Attention-based deep learning for DNA repair outcome prediction: Learning how the cell repairs DNA breaks using local sequence context

Recent advancements in quantification of repair outcomes of CRISPR-Cas9 mediated double-stranded DNA breaks (DSBs) have allowed for the use of machine learning for predicting the frequencies of these repair outcomes. Local DNA sequence context influences the frequencies of mutations that arise when DNA gets repaired after it is targeted by...

Long-term traffic flow predictions in a transformer-based framework: Capturing temporal and external features, to obtain a traffic flow prediction for the next 24 hours

Traffic flow predictions are an important component in the rising demand for solutions to cope with the increasing pressure on transportation networks. Especially on a long prediction horizon, traffic flow predictions remain challenging due to the complex, nonlinear nature of traffic flow and the influence of both temporal and external features....