Predicting Antibiotic Resistance with a Language AI Model
Typ
Examensarbete för masterexamen
Program
Complex adaptive systems (MPCAS), MSc
Publicerad
2022
Författare
Örtenberg Toftås, Mathias
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
The effectiveness of utilizing a BERT language AI model to predict antibiotic resistance
within beta-lactamase/transpeptidase-like proteins is tested. The performance
of the model is compared to a traditional Hidden Markov Model (HMM) to verify its
capabilities. To further ensure the models capabilities, several tests such as crossvalidation
and resistance towards sequence read errors are performed. We find that
the BERT model outperforms the HMM model on amino acid sequences with lengths
around 40-50 and shorter. These are the type of sequence lengths which we expect
to encounter in our use case. For longer sequences, the HMM model is preferable
as it requires less computation time. We also find that the BERT model and the
HMM model has learned different aspects about the data, allowing the combination
of the results of both methods to achieve even finer results.
Keywords:
Beskrivning
Ämne/nyckelord
Antibiotic Resistance, BERT, Beta-lactamase/transpeptidase-like, Hidden Markov Model