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MicroRNA (miRNA) are short, regulatory RNA transcribed from introns (non-coding regions of a gene) or intergenic regions (stretches of DNA present between genes). Several processing steps are required to form biologically active, mature miRNA. The initial transcript, called primary miRNA (pri-mRNA), base-pairs with itself, forming a stem-loop structure. Within the nucleus, an endonuclease enzyme, called Drosha, shortens the stem-loop structure into hairpin-shaped pre-miRNA. After the pre-miRNA...
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Proteins are involved in several cellular processes and biochemical reactions. Analyzing a specific protein of interest requires it to be isolated from the other proteins in the cell. This is achieved by overexpressing the specific gene in a suitable host to produce large quantities of the target protein. A tag or label is recombined with the gene to produce a fusion protein containing the target protein and the tag. The tags on these fusion proteins can then be used for easy detection and...
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Predicting Pseudogene-miRNA Associations Based on Feature Fusion and Graph Auto-Encoder.

Shijia Zhou1, Weicheng Sun1, Ping Zhang1

  • 1Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China.

Frontiers in Genetics
|December 30, 2021
PubMed
Summary

Pseudogenes and microRNAs (miRNAs) interact in complex regulatory networks. A new model, PMGAE, accurately predicts these pseudogene-miRNA associations, aiding disease research.

Keywords:
ceRNA networkextreme gradient boostingfeature fusiongraph auto-encodermicroRNApseudogene

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Area of Science:

  • Genomics
  • Molecular Biology
  • Bioinformatics

Background:

  • Pseudogenes, once considered non-functional, are now known to be transcribed and play regulatory roles.
  • microRNAs (miRNAs) are crucial non-coding RNAs involved in cellular regulation.
  • Pseudogenes and miRNAs interact, forming ceRNA networks that influence biological processes and diseases.

Purpose of the Study:

  • To develop a computational model for predicting pseudogene-miRNA associations (PMAs).
  • To leverage feature fusion, graph auto-encoder (GAE), and eXtreme Gradient Boosting (XGBoost) for accurate PMA prediction.
  • To facilitate the clinical diagnosis of diseases by exploring pseudogene-miRNA interactions.

Main Methods:

  • Calculated Jaccard, cosine, and Pearson similarities between pseudogene and miRNA nodes.
  • Fused similarity profiles to create initial node representation features.
  • Employed a GAE to obtain low-dimensional node embeddings by aggregating similarity and association data.
  • Utilized an XGBoost classifier to predict novel PMAs based on GAE embeddings.

Main Results:

  • The PMGAE model achieved a mean AUC of 0.8634 and a mean AUPR of 0.8966 in five-fold cross-validation.
  • Case studies demonstrated the model's reliability in identifying PMAs.
  • The results support the utility of PMGAE for understanding endogenous RNA networks and their relation to diseases.

Conclusions:

  • PMGAE is an effective computational tool for predicting pseudogene-miRNA associations.
  • The model contributes to understanding the functional roles of pseudogenes and miRNAs in biological regulation.
  • This approach has potential applications in disease biomarker discovery and therapeutic target identification.