Archaeoinformatics - Data Science

BA: Application of Language Models to Examine beta-Lactam Resistant AMRs

Contact: Steffen Strohm, M.Sc., Christian Beth, M.Sc.

Embedding_Visualization

For this work a bi-directional long-short-term-memory (bi-LSTM) neural network used for the study of viral mutations was adapted for bacteria. The network was trained on Lactamase and antimicrobiological resistant bacterial DNA-sequences, different settings for network parameters were studied. For these data sets embeddings into subsets of Rn for different embedding dimensions were created and analyzed via visualization methods based on dimension reduction. The networks ability to predict the likeliness of certain mutations was used ta analyze the correlation of the networks results to biological fitness-data of some mutations in the presence of two antibiotics.