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piRNA - Piwi-interacting RNAs02:57

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PIWI-interacting RNAs, or piRNAs, are the most abundant short non-coding RNAs. More than 20,000 genes have been found in humans that code for piRNAs while only 2000 genes have been found for miRNAs. piRNAs can act at the transcriptional and post-transcriptional levels and have a vital role in silencing transposable elements present in germ cells. They are also involved in epigenetic silencing and activation. Previously, they were thought to function only in germ cells but new evidence suggests...
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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
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In humans, more than 80% of the genome gets transcribed. However, only around 2% of the genome codes for proteins. The remaining part produces non-coding RNAs which includes ribosomal RNAs, transfer RNAs, telomerase RNAs, and regulatory RNAs, among other types. A large number of regulatory non-coding RNAs have been classified into two groups depending upon their length – small non-coding RNAs, such as microRNA, which are less than 200 nucleotides in length, and long non-coding RNA...
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PPDAMEGCN: Predicting piRNA-Disease Associations Based on Multi-Edge Type Graph Convolutional Network.

Yinglong Peng1, Shuang Chu2, Xindi Huang2

  • 1School of Information and Intelligence, XiangXi Vocational and Technical College for Nationalities, Jishou, China.

IET Systems Biology
|March 22, 2025
PubMed
Summary

This study introduces PPDAMEGCN, a novel computational method for predicting Piwi-interacting RNA (piRNA)-disease associations. By utilizing a multi-edge type graph convolutional network, it improves accuracy over traditional approaches for understanding these crucial biological links.

Keywords:
association predictionmulti‐edge type graph convolutional networkpiRNA‐disease associationssimilarity

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Piwi-interacting RNAs (piRNAs) are crucial in biological processes and linked to human diseases.
  • Existing computational methods for piRNA-disease association prediction have limitations.
  • Traditional graph convolutional networks (GCNs) do not differentiate between various association types.

Purpose of the Study:

  • To develop an advanced computational method for predicting piRNA-disease associations.
  • To address the limitations of existing GCNs by incorporating multi-edge types.
  • To enhance the accuracy of identifying known and unknown piRNA-disease relationships.

Main Methods:

  • Developed PPDAMEGCN, a multi-edge type graph convolutional network model.
  • Calculated piRNA sequence similarity using Smith-Waterman and GIP kernel similarities.
  • Computed disease semantic similarity via Disease Ontology (DO) and GIP kernel similarities.
  • Constructed integrated piRNA and disease similarity networks.
  • Obtained embeddings using a heterogeneous piRNA-disease association network and predicted associations with an MLP.

Main Results:

  • The PPDAMEGCN model demonstrated superior performance in predicting piRNA-disease associations.
  • Experimental results confirmed the method's effectiveness compared to existing prediction techniques.
  • The multi-edge type GCN approach successfully captured distinct signals from different association types.

Conclusions:

  • PPDAMEGCN offers a significant advancement in computational prediction of piRNA-disease associations.
  • The multi-edge type GCN framework is effective for analyzing complex biological networks.
  • This method accelerates the identification of potential piRNA-disease links, aiding biomedical research.