Title:Prediction of LncRNA-protein Interactions Using Auto-Encoder,
SE-ResNet Models and Transfer Learning
Volume: 13
Issue: 2
Author(s): Jiang Huiwen and Song Kai*
Affiliation:
- School of Mathematics and Statistics, Qingdao University, Qingdao, Shandong, China
Keywords:
LncRNA-protein interactions, auto-encoder, SE-ResNet module, sequence feature, secondary structure characteristics, transfer learning, feature extraction, feature selection.
Abstract:
Background: Long non-coding RNA (lncRNA) plays a crucial role in various biological
processes, and mutations or imbalances of lncRNAs can lead to several diseases, including
cancer, Prader-Willi syndrome, autism, Alzheimer's disease, cartilage-hair hypoplasia, and hearing
loss. Understanding lncRNA-protein interactions (LPIs) is vital for elucidating basic cellular
processes, human diseases, viral replication, transcription, and plant pathogen resistance. Despite
the development of several LPI calculation methods, predicting LPI remains challenging, with
the selection of variables and deep learning structure being the focus of LPI research.
Methods: We propose a deep learning framework called AR-LPI, which extracts sequence and
secondary structure features of proteins and lncRNAs. The framework utilizes an auto-encoder
for feature extraction and employs SE-ResNet for prediction. Additionally, we apply transfer
learning to the deep neural network SE-ResNet for predicting small-sample datasets.
Results: Through comprehensive experimental comparison, we demonstrate that the AR-LPI architecture
performs better in LPI prediction. Specifically, the accuracy of AR-LPI increases by
2.86% to 94.52%, while the F-value of AR-LPI increases by 2.71% to 94.73%.
Conclusion: Our experimental results show that the overall performance of AR-LPI is better than
that of other LPI prediction tools.
