MLCB 2021 - RAPPPID: Towards Generalisable Protein Interaction Prediction with AWD-LSTM Twin Networks

Presenting our RAPPPID manuscript at the Machine Learning in Computational Biology conference. Held virtually on November 22nd and 23rd.

Poster

You can download the poster we presented at MLCB 2021 here: [PDF 3.3 MB]

RAPPPID

Manuscript

This poster reports findings from our manuscript of the same name.

RAPPPID: Towards Generalisable Protein Interaction Prediction with AWD-LSTM Twin Networks bioRxiv 2021.08.13.456309; DOI: 10.1101/2021.08.13.456309

References

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  3. Merity, S. et al. (2017) Regularizing and Optimizing LSTM Language Models. arXiv:1708.02182 [cs].
  4. Kudo, T. and Richardson, J. (2018) SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing. In, Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations. Association for Computational Linguistics, Brussels, Belgium, pp. 66–71 DOI: 10.18653/v1/D18-2012
  5. Szklarczyk,D. et al. (2019) STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res, 47, D607–D613. DOI: 10.1093/nar/gky1131