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resistBERT

Text Classification
Transformers
Safetensors
bert
Trained with AutoTrain
protein-classification
protbert
antiobiotic-resistance
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metadata
tags:
  - autotrain
  - text-classification
  - protein-classification
  - protbert
  - antiobiotic-resistance
widget:
  - text: I love AutoTrain
  - text: >-
      M T L A L V G E K I D R N R F T G E K V E N S T F F N C D F S G A D L S G
      T E F I G C Q F Y D R E S Q K G C N F S R A N L K D A I F K S C D L S M A
      D F R N I N A L G I E I R H C R A Q G S D F R G A S F M N M I T T R T W F
      C S A Y I T N T N L S Y A N F S K V V L E K C E L W E N R W M G T Q V L G
      A T F S G S D L S G G E F S S F D W R A A N V T H C D L T N S E L G D L D
      I R G V D L Q G V K L D S Y Q A S L L L E R L G I A V M G
datasets:
  - as-cle-bert/AMR-Gene-Families
pipeline_tag: text-classification
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resistML

A tool for AMR gene family prediction, simple and ML-based. Please refer to this GitHub repository.

Training

Data collection for training

Latest reference sequences release (Feb 2024) were downloaded from CARD (The Comprehensive Antibiotic Resistance Database). If you want to automatically download them too, use this link <https://card.mcmaster.ca/latest/data>_.

Protein sequences were mapped with their ARO indices to the corrresponding AMR gene families (see this file for reference) and the 12 most common families were chosen to train resistML and resistBERT.

Training procedures

resistML (stable)

resistML was trained starting from all the protein sequences retrieved beforehands, extracting their features in a csv file.

Features were extracted through biopython Bio.SeqUtils.ProtParam --> ProteinAnalysis subclass, and they are (maiusc is for the header you can find in the csv):

  • HIDROPHOBICITY score
  • ISOELECTRIC point
  • AROMATICity
  • INSTABility
  • MW (molar weight)
  • HELIX,TURN,SHEET (percentage of these three secondary strcutures)
  • MOL_EXT_RED,MOL_EXT_OX (molar extinction reduced and oxidized)

Dataset building occured here

The base model itself is a simple Voting Classifier based on a DecisionTreeClassifier, ExtraTreesClassifier and HistGradientBoostingClassifier, all provided by scikit-learn library.

During validation, it yielded 100% accuracy on predicting training data.

resistBERT (unstable)

resistBERT is a BERT model for text classification, finetuned from prot_bert by RosettaLab.

Data using from finetuning were a selection of 1496 sequences out of the total 1836 ones. 80% were used for training, 20% were used for validations.

Sequences were preprocessed and labelled here, then the complete jsonl file was reduced here and uploaded to Huggingface under the identifier as-cle-bert/AMR-Gene-Families through this script.

Finetuning occurred from the HF dataset thanks to AutoTrain: during validation, the model yielded the following stats:

  • loss: 0.08235077559947968

  • f1_macro: 0.986759581881533

  • f1_micro: 0.99

  • f1_weighted: 0.9899790940766551

  • precision_macro: 0.9871615312791784

  • precision_micro: 0.99

  • precision_weighted: 0.9901213818860879

  • recall_macro: 0.986574074074074

  • recall_micro: 0.99

  • recall_weighted: 0.99

  • accuracy: 0.99

The model is now available on Huggingface under the identifier as-cle-bert/resistBERT. There is also a widget through which you can make inferences thanks to HF Inference API. Keep in mind that Inference API can be unstable, so downloading the model and using it from a local machine/cloud service would be preferable.

Testing

Data retrieval for tests

Data were downloaded from CARD (The Comprehensive Antibiotic Resistance Database), as the annotations for the family names used to label training sequences were the same.

For families "PDC beta-lactamase", "CTX-M beta-lactamase", "SHV beta-lactamase", "CMY beta-lactamase", sequences were downloaded after having searched the exact AMR gene family as in the labels used for training, through Download sequences method. In the downloading customization page, filters were set to is_a and Protein.

For all the other families, procedure was the same but customization filters were set to is_a, structurally_homologous_to, evolutionary_variant_of and Protein to increase the number of retrieved sequences.

Test building

Test were built thanks to this script.

These are the test metadata:

Metadata for test 0:

  • Protein statistics for resistML were saved in test/testfiles/test_0.csv
  • Sequences and labels for resistBERT were saved in test/testfiles/test_0.jsonl
  • 12 protein sequences were taken into account for 2 families
  • Families taken into account were: quinolone resistance protein (qnr), CMY beta-lactamase

Metadata for test 1:

  • Protein statistics for resistML were saved in test/testfiles/test_1.csv
  • Sequences and labels for resistBERT were saved in test/testfiles/test_1.jsonl
  • 11 protein sequences were taken into account for 2 families
  • Families taken into account were: VIM beta-lactamase,IMP beta-lactamase

Metadata for test 2:

  • Protein statistics for resistML were saved in test/testfiles/test_2.csv
  • Sequences and labels for resistBERT were saved in test/testfiles/test_2.jsonl
  • 13 protein sequences were taken into account for 2 families
  • Families taken into account were: quinolone resistance protein (qnr),SHV beta-lactamase

Metadata for test 3:

  • Protein statistics for resistML were saved in test/testfiles/test_3.csv
  • Sequences and labels for resistBERT were saved in test/testfiles/test_3.jsonl
  • 10 protein sequences were taken into account for 3 families
  • Families taken into account were: quinolone resistance protein (qnr),VIM beta-lactamase,CMY beta-lactamase

Metadata for test 4:

  • Protein statistics for resistML were saved in test/testfiles/test_4.csv
  • Sequences and labels for resistBERT were saved in test/testfiles/test_4.jsonl
  • 12 protein sequences were taken into account for 2 families
  • Families taken into account were: CMY beta-lactamase,IMP beta-lactamase

Metadata for test 5:

  • Protein statistics for resistML were saved in test/testfiles/test_5.csv
  • Sequences and labels for resistBERT were saved in test/testfiles/test_5.jsonl
  • 12 protein sequences were taken into account for 2 families
  • Families taken into account were: VIM beta-lactamase,SHV beta-lactamase

Metadata for test 6:

  • Protein statistics for resistML were saved in test/testfiles/test_6.csv
  • Sequences and labels for resistBERT were saved in test/testfiles/test_6.jsonl
  • 11 protein sequences were taken into account for 3 families
  • Families taken into account were: PDC beta-lactamase,MCR phosphoethanolamine transferase,ACT beta-lactamase

Metadata for test 7:

  • Protein statistics for resistML were saved in test/testfiles/test_7.csv
  • Sequences and labels for resistBERT were saved in test/testfiles/test_7.jsonl
  • 10 protein sequences were taken into account for 3 families
  • Families taken into account were: MCR phosphoethanolamine transferase,CTX-M beta-lactamase,PDC beta-lactamase

Metadata for test 8:

  • Protein statistics for resistML were saved in test/testfiles/test_8.csv
  • Sequences and labels for resistBERT were saved in test/testfiles/test_8.jsonl
  • 12 protein sequences were taken into account for 2 families
  • Families taken into account were: ACT beta-lactamase,CMY beta-lactamase

Metadata for test 9:

  • Protein statistics for resistML were saved in test/testfiles/test_9.csv
  • Sequences and labels for resistBERT were saved in test/testfiles/test_9.jsonl
  • 15 protein sequences were taken into account for 3 families
  • Families taken into account were: quinolone resistance protein (qnr),SHV beta-lactamase,KPC beta-lactamase

All data can be found here, along with the seqences used to generate them.

Test results

resistML yielded 100% accuracy, f1 score, recall score and precision score in all 10 tests.

resistBERT was more unstable:

  • On test_0, test_2, test_4, test_6, test_7, test_8 and test_9 yielded 100% accuracy, f1 score, recall score and precision score
  • On test_1 it yielded:
    1. Accuracy: 50%
    2. f1 score: 33%
    3. Precision: 25%
    4. Recall: 50%
  • On test_3 it yielded 66.7% accuracy, f1 score, recall score and precision score
  • On test_5 it yielded 50% accuracy, f1 score, recall score and precision score

All results for resistBERT can be found in the dedicated notebook .

License and rights of usage

The GitHub repository is provided under MIT license (more at LICENSE`).

If you use this work for your projects, please consider citing the author Astra Bertelli.

References

  1. CARD - The Comprehensive Antibiotic Resistance Database

  2. Biopython

  3. Scikit-learn

  4. Hugging Face's prot_bert Model

  5. Hugging Face's AutoTrain

If you feel that your work was relevant in building resistML and you weren't referenced in this section, feel free to flag an issue on GitHub or to contact the author.