We lifted 5 functions out of this paper's own repositories and ran 3 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| beam-labs/contranovo | canonical | 3 of 5 |
| Function | Status | Where it lives |
|---|---|---|
| check_int | Ran | beam-labs/contranovo/ContraNovo/utils2.py pointer only (licence: NONE) · get_code("622baf0432bd9084") |
| generate_tgt_mask | Ran | beam-labs/contranovo/ContraNovo/components/transformers.py pointer only (licence: NONE) · get_code("3ff1f6938e69098d") |
| split_version | Ran | beam-labs/contranovo/ContraNovo/utils.py pointer only (licence: NONE) · get_code("176b9d52089adf6c") |
| listify | Not yet run | beam-labs/contranovo/ContraNovo/utils2.py pointer only (licence: NONE) · get_code("145ed062058f289a") |
| read_tensorboard_scalars | Not yet run | beam-labs/contranovo/ContraNovo/utils2.py pointer only (licence: NONE) · get_code("c734de5aaa8e9552") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
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. The source code is available at https://github.com/BEAM-Labs/ContraNovo.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2312.11584")
get_code_for_paper("2312.11584")
have("2312.11584")
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