SaPt-CNN-LSTM-AR-EA: a hybrid ensemble learning framework for time series-based multivariate DNA sequence prediction

Wu Yan; Li Tan; Li Meng-Shan; Sheng Sheng; Wang Jun; Wu Fu-An

doi:10.7717/peerj.16192

SaPt-CNN-LSTM-AR-EA: a hybrid ensemble learning framework for time series-based multivariate DNA sequence prediction

PeerJ. 2023 Oct 4:11:e16192. doi: 10.7717/peerj.16192. eCollection 2023.

Authors

Wu Yan^#^{1

2

3}, Li Tan⁴, Li Meng-Shan⁴, Sheng Sheng^{1

3}, Wang Jun^{1

3}, Wu Fu-An^#^{1

3}

Affiliations

¹ School of Biotechnology, Jiangsu University of Science & Technology, Zhenjiang, China.
² School of Mathematics and Computer Science, Gannan Normal University, Ganzhou, Jiangxi, China.
³ Sericultural Research Institute, Chinese Academy of Agricultural Sciences, Zhenjiang, Jiangsu, China.
⁴ College of Physics and Electronic Information, Gannan Normal University, Ganzhou, China.

^# Contributed equally.

Abstract

Biological sequence data mining is hot spot in bioinformatics. A biological sequence can be regarded as a set of characters. Time series is similar to biological sequences in terms of both representation and mechanism. Therefore, in the article, biological sequences are represented with time series to obtain biological time sequence (BTS). Hybrid ensemble learning framework (SaPt-CNN-LSTM-AR-EA) for BTS is proposed. Single-sequence and multi-sequence models are respectively constructed with self-adaption pre-training one-dimensional convolutional recurrent neural network and autoregressive fractional integrated moving average fused evolutionary algorithm. In DNA sequence experiments with six viruses, SaPt-CNN-LSTM-AR-EA realized the good overall prediction performance and the prediction accuracy and correlation respectively reached 1.7073 and 0.9186. SaPt-CNN-LSTM-AR-EA was compared with other five benchmark models so as to verify its effectiveness and stability. SaPt-CNN-LSTM-AR-EA increased the average accuracy by about 30%. The framework proposed in this article is significant in biology, biomedicine, and computer science, and can be widely applied in sequence splicing, computational biology, bioinformation, and other fields.

Keywords: Biological sequence; DNA sequence; Data mining; Ensemble learning; Time series.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms*
Base Sequence
Learning*
Machine Learning
Time Factors

Grants and funding

This work was supported by the National Natural Science Foundation of China (No. 51663001, 52063002, 42061067, 61741202). There was no additional external funding received for this study. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.