Our peer-reviewed research on the application of deep learning approaches to the gap-filling problem in genome assembly (GapPredict) was just published

In our peer-reviewed manuscript, just published in the journal IEEE-TCBB, we report on a novel application, GapPredict, for resolving gaps in genome sequence assemblies. This proof-of concept study demonstrates the practical utility of deep-learning machine learning models for this task.