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MLWNNR: LncRNA-Disease Association Prediction with Multi-Kernel Learning-Driven Weighted Nuclear Norm Regularization.

Guo-Bo Xie1, Hao-Jie Xu1, Guo-Sheng Gu2

  • 1School of Computer Science, Guangdong University of Technology, Guangzhou, 510000, China.

Interdisciplinary Sciences, Computational Life Sciences
|June 23, 2025
PubMed
Summary

This study introduces a new algorithm, MLWNNR, to predict links between long non-coding RNAs (lncRNAs) and diseases. The method accurately identifies potential associations, aiding in understanding human pathologies.

Keywords:
k-nearest neighbor centered kernel learning algorithmMulti-kernel learningWeighted nuclear norm regularizationdiseaselncRNA

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Long non-coding RNAs (lncRNAs) are key regulators in human diseases.
  • Predicting lncRNA-disease associations is crucial for understanding disease mechanisms.

Purpose of the Study:

  • To develop a robust algorithm for forecasting lncRNA-disease associations.
  • To leverage multi-kernel learning and network completion for accurate predictions.

Main Methods:

  • Utilized a k-nearest neighbors-based kernel learning algorithm to integrate multi-similarity kernels.
  • Constructed a heterogeneous lncRNA-disease association network.
  • Applied weighted nuclear norm regularization for network completion and association scoring.

Main Results:

  • The MLWNNR algorithm demonstrated superior performance on three datasets compared to six other models.
  • Case studies confirmed a majority of predicted lncRNA-disease associations with existing literature.
  • The model exhibited robustness and excellent generalization capabilities.

Conclusions:

  • MLWNNR provides a reliable computational approach for inferring lncRNA-disease associations.
  • This method can aid in identifying novel biomarkers and therapeutic targets for human diseases.