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AttentionPert: accurately modeling multiplexed genetic perturbations with multi-scale effects.

Ding Bai1, Caleb N Ellington2, Shentong Mo1

  • 1Machine Learning Department, Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, 00000, United Arabic Emirates.

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|June 28, 2024
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Summary

AttentionPert, a novel neural network, accurately predicts cellular gene expression changes from genetic perturbations. This computational method generalizes to new conditions, improving disease mechanism understanding and therapeutic target identification.

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Area of Science:

  • Computational Biology
  • Genomics
  • Systems Biology

Background:

  • Genetic perturbations are crucial for understanding disease mechanisms and identifying therapeutic targets.
  • Experimental assays for genetic perturbations are limited in scale.
  • Predicting cellular responses to novel genetic perturbations computationally remains a challenge.

Purpose of the Study:

  • To develop a computational method for accurately predicting gene expression under novel genetic perturbations.
  • To improve the prediction of cellular transcriptional responses to both single and multiplexed genetic perturbations.
  • To generalize predictions to unseen perturbation conditions.

Main Methods:

  • Developed AttentionPert, a novel attention-based neural network.
  • Integrated global and local effects using a multi-scale model.
  • Represented system-wide impacts and localized gene-gene similarity networks.

Main Results:

  • AttentionPert accurately predicts gene expression under multiplexed perturbations and generalizes to unseen conditions.
  • Demonstrated superior performance across multiple datasets, outperforming state-of-the-art methods.
  • Revealed novel gene regulations and improved prediction of differential gene expression.

Conclusions:

  • AttentionPert offers a significant advancement in predicting cellular responses to diverse genetic perturbations.
  • The model excels in handling out-of-distribution scenarios and complex perturbation combinations.
  • This work enhances the potential of computational methods in genetic research and drug discovery.