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lncRNA - Long Non-coding RNAs02:39

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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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Inferring disease-associated long non-coding RNAs using genome-wide tissue expression profiles.

Xiaoyong Pan1,2, Lars Juhl Jensen2, Jan Gorodkin1

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A new machine learning method, DislncRF, identifies long non-coding RNAs (lncRNAs) linked to diseases using gene expression data. This approach improves genome-wide disease-lncRNA association inference.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Long non-coding RNAs (lncRNAs) play crucial roles in biological processes and disease development.
  • The relationship between lncRNAs and diseases is less understood compared to protein-coding genes (PCGs).
  • Genome-wide inference of disease-associated lncRNAs is essential for advancing biomedical research.

Purpose of the Study:

  • To develop and validate a novel computational method for identifying disease-associated lncRNAs.
  • To leverage tissue expression profiles for predicting lncRNA-disease associations.
  • To provide a scalable tool for genome-wide lncRNA-disease association screening.

Main Methods:

  • A machine learning approach named DislncRF was developed.
  • DislncRF utilizes random forest models trained on PCG expression profiles across human tissues.
  • The method scores potential associations between lncRNAs and diseases based on learned expression patterns.

Main Results:

  • DislncRF demonstrated promising performance in benchmarking against a gold standard dataset.
  • The proposed method outperformed existing computational approaches for lncRNA-disease association.
  • Top-scoring lncRNA candidates for specific diseases were validated through literature and independent data.

Conclusions:

  • DislncRF offers an effective and robust method for genome-wide inference of disease-associated lncRNAs.
  • The tool provides valuable insights into the roles of lncRNAs in various diseases.
  • DislncRF facilitates further research into lncRNA functions and their implications in human health.