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Protein Networks02:26

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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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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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Identification of RNAs Engaged in Direct RNA-RNA Interaction with a Long Non-Coding RNA
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Identification of RNAs Engaged in Direct RNA-RNA Interaction with a Long Non-Coding RNA

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Predicting lncRNA-miRNA interactions based on interactome network and graphlet interaction.

Li Zhang1, Ting Liu2, Haoyu Chen3

  • 1School of Life Science, Liaoning University, Shenyang, 110036, China; Research Center for Computer Simulating and Information Processing of Bio-macromolecules of Shenyang, Liaoning University, Shenyang, 110036, China; Technology Innovation Center for Computer Simulating and Information Processing of Bio-macromolecules of Shenyang, Shenyang, 110036, China.

Genomics
|February 15, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces LMI-INGI, a novel computational method to predict interactions between long non-coding RNAs (lncRNAs) and microRNAs (miRNAs). The approach offers a cost-effective and efficient way to understand gene regulation in human diseases.

Keywords:
Graphlet interactionInteraction predictionInteractome networklncRNAmiRNA

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Genomics

Background:

  • Long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) play crucial regulatory roles in human diseases.
  • Understanding lncRNA-miRNA interactions is vital, but experimental methods are costly and time-consuming.
  • Existing knowledge on these regulatory relationships remains incomplete.

Purpose of the Study:

  • To develop and validate a computational approach for predicting latent interactions between lncRNAs and miRNAs.
  • To provide an efficient and cost-effective alternative to experimental methods for identifying lncRNA-miRNA interactions.
  • To forecast potential regulatory networks involved in human disease development and treatment.

Main Methods:

  • Applied a semi-supervised, interactome network-based strategy.
  • Constructed graphs based on lncRNA and miRNA similarity.
  • Calculated interaction scores for lncRNA-miRNA pairs using graphlet interaction isomer analysis.

Main Results:

  • The developed model, LMI-INGI, demonstrated high reliability with a fivefold cross-validation AUC of 0.8957.
  • Performance metrics including PRE (0.6815), REC (0.8842), F1 score (0.7452), and AUPR (0.9213) confirmed model efficacy.
  • LMI-INGI showed strong generalizability and superiority over four other algorithms, with AUC values of 0.9197 and 0.9006 when tested with expression profile and function similarity data.

Conclusions:

  • The LMI-INGI model is a powerful and accurate tool for predicting lncRNA-miRNA interactions.
  • This computational approach significantly reduces the cost and time associated with identifying these crucial regulatory relationships.
  • LMI-INGI is expected to be valuable for advancing research in gene regulation and human disease mechanisms.