Alexander Rosenberg Johansen

GraphPart: Homology partitioning for biological sequence analysis

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 achieves signal peptide prediction across all types using protein language models

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

NetSolP: predicting protein solubility in E. coli using language models

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

Deep protein representations enable recombinant protein expression prediction

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.

Prediction of GPI-Anchored proteins with pointer neural networks

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.

Language modelling for biological sequences - curated datasets and baselines

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.