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Self-Weighted Multi-Kernel Multi-Label Learning for Potential miRNA-Disease Association Prediction.

Zhenxia Pan1, Huaxiang Zhang1, Cheng Liang1

  • 1School of Information Science and Engineering, Shandong Normal University, Jinan 250358, China.

Molecular Therapy. Nucleic Acids
|July 19, 2019
PubMed
Summary
This summary is machine-generated.

A new computational method, self-weighted, multi-kernel, multi-label learning (SwMKML), improves prediction of disease-related microRNAs (miRNAs). This tool achieved high accuracy and identified potential biomarkers for head and neck neoplasms.

Keywords:
graph-based learningmiRNA-disease association predictionmulti-kernel learning

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Area of Science:

  • Biomedical Informatics
  • Genomics
  • Computational Biology

Background:

  • MicroRNAs (miRNAs) are crucial in disease development.
  • Existing computational models for miRNA-disease association prediction require improvement.

Purpose of the Study:

  • To develop a novel computational method, SwMKML, for enhanced prediction of miRNA-disease associations.
  • To improve the accuracy and reliability of identifying disease-related miRNAs.

Main Methods:

  • Proposed a self-weighted, multi-kernel, multi-label learning (SwMKML) method.
  • SwMKML adaptively learns optimal kernel matrices for miRNAs and diseases.
  • Utilized a multi-label framework for simultaneous updating of predicted associations.

Main Results:

  • SwMKML achieved superior prediction accuracy: 95.5% (global leave-one-out), 93.1% (5-fold cross-validation), and 84.1% (overall).
  • Outperformed four state-of-the-art computational models.
  • Identified hsa-mir-125b-1 and hsa-mir-125b-2 as potential prognostic biomarkers for head and neck neoplasms.

Conclusions:

  • SwMKML offers a powerful and accurate approach for predicting miRNA-disease associations.
  • The method has potential as an effective tool for identifying disease biomarkers.
  • SwMKML is available as an open-source tool for further research.