Improving deep models of protein-coding potential with a Fourier-transform architecture and machine translation task
Fig 2
Comparison of training tasks and neural architectures.
Names have been shortened by removing the “bioseq2” prefix for all of them. (A) F1 score across five replicates of bioseq2seq, bioseq2seq-wt, bioseq2class, and bioseq2start using both LFNet and CNN architectures. (B) Analysis of CDS detection abilities by bioseq2seq variants. Rate at which predicted protein sequence aligns better to the CDS than alternative ORFs (left), and alignment percent identity with the CDS (right).