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Related Concept Videos

lncRNA - Long Non-coding RNAs02:39

lncRNA - Long Non-coding RNAs

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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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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.
GWAS does not require the identification of the target gene involved in...
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Related Experiment Video

Updated: May 17, 2025

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Neighborhood-Regularized Matrix Factorization for lncRNA-Disease Association Identification.

Jihwan Ha1, Kwangsu Kim2

  • 1Major of Big Data Convergence, Division of Data Information Science, Pukyong National University, Busan 48513, Republic of Korea.

International Journal of Molecular Sciences
|May 14, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces NRMFLDA, a novel model for predicting long non-coding RNAs (lncRNAs) linked to diseases. The model accurately identifies disease-related lncRNAs, aiding in biomarker discovery and advancing diagnostics.

Keywords:
diseaselncRNAlncRNA–disease associationmachine learningmatrix factorization

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Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • Long non-coding RNAs (lncRNAs) play critical roles in biological processes and human diseases.
  • Identifying lncRNA-disease associations is vital for disease biomarker discovery and therapeutic development.

Purpose of the Study:

  • To develop an effective computational model for inferring disease-related lncRNAs.
  • To enhance the accuracy and reliability of lncRNA-disease association predictions.

Main Methods:

  • Proposed a recommendation-system-based model named NRMFLDA.
  • Utilized matrix factorization with disease neighborhood regularization.
  • Employed leave-one-out and five-fold cross-validation for performance assessment.

Main Results:

  • NRMFLDA achieved high performance with AUC scores of 0.9143 and 0.8993.
  • Outperformed four previously established models in predicting lncRNA-disease associations.
  • Demonstrated robustness and effectiveness in identifying disease-related lncRNAs.

Conclusions:

  • NRMFLDA offers an innovative approach to uncover lncRNA-disease associations.
  • The model can significantly contribute to identifying novel disease biomarkers.
  • Advancements in diagnostic and therapeutic strategies are expected through this research.