ABSTRACT
Background: Piwi-interacting RNAs [piRNAs] are small non-coding RNAs [ncRNAs], with a length of about 24-32 nucleotides, which have been discovered recently. These ncRNAs play an important role in germline development, transposon silencing, epigenetic regulation, protecting the genome from invasive transposable elements, and the pathophysiology of diseases such as cancer. piRNA identification is challenging due to the lack of conserved piRNA sequences and structural elements
Methods: To detect piRNAs, an appropriate feature set, including 8 diverse feature groups to encode each RNA was applied. In addition, a Support Vector Machine [SVM] classifier was used with optimized parameters for RNA classification. According to the obtained results, the classification performance using the optimized feature subsets was much higher than the one in previously published studies
Results: Our results revealed 98% accuracy, Mathew' correlation coefficient of 98% and 99% specificity in discriminating piRNAs from the other RNAs. Also, the obtained results show that the proposed method outperforms its competitors
Conclusion: In this paper, a prediction method was proposed to identify piRNA in human. Also, 48 heterogeneous features [sequence and structural features] were used to encode RNAs. To assess the performance of the method, a benchmark dataset containing 515 piRNAs and 1206 types of other RNAs was constructed. Our method reached the accuracy of 99% on the benchmark dataset. Also, our analysis revealed that the structural features are the most contributing features in piRNA prediction
ABSTRACT
Prostate cancer, a serious genetic disease, has known as the first widespread cancer in men, but the molecular changes required for the cancer progression has not fully understood. Availability of high-throughput gene expression data has led to the development of various computational methods, for identification of the critical genes, have involved in the cancer. In this paper, we have shown the construction of co-expression networks, which have been using Y-chromosome genes, provided an alternative strategy for detecting of new candidate, might involve in prostate cancer. In our approach, we have constructed independent co-expression networks from normal and cancerous stages have been using a reverse engineering approach. Then we have highlighted crucial Y chromosome genes involved in the prostate cancer, by analyzing networks, based on party and date hubs. Our results have led to the detection of 19 critical genes, related to prostate cancer, which 12 of them have previously shown to be involved in this cancer. Also, essential Y chromosome genes have searched based on reconstruction of sub-networks which have led to the identification of 4 experimentally established as well as 4 new Y chromosome genes might be linked putatively to prostate cancer. Correct inference of master genes, which mediate molecular, has changed during cancer progression would be one of the major challenges in cancer genomics. In this paper, we have shown the role of Y chromosome genes in finding of the prostate cancer susceptibility genes. Application of our approach to the prostate cancer has led to the establishment of the previous knowledge about this cancer as well as prediction of other new genes