# Spec2vec Ml Embedding Annotation Workflow

> Use when you want to annotate untargeted MS2 spectra with a machine-learned spectral embedding instead of raw cosine scoring — convert spectra into peak/neutral-loss "documents", train or load a Spec2Vec (Word2Vec-style) embedding model on a reference corpus, embed query spectra into that learned vector space, and retrieve/annotate nearest library neighbours by embedding similarity, as a deep-learning-adjacent second pass beyond modified-cosine matching.

- Skill: `holobiomicslab/spec2vec-ml-embedding-annotation-workflow` (Agent Skill, multi-file: 3 files)
- Install (CLI): `npx skillmds@latest add holobiomicslab/spec2vec-ml-embedding-annotation-workflow`
- Raw SKILL.md: https://api.skillmd.com/api/skills/holobiomicslab/spec2vec-ml-embedding-annotation-workflow/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- License: CC-BY-4.0
- Author: HolobiomicsLab (https://skillmd.com/u/holobiomicslab)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/holobiomicslab/spec2vec-ml-embedding-annotation-workflow

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# Spec2Vec / ML Spectral-Embedding Annotation (peak-loss documents -> Word2Vec embedding -> nearest-neighbour ID)

## Summary

MS2 in, an embedding-based annotation table out: peak/loss document conversion, Spec2Vec (Word2Vec) model training or loading, query-spectrum embedding, and nearest-neighbour library retrieval and annotation.


## When to use

Use when you want to annotate untargeted MS2 spectra with a machine-learned spectral embedding instead of raw cosine scoring — convert spectra into peak/neutral-loss "documents", train or load a Spec2Vec (Word2Vec-style) embedding model on a reference corpus, embed query spectra into that learned vector space, and retrieve/annotate nearest library neighbours by embedding similarity, as a deep-learning-adjacent second pass beyond modified-cosine matching.


## When NOT to use

- The data is not LC-MS, GC-MS.
- You need a single atomic step, not the full pipeline (use the leaf skill directly via the router).

## Stages

### Stage 1 — preprocess

**Goal:** raw MS2 spectra -> filtered, normalized spectrum objects

**EDAM operation:** operation_3630

**Inputs:** mzML · **Outputs:** mgf/sirius

**Candidate leaf skills:** `peak-filtering-and-preprocessing-lc-ms` (primary), `spectral-peak-filtering-and-normalization`, `spectral-peak-filtering`, `spectrum-normalization-and-standardization`, `ms-ms-spectrum-parsing`

**Tools (primary):** matchms, gensim, Numba, Pandas, scipy, spec2vec, Word2Vec

**Other candidate tools:** pubchempy, RDKit, Python, numpy, MS2Query, MZMine

**Grounding:** 5 KB(s); DOIs: 10.1038/s41467-023-37446-4, 10.1186/s13321-021-00558-4, 10.1186/s13321-024-00878-1, 10.1371/journal.pcbi.1008724 …

### Stage 2 — document_conversion

**Goal:** filtered spectra -> peak/neutral-loss "documents" for word-embedding training

**EDAM operation:** operation_3357

**Inputs:** mgf/sirius · **Outputs:** json/spectral-documents

**Candidate leaf skills:** `spectrum-document-conversion-peak-loss-representation` (primary), `mass-spectrometry-spectrum-tokenization`, `neutral-loss-calculation-from-precursor`

**Tools (primary):** Spec2Vec, matchms, gensim, RDKit, NumPy, Numba, Pandas, scipy, gensim (Word2Vec, CBOW, Skip-gram), NumPy, Pandas, SciPy

**Other candidate tools:** Word2Vec (gensim), Word2Vec, Python 3.8, MEMO, memo-ms, Python

**Grounding:** 2 KB(s); DOIs: 10.1371/journal.pcbi.1008724, 10.3389/fbinf.2022.842964

### Stage 3 — embed

**Goal:** peak/loss documents -> Spec2Vec Word2Vec embedding model + query-spectrum embedding vectors

**EDAM operation:** operation_3800

**Inputs:** json/spectral-documents · **Outputs:** json/spectrum-embeddings

**Candidate leaf skills:** `corpus-size-coverage-scaling-analysis` (primary), `spectral-peak-word-embedding-representation`, `missing-fraction-quality-filtering-for-embeddings`, `mass-spectral-missing-word-fraction-computation`

**Tools (primary):** Spec2Vec, matchms, gensim, NumPy, Numba, Pandas, Word2Vec, NumPy, Pandas

**Other candidate tools:** Word2Vec (gensim)

**Grounding:** 1 KB(s); DOIs: 10.1371/journal.pcbi.1008724

### Stage 4 — retrieve_annotate

**Goal:** query embeddings -> nearest-neighbour library matches -> embedding-based annotation table

**EDAM operation:** operation_3767

**Inputs:** json/spectrum-embeddings · **Outputs:** tsv

**Candidate leaf skills:** `candidate-match-retrieval` (primary), `embedding-vector-similarity-ranking`, `approximate-nearest-neighbor-search`, `spectral-similarity-scoring-computation`, `spectrum-embedding-indexing`, `embedding-similarity-computation`

**Tools (primary):** MS2Query, MS2Deepscore, Spec2Vec, MZMine, RDKit

**Other candidate tools:** hnswlib, Python, gensim, Python 3.7, matchms, NumPy, Numba, Pandas, scipy, Word2Vec (gensim), NumPy, Pandas, SciPy, Numba, Anaconda, Git, MSBERT, PyTorch

**Grounding:** 4 KB(s); DOIs: 10.1021/acs.analchem.4c02426, 10.1038/s41467-023-37446-4, 10.1038/s41467-023-39279-7, 10.1371/journal.pcbi.1008724

## Grounding

Each stage carries the `kb_slugs`/`dois` of the leaves it draws on. Ground any stage against its source paper with the collection's `/ground` command or `bin/perspicacite_kb_bind.py` (Perspicacité KB; serverless local-clone fallback).

## Verification contract

`workflow.yaml` is gradable by `asb solve-workflow` (checkpoint mode). Each stage declares typed outputs; the final stage emits the master deliverable.

## Provenance

Generated by `compose_workflows.py` (semantic binding + EDAM-aware primary selection). `derived_from_workflows` lists ASB per-paper workflows whose structure corroborated this pipeline — the eval-ablation set (SPEC §8). Staging only; promote via `release_gate.py`.