Alexander Rosenberg Johansen

GraphPart partitions biological sequences to avoid closely related sequences in different partitions, retaining more sequences than reduction methods while maintaining homology separation quality.

SignalP 6.0 predicts all five types of signal peptides with improved accuracy and applies to metagenomic data.

NetSolP predicts protein solubility in E. coli from sequence using transformers, achieving state-of-the-art performance with reduced dataset bias.

The paper develops a machine learning model to predict recombinant protein expression in Bacillus subtilis using deep protein representations, achieving moderate performance for prioritizing expression candidates.

The paper introduces NetGPI, a new method for predicting GPI-anchored proteins using recurrent neural networks with attention mechanisms, achieving better performance than existing methods.

The paper analyzes properties of protein datasets for language modelling and evaluates n-gram and RNN models, finding that organism domain and data quality affect performance while completeness has little impact.