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MSlocPRED: deep transfer learning-based identification of multi-label mRNA subcellular localization.

Yun Zuo1, Bangyi Zhang1, Wenying He2

  • 1School of Artificial Intelligence and Computer Science, Jiangnan University, No. 1800 Lihu Avenue, Binhu District, Wuxi 214000, China.

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Summary
This summary is machine-generated.

This study introduces MSlocPRED, a novel computational model for predicting multi-label messenger RNA (mRNA) subcellular localization. MSlocPRED significantly outperforms existing tools, enhancing our understanding of gene expression regulation.

Keywords:
deep transfer learninginterpretable analysissequence analysissubcellular localization

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Area of Science:

  • Molecular Biology
  • Bioinformatics
  • Computational Biology

Background:

  • Subcellular localization of messenger RNA (mRNA) is crucial for regulating gene expression.
  • Existing computational methods for mRNA localization prediction often struggle with multi-label annotations and generalization.
  • Improved prediction of mRNA subcellular localization is needed for a deeper understanding of translational control.

Purpose of the Study:

  • To develop a novel computational model, MSlocPRED, for predicting multi-label mRNA subcellular localization.
  • To address the limitations of existing methods in handling multiple localization annotations and improve predictive performance.
  • To provide a robust tool for analyzing mRNA localization patterns in large biological datasets.

Main Methods:

  • mRNA sequences were preprocessed and transformed into image representations.
  • A novel MDNDO-SMDU resampling technique was employed for dataset balancing.
  • Deep transfer learning was utilized to construct the MSlocPRED model for multi-label classification.
  • SHapley Additive exPlanations (SHAP) were used for model interpretability.

Main Results:

  • MSlocPRED accurately predicts multi-label mRNA subcellular localization for 16 and 18 classes across two datasets.
  • The proposed MDNDO-SMDU resampling technique demonstrated superior performance in data preprocessing.
  • Optimal prediction accuracy was achieved when utilizing 35 nucleotides from the 5' and 3' untranslated regions (NC ends).
  • MSlocPRED significantly outperformed established prediction tools in independent testing and cross-validation.

Conclusions:

  • MSlocPRED offers a significant advancement in predicting multi-label mRNA subcellular localization.
  • The developed model and resampling technique provide valuable tools for molecular biology research.
  • The study highlights the importance of tailored preprocessing and deep learning for complex biological predictions.