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Use when when you need to evaluate how a specific algorithm parameter (such as SearchMolecularFormulas first_hit mode) affects the quantity and quality of molecular formula assignments on a given spectrum or dataset.
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SOC 職業分類に基づく
Use when when you have a neural network or machine learning model with multiple tunable hyperparameters (layer size, regularization strength, dropout) or design choices (e.
Use when when building a visualization library that must support multiple plotting backends (e.g., matplotlib, bokeh, plotly) and multiple data types (e.g., chromatograms, spectra, peak maps) without duplicating core logic or configuration handling across backend–plot-type combinations.
Use when after peaks have been assigned to heteroatom classes (e.g., CHO, CHON, CHOS, CHOP) and you need to compare molecular composition across samples, classes, or time series.
Use when after running inference on a trained structure prediction model with one or more input modalities (1H NMR, 13C NMR, or combined), you have generated predicted molecular formulas and connectivity graphs that need to be compared against known ground truth structures.
Use when after computing a histogram of all pairwise mass differences from an MSI dataset, use this skill when you have observed mass difference peaks that may correspond to known adducts (e.g., [M+H]+, [M+Na]+, [M−H2O]+).
Use when you have a trained formula ranking model (such as MIST-CF) and want to measure the specific performance gain from incorporating multiple positive-mode adduct types (e.g., [M+H]+, [M+Na]+, [M+K]+, [M+NH4]+) instead of restricting predictions to [M+H]+ only.
| name | algorithm-parameter-comparison-analysis |
| description | Use when when you need to evaluate how a specific algorithm parameter (such as SearchMolecularFormulas first_hit mode) affects the quantity and quality of molecular formula assignments on a given spectrum or dataset. |
| license | CC-BY-4.0 |
| metadata | {"edam_operation":"http://edamontology.org/operation_3632","edam_topics":["http://edamontology.org/topic_3520","http://edamontology.org/topic_0121"],"tools":["Docker","CoreMS","pandas","numpy","matplotlib"],"techniques":["GC-MS","NMR"],"license_tier":"open"} |
| derived_from | [{"doi":"10.5281/zenodo.14009575","title":"corems"}] |
| evidence_spans | ["from corems.encapsulation.factory.parameters import MSParameters","CoreMS [section=results; evidence='from corems.encapsulation.factory.parameters import MSParameters']","import pandas as pd","pandas [section=results; evidence='import pandas as pd']","import numpy as np","numpy [section=results; evidence='import numpy as np']"] |
| claims | [] |
| provenance | {"collection":"https://w3id.org/holobiomicslab/asb-skill/collection/metabolomics/v2","assembled_by":"scripts/collect_metabolomics_collection.py","sources":[{"build":"coll_corems","doi":"10.5281/zenodo.14009575","title":"corems"}],"dedup_kept_from":"coll_corems"} |
| schema_version | 0.2.0 |
| attribution | {"generator":"AgenticScienceBuilder","original_doi":"10.5281/zenodo.14009575","all_source_dois":["10.5281/zenodo.14009575"],"zenodo_doi":"10.5281/zenodo.20794027","curators":[],"promoter":"Louis-Félix Nothias","sponsor":"CNRS & Université Côte d'Azur"} |
Systematically compare molecular formula assignment behavior by running the same algorithm with different parameter configurations (e.g., first-hit vs. all-hits modes) and contrasting assignment counts, score distributions, and quality metrics. This skill enables evidence-based parameter tuning for mass spectrometry annotation workflows.
When you need to evaluate how a specific algorithm parameter (such as SearchMolecularFormulas first_hit mode) affects the quantity and quality of molecular formula assignments on a given spectrum or dataset. Use this when the output sensitivity/specificity trade-off is unknown or when optimizing assignment behavior for a particular sample type (e.g., natural organic matter like SRFA).
Load a calibrated mass spectrum from an instrument-specific format (e.g., ESI_NEG_SRFA.d Bruker Solarix data) and a reference file (e.g., SRFA.ref). Configure the SearchMolecularFormulas algorithm with the first parameter variant (e.g., first_hit=True) and execute formula assignment, then repeat with the alternative configuration (e.g., first_hit=False) on the identical spectrum. Extract assignment counts and score distribution statistics (mean, median, std, min, max) for each mode using pandas and numpy. Generate a comparison summary table contrasting both modes side-by-side and save as CSV, allowing direct inspection of how the parameter choice alters assignment behavior—e.g., whether first-hit prioritization reduces candidate count while preserving high-confidence matches.
from corems.encapsulation.factory.parameters import MSParameters; ms = load_spectrum('ESI_NEG_SRFA.d', 'SRFA.ref'); results_first_hit = [ms.run(SearchMolecularFormulas(first_hit=True)) for s in [ms]]; results_all_hits = [ms.run(SearchMolecularFormulas(first_hit=False)) for s in [ms]]; comparison = pd.DataFrame({'mode': ['first_hit=True', 'first_hit=False'], 'assignment_count': [len(results_first_hit[0]), len(results_all_hits[0])], 'mean_score': [np.mean([x.score for x in results_first_hit[0]]), np.mean([x.score for x in results_all_hits[0]])]}); comparison.to_csv('formula_assignment_comparison.csv', index=False)