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Adaptive multi-view multi-label learning for identifying disease-associated candidate miRNAs.

Cheng Liang1, Shengpeng Yu1, Jiawei Luo2

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

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This study introduces a new computational method, Adaptive Multi-View Multi-Label learning (AMVML), to accurately identify microRNAs (miRNAs) linked to human diseases. AMVML effectively uncovers novel disease-related miRNAs, aiding in understanding disease mechanisms.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • MicroRNAs (miRNAs) are crucial in disease pathogenesis.
  • Accurate identification of disease-related miRNAs is vital for understanding disease etiology.
  • Existing computational methods for predicting miRNA-disease associations have limitations in accuracy and applicability.

Purpose of the Study:

  • To develop a novel computational method for discovering disease-related candidate miRNAs.
  • To improve the accuracy and applicability of miRNA-disease association prediction.
  • To provide a reliable tool for uncovering novel disease-related miRNAs.

Main Methods:

  • Adaptive Multi-View Multi-Label learning (AMVML) framework.
  • Adaptive affinity graph learning from multiple similarity profiles for miRNAs and diseases.
  • Simultaneous update of miRNA-disease associations using multi-label learning.
  • Theoretical convergence proof and analysis of AMVML's fast convergence rate.

Main Results:

  • AMVML demonstrated comparable or superior performance against state-of-the-art methods.
  • The method achieved high accuracy across various evaluation metrics.
  • A case study on thyroid neoplasms identified a potential diagnostic biomarker, validating the method's utility.

Conclusions:

  • AMVML is a robust and efficient tool for predicting miRNA-disease associations.
  • The method can discover a greater number of true disease-related miRNAs.
  • This approach offers significant potential for advancing our understanding of complex human diseases.