PhageAI: a new approach to predicting the lifestyle of bacteriophages using proteinBERT and convolutional neural networks
Urbanowicz, M.; Guzinski, A.; Szulc, Z.
Show abstract
Bacteriophages are viruses that infect bacteria, including temperate, virulent and chronic phages. In the current times of increasing resistance to antibiotics (AMR), it is necessary to quickly find phages and determine their lifecycle, therefore computational methods in the first phase of composing phage preparations are necessary to find such sequences. The challenge in building such tools is the lack of good quality and correctly labeled data from multiple reference databases, which are necessary to select sequences to create cocktails that should contain complete and replication-capable phages. Current published models also do not support chronic phages, which constitute a small but significant group of phages. Another problem is explainability, current models are black-box models where we cannot observe what actually influences the models predictions, this may result in the model overlooking features that are truly associated with the phage life cycle. In the presented tool, however, these issues have been explicitly addressed. The presented tool was also compared to other lifecycle prediction models: Phatyp, PhagePred, Bacphlip and Phacts.
Matching journals
The top 10 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Identify phage hosts from metaviromic short reads based on deep learning and Markov chain model 94%
- No one tool to rule them all: Prokaryotic gene prediction tool performance is highly dependent on the organism of study 93%
- Phables: from fragmented assemblies to high-quality bacteriophage genomes 93%
Similar papers in this journal
- ORF1ab Codon Frequency Model Predicts Host-Pathogen Relationship in Orthocoronavirinae 91%
- Bacteriocin Prediction Through Cross-Validation-Based and Hypergraph-Based Feature Evaluation Approaches 91%
- Benchmarking software tools for trimming adapters and merging next-generation sequencing data for ancient DNA 90%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.