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 here 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 soderhamn ottoman cover can be accessed using publicly available and user-friendly web tool athttp://nsclbio.
jbnu.ac.kr/tools/piRDA/.