A capsule network-based method for identifying transcription factors

Peijie Zheng; Yue Qi; Xueyong Li; Yuewu Liu; Yuhua Yao; Guohua Huang

doi:10.3389/fmicb.2022.1048478

A capsule network-based method for identifying transcription factors

Front Microbiol. 2022 Dec 6:13:1048478. doi: 10.3389/fmicb.2022.1048478. eCollection 2022.

Authors

Peijie Zheng¹, Yue Qi¹, Xueyong Li¹, Yuewu Liu², Yuhua Yao³, Guohua Huang¹

Affiliations

¹ School of Electrical Engineering, Shaoyang University, Shaoyang, China.
² College of Information and Intelligence, Hunan Agricultural University, Changsha, China.
³ School of Mathematics and Statistics, Hainan Normal University, Haikou, China.

Abstract

Transcription factors (TFs) are typical regulators for gene expression and play versatile roles in cellular processes. Since it is time-consuming, costly, and labor-intensive to detect it by using physical methods, it is desired to develop a computational method to detect TFs. Here, we presented a capsule network-based method for identifying TFs. This method is an end-to-end deep learning method, consisting mainly of an embedding layer, bidirectional long short-term memory (LSTM) layer, capsule network layer, and three fully connected layers. The presented method obtained an accuracy of 0.8820, being superior to the state-of-the-art methods. These empirical experiments showed that the inclusion of the capsule network promoted great performances and that the capsule network-based representation was superior to the property-based representation for distinguishing between TFs and non-TFs. We also implemented the presented method into a user-friendly web server, which is freely available at http://www.biolscience.cn/Capsule_TF/ for all scientific researchers.

Keywords: LSTM; capsule network; deep learning; semantics; transcription factors.

Grants and funding

This study was supported by the National Natural Science Foundation of China (62272310 and 62162025), the Hunan Province Natural Science Foundation of China (2022JJ50177 and 2020JJ4034), the Scientific Research Fund of Hunan Provincial Education Department (21A0466 and 19A215), and the Shaoyang University Innovation Foundation for Postgraduate (CX2021SY052).