpenAlex, at import time) |
| References | 81 |
| Model or method | EDLMFC (CNN + bi-directional LSTM ensemble) |
| Organization | not stated in source |
| Primary source | https://doi.org/10.1186/s12859-021-04069-9 |
| Open access | gold |
| Keywords | ncRNA, protein interaction, ensemble deep learning, CNN, BLSTM, multi-scale features |
Importance rating
significant — thousands to millions affected
ncRNA-protein interactions play essential roles in physiological and pathological processes; experimental detection is time-consuming and labor-intensive.