GOProFormer

This repository corresponds to the article titled as "GOProFormer: A Multi-Modal Transformer Method for Gene Ontology Protein Function Prediction".

Data pre-processing steps:

• python data_preprocess/create_uniprot_species_dict.py

• python data_preprocess/goa_random_split_leakage.py

• python data_preprocess/goa_time_delay_no_knowledge.py

• python data_preprocess/goa_time_series_no_knowledge.py

• To compute the sequence representation:

  • Install ESM-1b (particularly used esm.pretrained.esm1_t12_85M_UR50S)
  • python data_preprocess/compute_seq_rep_using_esm1b.py

Model development

• python models/train_val.py
• python models/eval_on_val_and_test.py

Downloadables

• Trained weights can be downloaded from here.
• A copy of dataset can be obtained from here.

Citation

If the model is found useful, we request to cite the relevant paper:

@Article{biom12111709,
    AUTHOR = {Kabir, Anowarul and Shehu, Amarda},
    TITLE = {GOProFormer: A Multi-Modal Transformer Method for Gene Ontology Protein Function Prediction},
    JOURNAL = {Biomolecules},
    VOLUME = {12},
    YEAR = {2022},
    NUMBER = {11},
    ARTICLE-NUMBER = {1709},
    URL = {https://www.mdpi.com/2218-273X/12/11/1709},
    PubMedID = {36421723},
    ISSN = {2218-273X},
    DOI = {10.3390/biom12111709}
}