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mRNA-CLA: An interpretable deep learning approach for predicting mRNA subcellular localization.

Yifan Chen1, Zhenya Du2, Xuanbai Ren3

  • 1Institute of Artificial Intelligence Application, College of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha, Hunan 410004, China.

Methods (San Diego, Calif.)
|May 5, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces mRNA-CLA, a deep learning framework for predicting messenger RNA (mRNA) locations within cells. The novel approach accurately annotates multiple cellular sites, improving upon existing prediction methods.

Keywords:
Model interpretabilitySelf-attention weightsSequence analysisSubcellular localization

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Area of Science:

  • Molecular Biology
  • Bioinformatics
  • Computational Biology

Background:

  • Messenger RNA (mRNA) plays a crucial role in gene regulation and protein synthesis.
  • Accurate prediction of mRNA subcellular localization is essential but current methods are limited, especially for multi-label annotations.
  • Enhancing prediction accuracy and capability for multiple localizations is a key challenge in post-transcriptional gene regulation studies.

Purpose of the Study:

  • To develop an innovative multi-label subcellular localization prediction framework for mRNA.
  • To improve the accuracy and scope of predicting where mRNA molecules are located within a cell.
  • To provide a more interpretable model for analyzing mRNA sequence features related to localization.

Main Methods:

  • Developed the mRNA-CLA framework using a deep learning approach.
  • Incorporated a multi-head self-attention mechanism for sequence analysis.
  • Utilized multi-scale convolutional layers for feature extraction and Position Weight Matrices (PWMs) for interpretability.

Main Results:

  • The mRNA-CLA model demonstrated superior performance compared to existing methods.
  • Successfully annotated mRNA sequences with multiple subcellular localizations.
  • Identified nucleotide specificities associated with different subcellular localization sites through base-level analysis.

Conclusions:

  • The proposed mRNA-CLA framework significantly advances mRNA subcellular localization prediction.
  • The deep learning approach with self-attention offers a powerful and interpretable solution.
  • This work provides a valuable tool for understanding post-transcriptional gene regulation and mRNA dynamics.