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Inferring RNA-binding protein target preferences using adversarial domain adaptation.

Ying Liu1,2, Ruihui Li3, Jiawei Luo1

  • 1College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan, China.

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|February 24, 2022
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Summary
This summary is machine-generated.

This study introduces RBP-ADDA, a novel computational framework using adversarial domain adaptation to integrate in vivo and in vitro RNA-binding protein (RBP) data. RBP-ADDA improves the prediction of RBP binding sites by effectively addressing domain shift issues between different experimental datasets.

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Genomics

Background:

  • Accurate identification of RNA-binding protein (RBP) target sites is crucial for understanding their biological roles.
  • Experimental data from in vivo and in vitro methods reveal similar but distinct RBP binding preferences, posing challenges for computational models.
  • Existing computational methods struggle to generalize across different datasets due to domain shift.

Purpose of the Study:

  • To develop a computational framework, RBP-ADDA, that integrates in vivo and in vitro RBP binding data.
  • To address the domain shift problem between different experimental datasets using adversarial domain adaptation (ADDA).
  • To improve the prediction accuracy of RBP binding sites, particularly for in vivo data.

Main Methods:

  • Implemented an adversarial domain adaptation (ADDA) framework (RBP-ADDA) to integrate disparate RBP binding datasets.
  • Pre-trained a source network on in vitro data and adapted it to in vivo data using ADDA.
  • Fine-tuned the predictive model using fused in vitro and in vivo data.
  • Applied data augmentation for RBPs with limited in vivo data.

Main Results:

  • RBP-ADDA demonstrated superior performance in modeling in vivo RBP binding data compared to existing methods, as validated by Pearson correlations.
  • The framework also enhanced predictive accuracy on in vitro datasets.
  • Data augmentation strategies further improved prediction performance for datasets with sparse in vivo information.
  • Integrated Gradients analysis revealed key nucleotide positions critical for RBP recognition.

Conclusions:

  • RBP-ADDA effectively leverages complementary information from in vivo and in vitro datasets to improve RBP binding site prediction.
  • The ADDA technique provides a robust solution for domain shift challenges in computational RBP binding analysis.
  • The framework offers insights into RBP recognition mechanisms through interpretable feature analysis.