A residual network for de novo peptide sequencing with attention mechanism
peer-reviewed · 2020 16th International Conference on Control, Automation, Robotics and Vision (ICARCV) · 2020
| Date | 2020-12-13 |
| Type | peer-reviewed |
| Venue | 2020 16th International Conference on Control, Automation, Robotics and Vision (ICARCV) |
| Publisher | IEEE |
| Contribution | algorithm |
| DOI | 10.1109/icarcv50220.2020.9305327 |
| Citations (OpenAlex) | 4 |
Abstract
De novo peptide sequencing via tandem mass spectrometry is one of the most powerful tools for identifying proteins, especially for novel sequences without any database information. Due to the incomplete fragmentation information and the high complexity of the experimental spectra, the accuracy and efficiency of de novo peptide sequencing is a considerable challenge. In this study, a novel residual network structure integrated with attention mechanism is proposed for de novo peptide sequencing, called RANovo. On one hand, the residual structure enables the network to go deeper, therefore more features can be extracted from the input data. On the other hand, attention mechanism is designed to adaptively recalibrate dynamic channel-wise information, which makes better use of the hidden features. Taking these advantages, the proposed method shows superior prediction accuracy on both amino acid level and peptide level in a series of experiments.
Methods and tools
- RANovo: A residual network with an attention mechanism for de novo peptide sequencing.