ML Anthology

Improving Protein Subcellular Localization Prediction with Structural Prediction & Graph Neural Networks

Geoffroy Dubourg-Felonneau, Arash Abbasi, Eyal Akiva, Lawrence Lee
NeurIPSW 2022
/neuripsw/2022/dubourgfelonneau2022neuripsw-improving/

Abstract

We present a method that improves subcellular localization prediction for proteins based on their sequence by leveraging structure prediction and Graph Neural Networks. We demonstrate that Language Models, trained on protein sequences, and Graph Neural Nets, trained on protein's 3D structures, are both efficient approaches. They both learn meaningful, yet different representations of proteins; hence, ensembling them outperforms the reigning state of the art method.

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Cite

Text

Dubourg-Felonneau et al. "Improving Protein Subcellular Localization Prediction with Structural Prediction & Graph Neural Networks." NeurIPS 2022 Workshops: LMRL, 2022.

Markdown

[Dubourg-Felonneau et al. "Improving Protein Subcellular Localization Prediction with Structural Prediction & Graph Neural Networks." NeurIPS 2022 Workshops: LMRL, 2022.](https://mlanthology.org/neuripsw/2022/dubourgfelonneau2022neuripsw-improving/)

BibTeX

@inproceedings{dubourgfelonneau2022neuripsw-improving,
  title     = {{Improving Protein Subcellular Localization Prediction with Structural Prediction & Graph Neural Networks}},
  author    = {Dubourg-Felonneau, Geoffroy and Abbasi, Arash and Akiva, Eyal and Lee, Lawrence},
  booktitle = {NeurIPS 2022 Workshops: LMRL},
  year      = {2022},
  url       = {https://mlanthology.org/neuripsw/2022/dubourgfelonneau2022neuripsw-improving/}
}