期刊
COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL
卷 20, 期 -, 页码 1208-1217出版社
ELSEVIER
DOI: 10.1016/j.csbj.2022.02.026
关键词
Sequence analysis; piRNA disease associations; Positive unlabeled learning; Reliable negative sample; Convolutional Neural Network; Deep learning; Web-server
资金
- National Research Foundation of Korea(NRF) - Korea government(MSIT) [2020R1A2C2005612]
- Brain Research Program of the National Research Foundation (NRF) - Korean government (MSIT) [NRF-2017M3C7A1044816]
piRNAs play a crucial role in maintaining genome integrity, and piRDA is an effective deep learning method for identifying piRNA-disease associations, facilitating drug development.
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 at http:nsclbio.jbnu.ac.kr/tools/piRDA/. (C) 2022 Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.
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