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from Agentic Science Builder Collections10
Convert LC gradient parameters into normalized numerical vectors for Bayesian optimization and GP regression.
Enables the bidirectional transformation between human-readable Liquid Chromatography (LC) gradient parameters (such as flow rate, solvent composition, and temperature) and normalized numerical vectors. This is critical for applying machine learning techniques like Gaussian Process regression to optimize chemical separations.
Use this skill when you are designing an experimental space for LC-MS analysis and need to represent physical instrument parameters as scaled input vectors (0-1 range) for a Bayesian Optimization (BO) framework, or when you need to decode a proposed optimal vector back into actionable instrument settings.
StandardScaler from scikit-learn to normalize parameters and an integration path for the BAGO framework.Scientific AI Agents, Data Science assistants with Python environment (scikit-learn)
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GitHub Actions Workflow Inspector
Verify and locally reproduce GitHub Actions CI workflows to validate badge status and build integrity.
Injection Sequence Annotation
Identifies and validates QCpool sample spacing in Sciex Multiquant metabolomics/lipidomics exports.
PyTorch Pretrained Weight Loader
Load pretrained PyTorch model checkpoints to rank candidate molecules or score spectra in metabolomics without retraining.
Positional Encoding for Spectral Sequences
Embeds sequential information into mass spectrum m/z-intensity pairs for transformer-based MS/MS interpretation in IDSL_MINT.
Record Field Transformation & Collation
Transform and collate tabular records into structured JSON dictionaries using field mapping, filtering, and grouping for scientific metadata.
Fragmentation Spectrum Extraction
Extract and deconvolve individual fragmentation spectra from composite mass spectrometry data for metabolomics and exposomics annotation.
Metabolomics QC Report Generation
Automates the export of LC-MS metabolomics quality control results into publication-ready plots, HTML reports, and data tables.
Proteomics Spectral Quality Assurance
Validate Orbitrap mass spectrometry data by verifying instrument metadata and quantifying fragment ion signal-to-noise ratios.
GNPS Molecular Network Metadata Organization
Fetch, extract, and standardize GNPS molecular networking job archives for NPLinker integration.
Metabolomic Feature Table Imputation
Replace zero and missing values in LC-MS metabolomics feature tables using minimum-value-based interpolation.
MatchMS (mass spectrometry processing)
Mass spectrometry data processing: import/export mzML/MGF/MSP, filter and normalize spectra, compute spectral similarity (cosine/modified cosine), and build rep
matchms — Mass Spectrometry Processing
Python library skill for importing, filtering, and comparing mass spectra (mzML/MGF/MSP); builds reproducible preprocessing pipelines and computes spectral simi
MatchMS — Mass Spectrometry Utilities
Import, process and compare mass spectrometry spectra (mzML/MGF/MSP) with filters, similarity metrics and reproducible pipelines for metabolomics workflows.
MatchMS
Python library for mass spectrometry data processing: import/export mzML/MGF/MSP, filter and harmonize metadata, compute spectral similarity (cosine & modified
ChEMBL Database Explorer
Query ChEMBL programmatically to retrieve bioactive molecule data, bioactivity measurements, targets, and perform similarity/substructure searches for drug disc
MS-DIAL metabolomics preprocessing
Headless MS-DIAL preprocessing for LC/GC workflows: peak picking, MS2Dec deconvolution, alignment and honest feature-table import into R or Python.
Injection Sequence Annotation
Identifies and validates QCpool sample spacing in Sciex Multiquant metabolomics/lipidomics exports.
PyTorch Pretrained Weight Loader
Load pretrained PyTorch model checkpoints to rank candidate molecules or score spectra in metabolomics without retraining.