Research Article

Prediction of LncRNA-protein Interactions Using Auto-Encoder, SE-ResNet Models and Transfer Learning

Author(s): Jiang Huiwen and Song Kai*

Volume 13, Issue 2, 2024

Published on: 08 April, 2024

Page: [155 - 165] Pages: 11

DOI: 10.2174/0122115366288068240322064431

Price: $65

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.

Keywords: LncRNA-protein interactions, auto-encoder, SE-ResNet module, sequence feature, secondary structure characteristics.


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