ContraNovo: A Contrastive Learning Approach to Enhance De Novo Peptide Sequencing
Abstract
De novo peptide sequencing from mass spectrometry (MS) data is a critical task in proteomics research. Traditional de novo algorithms have encountered a bottleneck in accuracy due to the inherent complexity of proteomics data. While deep learning-based methods have shown progress, they reduce the problem to a translation task, potentially overlooking critical nuances between spectra and peptides. In our research, we present ContraNovo, a pioneering algorithm that leverages contrastive learning to extract the relationship between spectra and peptides and incorporates the mass information into peptide decoding, aiming to address these intricacies more efficiently. Through rigorous evaluations on two benchmark datasets, ContraNovo consistently outshines contemporary state-of-the-art solutions, underscoring its promising potential in enhancing de novo peptide sequencing.
Cite
Text
Jin et al. "ContraNovo: A Contrastive Learning Approach to Enhance De Novo Peptide Sequencing." AAAI Conference on Artificial Intelligence, 2024. doi:10.1609/AAAI.V38I1.27765Markdown
[Jin et al. "ContraNovo: A Contrastive Learning Approach to Enhance De Novo Peptide Sequencing." AAAI Conference on Artificial Intelligence, 2024.](https://mlanthology.org/aaai/2024/jin2024aaai-contranovo/) doi:10.1609/AAAI.V38I1.27765BibTeX
@inproceedings{jin2024aaai-contranovo,
title = {{ContraNovo: A Contrastive Learning Approach to Enhance De Novo Peptide Sequencing}},
author = {Jin, Zhi and Xu, Sheng and Zhang, Xiang and Ling, Tianze and Dong, Nanqing and Ouyang, Wanli and Gao, Zhiqiang and Chang, Cheng and Sun, Siqi},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2024},
pages = {144-152},
doi = {10.1609/AAAI.V38I1.27765},
url = {https://mlanthology.org/aaai/2024/jin2024aaai-contranovo/}
}