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HolobiomicsLab

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1. ▌
   Eic Signal Peak Detection 2 · holobiomicslab

   Use when after EIC candidate generation from LC/HRMS data (mzXML, mzML, or netCDF formats), when you need to localize discrete peaks within chromatographic profiles and assign retention time boundaries, apex intensities, and quality scores prior to peak annotation or cross-sample alignment.
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   Metadata Field Validation 2 · holobiomicslab

   Use when you have received new or updated MassBank records (in plain-text or structured format) that must be integrated into the MassBank-data repository and you need to ensure they conform to the MassBank format specification before acceptance.
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   Dimple Pipeline Execution 2 · holobiomicslab

   Use when when you have deposited mass spectrometry imaging data in NetCDF (CDF) format paired with MATLAB workspace files (.
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   Image Intensity Jittering 2 · holobiomicslab

   Use when when preparing ion images (single-channel 2D arrays or multi-channel spectral images) from mass spectrometry imaging for contrastive learning in DeepION's COL or ISO modes.
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5. ▌
   Spatial Feature Embedding 2 · holobiomicslab

   Use when when analyzing imaging mass spectrometry datasets where you need to reduce high-dimensional peak intensity features while preserving spatial structure, and when automatic peak picking and marker ion identification are required.
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   Lipid Identification Scoring 2 · holobiomicslab

   Use when after peak picking has generated a peaklist of experimental fragment m/z values (from Q-Exactive orbitrap, Agilent/Bruker/SCIEX Q-TOF UHPLC-HRMS/MS, or direct infusion/imaging experiments) and you need to compare those fragments against the LipidMatch in-silico fragmentation library.
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7. ▌
   M Z Alignment Across Samples 2 · holobiomicslab

   Use when after mass track construction for individual samples, when you need to establish consensus m/z values across a cohort of LC-MS samples to build a unified feature table.
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8. ▌
   Ms Ms Spectrum Preprocessing 2 · holobiomicslab

   Use when you have raw MS/MS spectra (in MGF or mzML format) with unscaled peak intensities and noise artifacts, and you plan to rank chemical formulas, predict adducts, or score precursor–spectrum agreement using a machine learning model such as MIST-CF.
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9. ▌
   Candidate Metabolite Ranking 2 · holobiomicslab

   Use when when you have generated a set of predicted metabolite structures from BioTransformer's metabolism prediction engine and need to assign identity to observed compounds from LC-MS/MS, spectral, or chromatographic experiments.
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10. ▌
    Cohort Performance Reporting 2 · holobiomicslab

    Use when you have NMR metabolite measurements from peripheral blood samples (plasma/serum) paired with processing delay metadata (pre-centrifugation and post-centrifugation times) and need to benchmark metabolic parameter stability across delay windows.
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11. ▌
    Command Line Tool Invocation 2 · holobiomicslab

    Use when you need to bootstrap a tool workflow by generating a version- or instrument-specific default configuration file (e.g., for MS-DIAL 4 vs. 5), execute an analysis on formatted input files (e.g., MS-DIAL export .txt files), or capture tool output for downstream validation.
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12. ▌
    Dataset Integrity Assessment 2 · holobiomicslab

    Use when when you have downloaded a released version of a structured dataset (e.g., LOTUS from Zenodo) and need to confirm it matches the documented headline statistics before downstream analysis, or when auditing data integrity after ingestion into a processing pipeline.
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13. ▌
    Exact Mass Database Matching 2 · holobiomicslab

    Use when after feature detection and alignment on raw MS data, when you have a list of unknown feature m/z values and need to assign them to known xenobiotic metabolites or their predicted biotransformation products.
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14. ▌
    Gnps Library Format Assembly 2 · holobiomicslab

    Use when you have extracted MS1 and MS2 scans (in mzML/mzXML format) from raw chromatogram files and possess user-provided metadata (retention time, m/z, compound name, molecular weight, annotation fields) that must be combined into a single structured library entry suitable for spectral library.
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15. ▌
    Hybrid Model Fusion Strategy 2 · holobiomicslab

    Use when you have 1H NMR spectral data from complex mixtures and need to identify component compounds, but a single architecture (CNN or Transformer alone) fails to capture both fine local patterns in peak structures and long-range dependencies across the full spectral range.
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16. ▌
    Linux Command Line Execution 2 · holobiomicslab

    Use when you have vendor-specific raw mass spectrometry data (ThermoFisher .raw, Agilent .
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17. ▌
    Lipid A Structure Annotation 2 · holobiomicslab

    Use when when you have high-resolution tandem mass spectrometry (MS2) data in .ms2 format and need to identify and annotate lipid A structures at scale.
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18. ▌
    M Z Alignment Across Samples 3 · holobiomicslab

    Use when you have extracted mass tracks (EICs) from multiple LC-MS samples at 0.001 amu resolution and need to construct a sample-agnostic m/z reference frame.
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19. ▌
    Mass Feature To Node Mapping 2 · holobiomicslab

    Use when you have an untargeted metabolomics feature table with m/z values, retention times, and intensity measurements, a metabolic network representation with compound nodes and chemical formulas, and you want to infer functional pathway activity directly from features without performing.
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20. ▌
    Metabolite Spectral Matching 2 · holobiomicslab

    Use when you have an experimental mass spectrum (or a set of spectra from LC-MS/MS data) and need to identify the underlying metabolite(s) by comparing against known reference spectra in GNPS or a local indexed repository.
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21. ▌
    Ms Ms Spectrum Preprocessing 3 · holobiomicslab

    Use when you have raw or semi-processed MS/MS spectral data from bottom-up tandem mass spectrometry experiments (data-dependent acquisition) that you intend to input to de novo peptide sequencing tools like Casanovo.
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22. ▌
    Network Node Label Spreading 2 · holobiomicslab

    Use when you have an untargeted metabolomics dataset with a two-layer network topology already constructed (one layer representing biochemical knowledge/pathways, the other representing data-driven MS2 similarity), seed metabolites with reliable annotations from database matching or curation, and.
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23. ▌
    Pathway Database Integration 2 · holobiomicslab

    Use when you have intensity measurements (peak features, protein intensities, or gene expression values) with compound or gene annotations (KEGG IDs, ChEBI IDs, UniProt IDs, or ENSEMBL IDs), and you need to aggregate them into biologically meaningful pathway groups for differential analysis.
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24. ▌
    Precursor Product Mz Parsing 2 · holobiomicslab

    Use when you have raw MRM sample files from a LC-MS/MS instrument and need to systematically recover all precursor m/z and product m/z pairs for each transition. Use this as an initial parsing step before quantitation, method optimization, or transition verification workflows.
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25. ▌
    Qc Sample Quality Assessment 2 · holobiomicslab

    Use when after drift correction and before imputation when you have LC-MS data with designated QC samples and you need to remove features with poor reproducibility across QC replicates.
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26. ▌
    R Package Function Execution 2 · holobiomicslab

    Use when you have raw Bruker NMR spectral data files (1D 1H format) stored in a directory structure and need to prepare them for automated metabolite identification and quantification in ASICS.
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27. ▌
    Retention Time Peak Matching 2 · holobiomicslab

    Use when after drift correction and quality flagging, when you have a feature abundance matrix with associated metadata (Feature_ID, m/z, retention time) and need to identify which features likely represent the same underlying metabolite or adduct series before statistical analysis or metabolite.
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28. ▌
    Simulation Output Validation 2 · holobiomicslab

    Use when after executing a multi-stage simulation workflow in R and/or MATLAB, when you have generated intermediate and final outputs (tables, figures, model objects) and need to confirm that all artifacts conform to the documented format, structure, and expected content before downstream analysis.
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29. ▌
    Spectral Mz Window Filtering 2 · holobiomicslab

    Use when when you have resolved mzML or mzXML spectrum files and need to isolate signals for a target m/z value (e.g., 870.954) across all retention times or a specific scan.
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30. ▌
    Sterol Isomer Classification 2 · holobiomicslab

    Use when you have LC-IM-MS/MS raw data from sterol-containing tissue samples and need to assign detected peaks to specific structural isomers (e.g., distinct double bond positions or saturation patterns in C27–C29 sterols).
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31. ▌
    Stocsy Metabolite Assignment 2 · holobiomicslab

    Use when use STOCSY when you have preprocessed 1H NMR spectral data with an unidentified peak of interest (driver signal at a specific δ ppm value) and need to determine its metabolite identity by finding correlated signals across the spectrum.
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32. ▌
    Two Layer Topology Traversal 2 · holobiomicslab

    Use when you have an untargeted metabolomics dataset with partial metabolite annotations (from database matching or prior curation) and need to extend annotation coverage to unannotated metabolites.
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33. ▌
    YAML JSON Structural Parsing 2 · holobiomicslab

    Use when you have a versioned workflow definition file (YAML or JSON) from a specific release commit and need to verify it conforms to the project's schema specification, validate the presence of all required metadata fields (name, version, inputs, outputs, steps), and detect syntax errors or.
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34. ▌
    Expert Review Preparation 2 · holobiomicslab

    Use when when a paper describes a computational or statistical method and you need to verify that claims are supported by available code, data, or documentation before human expert evaluation.
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35. ▌
    Cross Domain Token Mapping 2 · holobiomicslab

    Use when when building a unified sequence model (e.
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36. ▌
    Spectral Metadata Grouping 2 · holobiomicslab

    Use when processing a mass spectrometry dataset (in FragHub JSON format or similar) where duplicate spectral records are suspected or known to exist. The input dataset should already be in a standardized format with computed or retrievable SPLASH keys.
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37. ▌
    Validator Tool Integration 2 · holobiomicslab

    Use when you have a repository of structured records (e.g., mass spectrometry data, metadata, or domain-specific formats) and need to enforce validation rules systematically across all records.
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38. ▌
    Python Environment Pinning 2 · holobiomicslab

    Use when when you have access to a research repository or README documenting a machine learning implementation (e.g., Keras/TensorFlow-based deep learning model) and need to reproduce the computational environment exactly. Triggers include: (1) README explicitly lists pinned versions (e.
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39. ▌
    Nps Classification Prediction 2 · holobiomicslab

    Use when you have acquired a mass spectrum from an unknown suspected illicit drug analyte and need to compare it against a synthetic NPS database to rank candidate identities by similarity.
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40. ▌
    Adduct Mass Offset Assignment 2 · holobiomicslab

    Use when when you have an LC-MS feature table with m/z and retention time columns and need to identify which observed ions correspond to the same neutral compound under different ionization conditions and isotopic enrichment.
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41. ▌
    Adduct Regex Pattern Matching 2 · holobiomicslab

    Use when ingesting mass spectrometry spectra from heterogeneous databases or libraries where adduct annotations may be incomplete, incorrectly formatted, or inconsistent with the ionization mode. Use it before downstream analysis (e.
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42. ▌
    Annotation Confidence Scoring 2 · holobiomicslab

    Use when after recursive annotation propagation has assigned metabolite labels to previously unannotated nodes in a two-layer metabolomic network, and before reporting final annotated metabolite identities.
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43. ▌
    Binary Additive Flag Encoding 2 · holobiomicslab

    Use when constructing HPLC column feature vectors from raw metadata that includes additive composition flags (e.g., presence/absence or concentration of formic acid, acetic acid, TFA, or phosphoric acid in mobile phase eluents A and B).
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44. ▌
    Cosine Similarity Computation 2 · holobiomicslab

    Use when when comparing two MS/MS spectra (query and reference) to quantify their spectral resemblance for compound identification or molecular networking, particularly when you need a simple, symmetric measure that is insensitive to precursor mass differences and does not require peak alignment.
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45. ▌
    Cross View Similarity Scoring 2 · holobiomicslab

    Use when you have an experimental mass spectrum (query) and a set of molecular candidate structures, and you need to rank the candidates by how well their predicted spectral features match the query spectrum.
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46. ▌
    Database Metadata Enumeration 2 · holobiomicslab

    Use when when you have downloaded a curated structure-organism dataset (such as LOTUS) and need to verify the reported counts of unique entities (source databases, organisms, structures, and their pairs) to confirm dataset integrity, assess data coverage, or reproduce published statistics in a.
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47. ▌
    Dda Acquisition Data Handling 2 · holobiomicslab

    Use when you have raw or processed LC-MS/MS data from DDA mode acquisitions and need to extract, annotate, and structure MS/MS spectra with purity labels (or quality indicators) to serve as input to the DNMS2Purifier customized model training workflow, or to prepare data for purification of.
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48. ▌
    Decision Tree Path Extraction 2 · holobiomicslab

    Use when you have a trained shallow decision tree on ChemEcho feature vectors (sparse, high-dimensional representations of tandem mass spectra peaks and neutral losses) and need to convert it into an interpretable, deployable query for a domain-specific language like MassQL.
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49. ▌
    Deep Learning Model Inference 2 · holobiomicslab

    Use when you have preprocessed mass spectrometry spectra (tokenized m/z and intensity pairs or feature matrices) and a trained deep learning model checkpoint, and you need to classify unknown compounds or generate prediction confidence scores for structural novelty analysis.
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50. ▌
    Eic Data Extraction From Xcms 2 · holobiomicslab

    Use when after running XCMS getEIC() to generate xcmsEIC objects and fillPeaks() to produce a filled xcmsSet object, before computing the 12 peak-quality metrics (Apex Max-Boundary Ratio, Elution Shift, FWHM2Base, Jaggedness, Modality, Symmetry, Sharpness, Gaussian Similarity, Retention-Time.
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51. ▌
    Feature Identifier Assignment 2 · holobiomicslab

    Use when after constructing MetaboSet objects from Excel-formatted LC-MS peak tables and before drift correction or quality flagging.
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52. ▌
    Flat File Parsing And Loading 2 · holobiomicslab

    Use when when you have published LOTUS flat files (TSV or compressed TSV.GZ) containing structure-organism pairs and need to enumerate unique structures, group by organism prevalence, or validate record counts against gold-standard benchmarks.
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53. ▌
    Fragment Ion Mass Calibration 2 · holobiomicslab

    Use when when comparing experimental spectra to reference library spectra and fragment ion m/z values show systematic drift or measurement noise that could distort neutral loss peaks or cosine similarity scores.
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54. ▌
    Gradient Performance Encoding 2 · holobiomicslab

    Use when when you have extracted retention times from the top detected MS1 features in a LC-MS run and need to evaluate whether the gradient spreads those compounds efficiently across the available chromatographic time window—particularly during iterative gradient optimization where you need a.
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55. ▌
    In Silico Fragment Prediction 2 · holobiomicslab

    Use when you have a collection of compound structures in SDF format (e.g., DNA adduct structures) and need to systematically generate predicted fragment spectra across a defined ionization level and mass range to populate a reference spectral database or validate experimental fragmentation patterns.
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56. ▌
    Ion Image Augmentation Design 2 · holobiomicslab

    Use when when preparing ion image data from mass spectrometry imaging for contrastive self-supervised representation learning, and you need to generate augmented image pairs that reflect either co-localization relationships between different molecular ions (COL mode) or isotopic relationships.
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57. ▌
    Lc Ms Feature Quality Scoring 2 · holobiomicslab

    Use when immediately after peak detection and feature table generation from LC-MS data, when you need to rank or filter features by confidence before annotation or statistical analysis.
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58. ▌
    Metabolite Annotation Scoring 2 · holobiomicslab

    Use when you have a feature table with candidate metabolite annotations (m/z, retention time, chemical identifiers) from MS/MS spectra or external tools (SIRIUS, GNPS), sample metadata linking samples to organisms, and you need to prioritize candidates by both annotation quality AND biological.
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59. ▌
    Metaboset Object Manipulation 2 · holobiomicslab

    Use when when you have read LC-MS peak table data from Excel (or equivalent) into R and need to organize it into a structured object that tracks feature abundances, sample information (injection order, QC status), and feature metadata (mass, retention time, Feature_ID) simultaneously.
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60. ▌
    Molecular Formula Calculation 2 · holobiomicslab

    Use when you have user-specified lipid class constraints (e.
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61. ▌
    Ms2 Annotation Interpretation 2 · holobiomicslab

    Use when after GNPS spectral library search has returned matched chemical annotations (with m/z values and cosine similarity scores) for MS/MS spectra.
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62. ▌
    Multi Platform Ms Integration 2 · holobiomicslab

    Use when you have untargeted metabolomics data from multiple MS instruments (e.
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63. ▌
    Mzml File Parsing And Loading 2 · holobiomicslab

    Use when when you have centroided mzML format LC–MS files from multiple runs (e.
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64. ▌
    Neural Network Model Training 2 · holobiomicslab

    Use when you have downloaded LC-MS spectral peak data (DOI 10.25345/C5FD2F or equivalent) and need to build a supervised deep neural network classifier to distinguish peak classes in mass spectrometry data.
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65. ▌
    Nps Classification Prediction 3 · holobiomicslab

    Use when you have an unknown mass spectrum from a suspicious analyte and need to determine whether it matches a known NPS or a derivative thereof. The analyte's mass spectrum is available in MSP or equivalent format, and you have a core drug structure to enumerate derivatives from.
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66. ▌
    Pre Trained Model Fine Tuning 2 · holobiomicslab

    Use when you have a small training dataset for molecular property prediction (e.g., <500 samples from PredRet or MoNA databases) and a pre-trained GNN model is available that was trained on a related, larger molecular corpus.
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67. ▌
    Precursor M Z Based Filtering 2 · holobiomicslab

    Use when you have an unknown MS/MS query spectrum with a known or measured precursor m/z value and need to search a spectral library (local or public: GNPS, MASSBANK, DrugBANK) to annotate the compound.
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68. ▌
    Qc Sample Type Classification 2 · holobiomicslab

    Use when when constructing a sample list from an Excel template for LC/GC-MS analysis, you must classify each QC sample by type before proceeding to plate layout and randomization steps.
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69. ▌
    Quality Metrics Summarization 2 · holobiomicslab

    Use when after running QC analysis on NMR or MS metabolomic data and obtaining per-feature CV values, use this skill to validate that the dataset meets FDA thresholds (CV < 0.30 for discovery, CV < 0.15 for quantification) and to report the proportion of features meeting each threshold.
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70. ▌
    Raw Ms Data Format Conversion 2 · holobiomicslab

    Use when you have raw UPLC-HRMS data from ThermoFisher or Agilent instruments and need to feed it into MSThunder for nontargeted pollutant identification. Your input is a vendor binary format (.raw or .d) that MSThunder cannot directly ingest. Environment constraints (e.
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71. ▌
    Smiles Canonicalization Rdkit 2 · holobiomicslab

    Use when when processing raw SMILES strings from external databases or user input that may contain non-canonical tautomeric forms, variable stereochemical notation, or redundant representations of the same chemical structure.
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72. ▌
    Smiles Parsing And Validation 2 · holobiomicslab

    Use when you have SMILES strings for candidate novel psychoactive substance structures and need to convert them into a machine-readable molecular representation before computing descriptors, generating mass spectra, or calculating chemical fingerprints.
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73. ▌
    Spectral Embedding Generation 2 · holobiomicslab

    Use when you have a collection of pre-processed MS/MS spectra (binned, intensity-normalized) and a trained MS2DeepScore base network, and you need to compute structural similarity scores between spectrum pairs or visualize spectra in chemical space via dimensionality reduction (e.g., UMAP).
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74. ▌
    Tensor Encoding Deep Learning 2 · holobiomicslab

    Use when when you have validated SMILES strings or RDKit molecule objects representing chemical structures and need to feed them into a pre-trained deep learning model (such as PS2MS, NEIMS, or DeepEI) that expects fixed-size numerical tensor inputs.
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75. ▌
    C Python Interface Wrapping 2 · holobiomicslab

    Use when you have a mature C++ library (like OpenMS) with stable APIs that you want to make accessible from Python environments, and you need to preserve performance-critical C++ execution while supporting rapid prototyping or integration into Python-based data pipelines (e.
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76. ▌
    Dna Adduct Characterization 2 · holobiomicslab

    Use when when you have a collection of DNA adduct compound structures in SDF format that requires validation for structural integrity and completeness, and you need to generate predicted fragment spectra at defined ionization levels and mass ranges for comparison against experimental mass.
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77. ▌
    Local Maxima Identification 2 · holobiomicslab

    Use when you have raw LC-HRMS profile-mode data and need to identify candidate chromatographic peaks before classification or feature extraction.
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78. ▌
    Openms API Surface Exposure 2 · holobiomicslab

    Use when when you need to make OpenMS C++ classes, functions, or data structures callable from Python code, or when verifying that a newly bound C++ component can be imported and instantiated without errors in a Python environment.
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79. ▌
    Runtime Comparison Analysis 2 · holobiomicslab

    Use when when a new version or variant of a tool claims performance improvements over a prior version (e.g., MASST+ vs. MASST), and you need empirical evidence that the claimed speedup (e.g., ~100-fold reduction in search time) is real, reproducible, and quantifiable.
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80. ▌
    Schema Conformance Checking 2 · holobiomicslab

    Use when you have a collection of records in a standardized format (e.g., MassBank plain-text or structured records) that must be validated before commit or publication.
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81. ▌
    Semantic Metabolite Ranking 2 · holobiomicslab

    Use when you have an unknown metabolite with unknown mass spectrum and need to prioritize structural candidates from databases (PubChem, HMDB) by their likelihood of being the true compound.
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82. ▌
    Contrastive Pair Generation 2 · holobiomicslab

    Use when you have raw ion images from MSI data and need to train a contrastive encoder to learn stable, mode-specific representations.
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83. ▌
    Ppm Mass Accuracy Filtering 2 · holobiomicslab

    Use when when processing imzML/ibd Imaging Mass Spectrometry datasets and you need to extract ion density maps for specific analytes or isotopes. Apply this skill after importing the .imzML metadata and .
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84. ▌
    Mass Spectrometry Data Parsing 2 · holobiomicslab

    Use when you have raw mass spectrometry files in standard formats (mzML, mzXML, msp, MGF, JSON) and need to extract precursor m/z values, fragment peaks, neutral losses, retention times, and compound metadata into a structured, queryable spectrum object representation before performing MS/MS.
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85. ▌
    4d Lcimmsms Feature Extraction 2 · holobiomicslab

    Use when you have raw LC-IM-MS/MS data files from sterol lipid analysis and need to identify unsaturated sterol isomers by matching experimental collision cross section values against a quantum chemistry calculation-assisted CCS prediction database.
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86. ▌
    Artifact Checksum Verification 2 · holobiomicslab

    Use when when reproducing a prior software release (especially one generated by automated versioning tools like Semantic Release), you need to confirm that the artifacts produced in your environment match the original release byte-for-byte.
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87. ▌
    Attention Mechanism Validation 2 · holobiomicslab

    Use when after instantiating a transformer encoder module for mass spectrometry data processing (e.g., in IDSL_MINT), before training on large MS/MS datasets or running inference on test spectra.
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88. ▌
    Automated Lipid Identification 2 · holobiomicslab

    Use when you have high-resolution tandem mass spectrometry (MS2) data in .ms2 format and need to systematically identify and annotate lipid A molecular structures.
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89. ▌
    Compound Ground Truth Matching 2 · holobiomicslab

    Use when when you have pre-computed embeddings for query and reference MS/MS spectra, computed their cosine similarity matrix, and need to measure retrieval success by verifying whether the correct compound (identified by SMILES string) appears in the top-1, top-5, or top-10 ranked candidates from.
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90. ▌
    Cross Split Metric Aggregation 2 · holobiomicslab

    Use when when you have a pre-trained model and need to report stable, generalizable performance on a fixed training set with multiple held-out test splits. Specifically: when you have 10 (or n) random query/reference splits on the same dataset (e.
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91. ▌
    Docker Container Orchestration 2 · holobiomicslab

    Use when your analysis requires msconvert or another ProteoWizard tool on macOS, but native installation is infeasible or licensing-restricted. You need to convert vendor raw mass spectrometry files (.raw) to the open mzML format without installing ProteoWizard directly on your system.
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92. ▌
    Feature Group Adduct Detection 2 · holobiomicslab

    Use when you have a feature table from LC-MS analysis (containing m/z, retention time, and intensity values) and need to identify which detected features represent the same molecular species ionized under different adduction states.
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93. ▌
    File Format Robustness Testing 2 · holobiomicslab

    Use when when processing MS spectral data from multiple open mass spectra libraries (OMSLs) in mixed formats (MSP, MGF, JSON, CSV), especially when source data exhibits missing fields, malformed entries, inconsistent adduct representations, or non-standard format variants that may cause silent.
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94. ▌
    Frequency Distribution Binning 2 · holobiomicslab

    Use when you have loaded a table of entity–attribute pairs (e.
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95. ▌
    Gaussian Peak Shape Evaluation 2 · holobiomicslab

    Use when after peak detection on a composite mass track has identified candidate peaks in a mass chromatogram, and before compiling the final feature table.
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96. ▌
    HTTP Connectivity Verification 2 · holobiomicslab

    Use when you need to confirm that a documented web service URL is live and reachable before attempting to submit analysis jobs, download results, or integrate the service into an automated pipeline. Use it as a prerequisite check when the service documentation claims academic or public availability.
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97. ▌
    Imputation Algorithm Selection 2 · holobiomicslab

    Use when you have a metabolomics dataset with left-censored missing values (e.g., below limit of quantification in LC/MS or GC/MS) and need to evaluate multiple imputation approaches.
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98. ▌
    Interpretable Machine Learning 2 · holobiomicslab

    Use when when you have tandem mass spectra data and need to predict a binary molecular property (e.g., presence of a functional group like a sulfo group) while maintaining full interpretability of the model's decision logic.
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99. ▌
    Mass Spectral Feature Grouping 2 · holobiomicslab

    Use when you have untargeted metabolomics MS/MS spectra from multiple features and need to identify which features belong to the same molecular family or are related by biotransformation.
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100. ▌
     Mass Spectrometry Data Parsing 3 · holobiomicslab

     Use when you have received raw or vendor-converted centroid mzML files from LC-MS, GC-MS, or DI-MS platforms and need to extract MS1 spectra before building mass tracks, performing peak detection, or constructing composite feature maps.
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