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Embeds sequential information into mass spectrum m/z-intensity pairs for transformer-based MS/MS interpretation in IDSL_MINT.
This skill implements positional encoding to embed ordinal and sequential information into mass spectrum data. This allows transformer self-attention layers to distinguish the position of peaks in a spectrum, which is critical for accurate MS/MS interpretation.
Use this skill specifically when preparing mass spectrum input tensors for transformer encoder layers within the IDSL_MINT framework. It should be applied after vectorizing m/z and intensity features but before the multi-head self-attention blocks.
Scientific AI agents equipped with PyTorch and RDKit, specifically those targeted at bioinformatics, cheminformatics, and metabolomics research.
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GitHub Actions Workflow Inspector
Verify and locally reproduce GitHub Actions CI workflows to validate badge status and build integrity.
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