Identification of piRNA disease associations using deep learning

Syed Danish Ali; Hilal Tayara; Kil To Chong

doi:10.1016/j.csbj.2022.02.026

Identification of piRNA disease associations using deep learning

Comput Struct Biotechnol J. 2022 Mar 3:20:1208-1217. doi: 10.1016/j.csbj.2022.02.026. eCollection 2022.

Authors

Syed Danish Ali^{1

2}, Hilal Tayara³, Kil To Chong^{1

4}

Affiliations

¹ Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, South Korea.
² The University of Azad Jammu and Kashmir, Muzaffarabad 13100, Pakistan.
³ School of International Engineering and Science, Jeonbuk National University, Jeonju 54896, South Korea.
⁴ Advanced Electronics and Information Research Center, Jeonbuk National University, Jeonju 54896, South Korea.

Abstract

Piwi-interacting RNAs (piRNAs) play a pivotal role in maintaining genome integrity by repression of transposable elements, gene stability, and association with various disease progressions. Cost-efficient computational methods for the identification of piRNA disease associations promote the efficacy of disease-specific drug development. In this regard, we developed a simple, robust, and efficient deep learning method for identifying the piRNA disease associations known as piRDA. The proposed architecture extracts the most significant and abstract information from raw sequences represented in a simplicated piRNA disease pair without any involvement of features engineering. Two-step positive unlabeled learning and bootstrapping technique are utilized to abstain from the false-negative and biased predictions dealing with positive unlabeled data. The performance of proposed method piRDA is evaluated using k-fold cross-validation. The piRDA is significantly improved in all the performance evaluation measures for the identification of piRNA disease associations in comparison to state-of-the-art method. Moreover, it is thus projected conclusively that the proposed computational method could play a significant role as a supportive and practical tool for primitive disease mechanisms and pharmaceutical research such as in academia and drug design. Eventually, the proposed model can be accessed using publicly available and user-friendly web tool athttp://nsclbio.jbnu.ac.kr/tools/piRDA/.

Keywords: Convolutional Neural Network; Deep learning; Positive unlabeled learning; Reliable negative sample; Sequence analysis; Web-server; piRNA disease associations.