NeuroCDS: Integrating Local and Global Neural Network Representations via Structural Constrained Viterbi Decoding for Robust CDS Annotation (opens in new tab)

Motivation: Robust annotation of Coding Sequences (CDS) is critical for downstream transcriptomics, yet heavily fragmented de novo RNA-Seq assemblies pose a severe challenge. Traditional computational tools rely on fixed, hand-crafted features that are prone to fail when canonical sequence signals are truncated. While recent deep learning models excel at automatically extracting complex representations, they predominantly treat these as isolated prediction tasks. Lacking a joint inference mec...