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HolobiomicsLab

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1. ▌
   Metabolomics Software Benchmarking 2 · holobiomicslab

   Use when you have completed peak picking with two or more competing tools (e.g., IDSL.IPA, MZmine 2, xcms, MS-DIAL) on the same LC/HRMS dataset(s) and need to quantify which performs better. Use this skill when tool selection claims require validation (e.g., 'IDSL.
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   Molecular Family Grouping Analysis 2 · holobiomicslab

   Use when you have untargeted metabolomics peak intensity data and spectral groupings (Molecular Families or Mass2Motifs) but lack confident chemical annotations or want to avoid pathway database dependency.
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   Ms1 Feature Ranking And Extraction 2 · holobiomicslab

   Use when when you have raw LC-MS data files and need to identify which compounds were actually detected at high abundance during a gradient run, prior to evaluating whether the gradient provided good separation across the chemical space.
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   Nmr Spectra Deep Learning Encoding 2 · holobiomicslab

   Use when when you have preprocessed 1H NMR spectral data from flavor mixtures or similar compound identification tasks, and you need to identify which compounds are present.
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   Noise Suppression Spectral Imaging 2 · holobiomicslab

   Use when your input is a raw two-dimensional MS map (m/z vs retention time) derived from chromatography–mass spectrometry data with poor signal-to-noise characteristics, and you need to discriminate individual analytes and identify marker features reliably.
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   Nv Header Structure Interpretation 2 · holobiomicslab

   Use when you have a raw NV file from NMRViewJ or compatible NMR acquisition software and need to extract header metadata before processing spectroscopic data.
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   Parameter Sharing Mechanism Design 2 · holobiomicslab

   Use when designing a contrastive learning pipeline for ion images or other data modalities where you need to process multiple augmented versions of the same input through an encoder and enforce similarity between the resulting representations.
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8. ▌
   Pathway Ranking And Prioritization 2 · holobiomicslab

   Use when after peak annotation when you have: (1) a peak intensity matrix (rows=peaks with KEGG/ChEBI/UniProt IDs, columns=samples) with group labels; (2) a pathway database (KEGG, Reactome, or user-defined metabolite sets); (3) a comparative experimental design (case vs. control groups).
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9. ▌
   Peak Picking Avoidance Ms Analysis 2 · holobiomicslab

   Use when analyzing raw 2D MS data (m/z vs. retention time maps) where conventional peak picking introduces unacceptable error rates, particularly in untargeted metabolomics or chemometrics studies requiring sensitive marker identification at trace levels (e.g., parts per billion).
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10. ▌
    Peak Selectivity Metric Evaluation 2 · holobiomicslab

    Use when when identifying landmark peaks for retention time alignment in multi-sample LC-MS metabolomics workflows.
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11. ▌
    Plant Metabolic Network Validation 2 · holobiomicslab

    Use when after community-dependent gap-filling has proposed reactions to fill metabolic gaps in individual consensus reconstructions.
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12. ▌
    Regression Error Metric Evaluation 2 · holobiomicslab

    Use when when you have trained multiple regression models (e.g., using different feature sets: descriptors-only, fingerprints-only, or combined) on the same training data and need to objectively rank their generalization performance on unseen test data.
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13. ▌
    Representation Collapse Prevention 2 · holobiomicslab

    Use when training a contrastive learning model on ion image data (mass spectrometry imaging) where augmented pairs of the same ion image must maximize similarity while different images minimize similarity.
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14. ▌
    Retrieval Metric Hit K Calculation 2 · holobiomicslab

    Use when when you have generated embeddings for query and reference MS/MS spectra, computed a cosine similarity matrix between them, and need to evaluate how often the correct compound appears in the top-1, top-5, or top-10 retrieved candidates.
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15. ▌
    Sample Capacity Constraint Setting 2 · holobiomicslab

    Use when when designing injection sequences for LC/GC-MS multi-omics experiments where you need to distribute samples across multiple plates and must account for mandatory QC sample positions (Blank QC, Solvent QC, Pooled QC, Long-Term Reference QC, and custom QC).
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16. ▌
    Spectral Adduct Ionmode Validation 2 · holobiomicslab

    Use when when processing raw or aggregated mass spectra datasets (from .mgf, .msp, .json, or .
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17. ▌
    Spectral Library Msp Serialization 2 · holobiomicslab

    Use when after generating in-memory lipid spectra (with m/z, intensity, and metadata such as lipid class, fatty acid composition, and adduct type) when you need to export those spectra as a reusable MSP-format spectral library for downstream identification tasks in Excalibur, Skyline, or NIST MS.
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18. ▌
    Spectral Data Preprocessing 2 · holobiomicslab

    Use when you have raw or processed MS spectrum data (mz/intensity pairs) from direct-injection MS (DI-MS), ASAP-MS, or other ambient ionization instruments (AI-MS, LDI-MS), and you need to identify peaks of interest, assign confidence scores, and prepare the data for database matching or species.
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19. ▌
    Low Energy Structure Selection 2 · holobiomicslab

    Use when after generating an ensemble of 3D conformers via RDKit conformation sampling, when you need to reduce the conformer set size before expensive quantum-chemical calculations (e.g., QUICK).
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20. ▌
    Quantum Input File Preparation 2 · holobiomicslab

    Use when you have a set of RDKit-generated conformers ranked by ASE-ANI single-point energies, and you need to submit the lowest-energy subset to quantum software (e.g. QUICK) for CCS-relevant electronic structure calculations.
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21. ▌
    Retention Time Range Filtering 2 · holobiomicslab

    Use when you have raw IM-MS data (Agilent MassHunter .d or UIMF format) and need to exclude early or late chromatographic regions—e.g., to skip dead volume, exclude blank runs, focus on a known analyte window, or reduce file size for faster processing.
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22. ▌
    Smiles Adduct Form Enumeration 2 · holobiomicslab

    Use when when you have SMILES structures of small organic molecules and need to predict CCS values for metabolite annotation in untargeted mass spectrometry workflows. Specifically, apply this skill when the same chemical entity may appear in multiple ionization states (e.
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23. ▌
    Structured Data Table Creation 2 · holobiomicslab

    Use when you have obtained a raw reference library file (such as the DTCCS_N2 library for U13C labeled lipids) and need to validate its structure, verify that all expected lipid entries are present, and ensure CCS values fall within physically plausible ranges (typically 50–300 Ų for small lipids).
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24. ▌
    Chromatographic Data Structuring 2 · holobiomicslab

    Use when after parsing a centroid mzML file into (m/z, scan_number, intensity) tuples, when you need to organize sparse MS1 data for efficient peak detection and cross-sample alignment. Apply this skill when high mass resolution (e.
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25. ▌
    Molecular Descriptor Computation 2 · holobiomicslab

    Use when when you have a query mass spectrum and a set of candidate molecular structures (as SMILES or 2D/3D coordinates), and you need to prepare them for cross-view similarity comparison or machine learning-based ranking.
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26. ▌
    Neural Network Module Validation 2 · holobiomicslab

    Use when after implementing a neural network component that will feed into a downstream architecture (e.g., a transformer).
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27. ▌
    Sample Replicate Pair Assessment 2 · holobiomicslab

    Use when you have high-throughput replicate measurements (e.g., mass spectrometry metabolomics) on biological replicates and need to identify which sample pairs exhibit reproducible feature signals across a threshold (typically 75% reproducibility).
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28. ▌
    Color Jitter Application Imaging 2 · holobiomicslab

    Use when preparing ion image data for representation learning in mass spectrometry imaging, specifically when you need to augment raw ion images to generate pairs of diverse views for contrastive loss training.
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29. ▌
    M Z Window Tolerance Application 2 · holobiomicslab

    Use when after parsing an imzML XML metadata file and loading the corresponding .ibd binary intensity data, when you need to isolate and visualize the spatial distribution of specific isotopes, chemical species, or mass fragments.
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30. ▌
    Marker Ion Ranking And Filtering 2 · holobiomicslab

    Use when you have extracted latent low-dimensional peak features from imaging mass spectrometry (IMS) data using a graph-attention autoencoder and need to identify a ranked subset of marker ions that represent spatial metabolomic patterns.
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31. ▌
    Poisson Noise Injection Spectral 2 · holobiomicslab

    Use when augmenting mass spectrometry ion images for contrastive learning, specifically when you need to simulate the natural Poisson noise that arises from photon-counting detectors in mass spectrometry imaging experiments.
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32. ▌
    Seurat Assay Metadata Extraction 2 · holobiomicslab

    Use when you have a SpaMTP Seurat object with a 'Spatial' assay containing metabolomics features (m/z values) and their associated metadata columns (e.
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33. ▌
    Substructure Annotation Integration 2 · holobiomicslab

    Use when you have created a GNPS molecular network (either classical or feature-based) and have computed MS2LDA motif assignments (probability and overlap scores) for the same spectra.
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34. ▌
    Adduct Ion Prediction And Filtering 2 · holobiomicslab

    Use when when annotating m/z features against a metabolite database (HMDB, Lipidmaps, etc.) and the sample preparation, ionization method, or polarity mode favors specific adduct species. For example: negative-mode LC-MS or MS imaging will preferentially generate M-H and halide adducts (M+Cl);
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35. ▌
    Adduct Ionmode Consistency Checking 2 · holobiomicslab

    Use when parsing, standardizing, or filtering MS spectra from mixed or heterogeneous databases where adduct assignment may be manually entered, auto-inferred, or missing.
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36. ▌
    Biochemical Transformation Matching 2 · holobiomicslab

    Use when after you have detected and assigned molecular formulas to peaks in a single FT-ICR MS sample, and you want to infer which biochemical or abiotic reactions are occurring by examining pairwise mass differences.
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37. ▌
    Collision Cross Section Computation 2 · holobiomicslab

    Use when you have a set of molecular structures in SMILES format that require CCS prediction for metabolite annotation in untargeted mass spectrometry workflows.
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38. ▌
    Cross Domain Metadata Harmonization 2 · holobiomicslab

    Use when when you have submitted the same MS/MS spectrum query to multiple domain-specific MASST tools and need to compare matches, combine ranked results, or generate cross-domain summary statistics.
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39. ▌
    Cross Reference Publication Linking 2 · holobiomicslab

    Use when when cataloging a suite of related bioinformatics tools or web applications (particularly in domains like metabolomics, microbiology, or systems biology) and you need to establish the authoritative peer-reviewed or preprint publication for each tool, verify publication URLs are live, and.
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40. ▌
    Deep Learning Metabolite Annotation 2 · holobiomicslab

    Use when you have UPLC-HRMS data (ThermoFisher, Agilent, or MSConvert-compatible format) from a water sample, a precursor m/z and retention time of interest, and want to annotate an unknown compound by predicting its molecular formula, structure, and name using deep learning scoring rather than.
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41. ▌
    Excel File Parsing For Metabolomics 2 · holobiomicslab

    Use when you have a preprocessed LC-MS peak table exported from peak-picking software (e.g., MS-DIAL) in Excel format with three logical compartments: sample annotation (rows), feature annotation (columns), and abundance matrix (numeric values).
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42. ▌
    Format Conversion Conditional Logic 2 · holobiomicslab

    Use when you have generated a lipid spectral library (with lipid identities, adducts, m/z values, and fragmentation patterns) and need to export it for downstream mass spectrometry analysis on either an Orbitrap (via Excalibur DDA) or via Skyline's transition-based workflow.
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43. ▌
    High Resolution Ms2 Peak Assignment 2 · holobiomicslab

    Use when you have high-resolution MS2 data in .ms2 format from lipid A samples and need to perform automated structure annotation to identify lipid A molecular variants and their fragmentation patterns at scale.
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44. ▌
    Hplc Column Parameter Normalization 2 · holobiomicslab

    Use when when you have raw HPLC column specifications from RepoRT or similar metadata repositories and need to prepare them as input features for machine learning models. Apply this skill before featurizing molecular structures or training graph transformers for retention time prediction.
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45. ▌
    Identity Search Spectrum Annotation 2 · holobiomicslab

    Use when you have experimental MS/MS spectra and need to assign definitive molecular identities by matching against a curated spectral library.
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46. ▌
    Intensity Vector Manipulation Numpy 2 · holobiomicslab

    Use when you have extracted mass tracks (EICs) from multiple LC-MS samples aligned into a MassGrid structure, and you need to combine their intensity vectors into a single composite intensity vector for peak detection on the aggregate signal rather than per-sample.
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47. ▌
    Interactive Data Exploration Design 2 · holobiomicslab

    Use when you have NMR metabolomics measurements paired with pre-analytical metadata (processing delay times, centrifugation timing, sample type such as plasma vs. serum, cohort identifiers) and need to interactively explore how variation in processing conditions drives changes in metabolic.
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48. ▌
    Interference Profile Identification 2 · holobiomicslab

    Use when after running saturation repair or multidimensional smoothing on IM-MS data when you need to validate whether corrected peaks are reliable or whether overlapping coeluting/comobiling ions may have caused incorrect signal reconstruction.
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49. ▌
    Ion Adduct Isotope Pattern Matching 2 · holobiomicslab

    Use when you have a preprocessed feature table (m/z, retention time, intensities) from LC-MS and need to group features into empirical compounds.
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50. ▌
    Ion Mobility Feature Classification 2 · holobiomicslab

    Use when you have high-dimensional TWIM-MS data (arrival time and m/z dimensions) from a multi-omic sample and need to associate experimental features with biomolecular classes *before* running peak detection or feature identification pipelines.
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51. ▌
    Isotopologue Mass Delta Calculation 2 · holobiomicslab

    Use when when constructing a reference mass-matching framework for untargeted metabolomics or isotope-tracing LC-MS data, before pattern-matching observed features to isotopic and adduct variants.
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52. ▌
    Mass Spectral Relationship Matching 2 · holobiomicslab

    Use when after peak detection and feature table generation when you have a collection of m/z, retention time, and intensity values and need to identify which features are related variants (isotopes, adducts, or fragments) of the same parent compound.
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53. ▌
    Mass Spectrometry Adduct Annotation 2 · holobiomicslab

    Use when you have tabulated pairwise mass differences from a MALDI-MS imaging dataset (via massdiff()) and need to identify which observed mass differences correspond to known chemical adducts (e.g., [M+H]+, [M+Na]+, [M-H2O]+).
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54. ▌
    Msms Spectrum Metadata Preservation 2 · holobiomicslab

    Use when removing invalid or malformed entries (e.g., SMILES validation, format errors) from large spectral datasets (GNPS, MoNA, MTBLS1572, MassBank).
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55. ▌
    Msp File Parsing Edge Case Handling 2 · holobiomicslab

    Use when you are parsing mass spectrometry spectral library files in MSP format and need to guarantee that all spectrum records are either successfully integrated into the final dataset or explicitly logged with a reason for exclusion.
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56. ▌
    Natural Product Database Validation 2 · holobiomicslab

    Use when after curating and integrating structure-organism pairs from multiple source databases, and before publishing or using the dataset for computational research. Apply this skill when you have aggregated organism counts binned by structural diversity (e.
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57. ▌
    Network Based Functional Prediction 2 · holobiomicslab

    Use when you have an untargeted metabolomics feature table with m/z values, retention times, intensity measurements, and p-values from statistical testing, but lack or wish to bypass metabolite identification.
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58. ▌
    Numeric Array Round Trip Validation 2 · holobiomicslab

    Use when after implementing or modifying a numerical compression codec (such as MSNumpressCoder for m/z and intensity arrays in mass-spectrometry workflows) to verify that round-trip encode–decode cycles preserve numerical values within expected tolerance.
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59. ▌
    Poisson Noise Injection For Imaging 2 · holobiomicslab

    Use when augmenting mass spectrometry ion images for contrastive learning, particularly when the model must generalize across different detector conditions or signal-to-noise ratios.
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60. ▌
    Pre Analytical Delay Stratification 2 · holobiomicslab

    Use when when you have NMR metabolite measurements paired with documented pre-centrifugation and post-centrifugation delay times, and need to assess how processing delays affect metabolic parameter stability within a plasma or serum sample cohort.
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61. ▌
    Response Status Code Interpretation 2 · holobiomicslab

    Use when when you need to confirm that a documented web service endpoint is deployed and accessible before using it for analysis, or when troubleshooting tool availability in a bioinformatics pipeline. Apply this skill after obtaining a service URL (e.
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62. ▌
    Retention Time Intensity Tabulation 2 · holobiomicslab

    Use when when you have a resolved mzML or mzXML spectrum file and need to visualize or analyze the temporal intensity profile of a specific analyte (defined by its m/z value).
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63. ▌
    Sample Information Metadata Parsing 2 · holobiomicslab

    Use when when you have a validated ReDU sample-information metadata file (gnps_metadata.
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64. ▌
    Spectral Correlation Interpretation 2 · holobiomicslab

    Use when you have preprocessed 1H NMR spectral data (e.g., from plasma or biological samples acquired on a 600 MHz instrument) and need to identify the chemical composition of a prominent but structurally ambiguous peak.
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65. ▌
    Spectral Entropy Quality Assessment 2 · holobiomicslab

    Use when after feature detection and alignment in untargeted MS data processing, when you need to filter or rank candidate metabolite annotations by spectral quality before committing to xenobiotic metabolite assignments. Use when combining fragmentation similarity scores (e.
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66. ▌
    Spectral Library Record Structuring 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 you need to bind together into a queryable spectral library record for local compound.
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67. ▌
    Two Dimensional Ms Image Processing 2 · holobiomicslab

    Use when you have raw GC–MS or LC–MS data represented as a two-dimensional map (m/z axis vs. retention time axis) and need to identify chemo-/biomarker features across multiple analytes simultaneously, especially when conventional peak picking produces high false-positive or false-negative rates.
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68. ▌
    Untargeted Lc Ms Data Preprocessing 2 · holobiomicslab

    Use when when you have raw untargeted LC-MS metabolomics data and need to detect low-quality or mis-integrated peaks in an XCMS-processed xcmsSet object before performing metabolite annotation, statistical analysis, or biomarker discovery.
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69. ▌
    Weighted Loss Function Optimization 2 · holobiomicslab

    Use when when training a dual-encoder architecture (bi-encoder + cross-encoder) on NMR spectral data where independent encoding and joint pair processing produce competing or imbalanced gradient signals.
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70. ▌
    Mass Spectrum Peak Detection 2 · holobiomicslab

    Use when you have raw or processed MS spectrum data (mz/intensity pairs) from direct infusion MS (DI-MS), ASAP-MS, or other high-throughput ambient ionization methods, and need to identify which m/z signals represent true peaks of interest rather than noise or baseline drift.
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71. ▌
    Peak Labeling And Annotation 2 · holobiomicslab

    Use when immediately after automatic peak detection on a raw or processed MS spectrum when you have a list of candidate peaks with m/z and intensity values but lack systematic identifiers, confidence estimates, or ranked ordering.
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72. ▌
    Force Field Minimization Mmff94 2 · holobiomicslab

    Use when after RDKit generates multiple 3D conformers from ionized molecular structures using distance-geometry embedding, before filtering with ASE-ANI or submitting to quantum calculations.
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73. ▌
    Data Type Constraint Verification 2 · holobiomicslab

    Use when when ingesting or updating MassBank records in plain-text or structured format, and you need to verify that metadata fields (accession, name, formula, mass, spectrum peaks) comply with type definitions, presence requirements, and allowed value ranges.
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74. ▌
    Docker Volume Mount Configuration 2 · holobiomicslab

    Use when deploying the ipbhalle/metfragweb container and you need to supply custom MetFrag settings (ChemSpider tokens, proxy servers, local database connections) without modifying the container image.
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75. ▌
    Fatty Acid Composition Generation 2 · holobiomicslab

    Use when you need to systematically enumerate all possible lipid species within a defined analytical scope—specifically when you have specified one or more lipid classes (e.g., phosphatidylcholine, triacylglycerol) and fatty acid composition ranges (e.
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76. ▌
    Github Actions Workflow Execution 3 · holobiomicslab

    Use when you have a GitHub repository containing scientific records (e.
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77. ▌
    Hash Based Deduplication Workflow 2 · holobiomicslab

    Use when when processing open mass spectrometry library (OMSL) data that may contain duplicate spectral records (e.
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78. ▌
    Lipid Metadata Annotation Mapping 2 · holobiomicslab

    Use when when exporting in-memory generated spectra as MSP-format spectral libraries, you must first map each spectrum record to required MSP fields (NAME, PRECURSORMZ, SPECTRUM) and optional metadata annotations.
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79. ▌
    Hdf5 Cardinal Format Preservation 2 · holobiomicslab

    Use when after performing isotopic correction, quantitation, or other pixel-level transformations on a feature-by-pixel intensity matrix imported from an imzML file via Cardinal's HDF5 layout.
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80. ▌
    Hdf5 File Structure Specification 2 · holobiomicslab

    Use when when exporting quantified MSI data (feature-by-pixel intensity matrices with associated ion m/z, lipid annotations, and pixel spatial coordinates) from LipidQMap and you need to produce a standards-compliant HDF5 container that can be read by Cardinal and other MSI analysis tools.
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81. ▌
    Lipid Feature Identifier Matching 2 · holobiomicslab

    Use when you have an isotope-corrected or raw MSI dataset stored in HDF5 format following Cardinal::HDF5 conventions, a user-provided internal standard definition (sample identifier and/or feature name), and need to locate and extract the intensity row for that lipid before performing ratio-based.
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82. ▌
    Lipid Nomenclature Simplification 2 · holobiomicslab

    Use when you have spatial metabolomics data with semicolon-delimited isomer name annotations (such as the 'all_IsomerNames' column in SpaMTP Seurat objects) and you need to reduce annotation complexity before pathway analysis, statistical testing, or visualization.
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83. ▌
    Mass Difference Network Construction 2 · holobiomicslab

    Use when you have a filtered peak list (CSV with m/z values and assigned molecular formulas) from FT-ICR MS and want to infer biochemical transformations occurring in microbial or environmental samples.
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84. ▌
    Mass Spectrometry Data Preprocessing 2 · holobiomicslab

    Use when you have raw mass spectra or processed feature matrices from liquid chromatography–mass spectrometry (LC-MS) or direct infusion MS that must be ingested by a deep learning model for substance identification.
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85. ▌
    Spectrum Vector Similarity Searching 2 · holobiomicslab

    Use when you have millions of MS/MS spectra represented as low-dimensional vectors (via feature hashing) and need to compute pairwise distances only between similar spectra rather than comparing every spectrum to every other spectrum.
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86. ▌
    2d Nmr Pulse Sequence Implementation 2 · holobiomicslab

    Use when when you need to generate 2D metabolomic NMR spectra (COSY for homonuclear or HSQC/HMQC for heteronuclear correlations) from parsed metabolite concentration and spin-system J-coupling data, and you want to simulate realistic peak patterns including indirect-dimension evolution and phase.
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87. ▌
    Activity Score Robustness Assessment 2 · holobiomicslab

    Use when after computing PLAGE-derived activity scores for pathways or metabolite sets (Molecular Families, Mass2Motifs) from log2-standardized metabolomics intensity data.
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88. ▌
    Bayesian Meta Learning Model Fitting 2 · holobiomicslab

    Use when you have a pre-trained DNN RT predictor (e.g., trained on METLIN SMRT with 80,038 experimental RTs) and need to adapt it to predict retention times in a new or external chromatographic method for which you have only 10–50 calibration molecules with known RTs.
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89. ▌
    Categorical Grouping And Aggregation 2 · holobiomicslab

    Use when you have a flat table of structure-organism pairs or entity records and need to summarize their distribution across categorical bins (e.g., organism counts binned by number of associated structures in categories: 1, 1–10, 10–100, >100).
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90. ▌
    Chemical Database Query And Matching 2 · holobiomicslab

    Use when when you have mass-to-charge (m/z) values from mass spectrometry imaging or other MS experiments and need to assign molecular formulae with high precision, especially in spatially-resolved metabolomics where traditional LC-MS annotation methods are insufficient.
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91. ▌
    Coefficient Of Variation Computation 2 · holobiomicslab

    Use when you have loaded raw NMR or MS metabolomic abundance data into a SummarizedExperiment object and need to assess feature reproducibility before downstream association modeling.
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92. ▌
    Cosine Similarity Matrix Computation 2 · holobiomicslab

    Use when after generating normalized dense embeddings for both query and reference MS/MS spectra using a pre-trained model like SpecEmbedding.
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93. ▌
    Detection Limit Threshold Definition 2 · holobiomicslab

    Use when when preparing metabolomics abundance tables with left-censored missingness (values below instrument detection limit or quantification limit) for imputation.
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94. ▌
    Few Shot Learning Calibration Design 2 · holobiomicslab

    Use when you have experimental retention times measured on a source chromatographic method and want to predict RTs on a target chromatographic method, but possess only a small set (10–100) of molecules with ground-truth measurements on both methods.
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95. ▌
    Fragmentation Pattern Classification 2 · holobiomicslab

    Use when you have tandem mass spectra for compounds with known binary or categorical molecular properties (e.
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96. ▌
    Graph Based Knowledge Representation 2 · holobiomicslab

    Use when annotating metabolites in untargeted metabolomics experiments where both established biochemical pathways and experimental MS2 similarity patterns must be simultaneously leveraged.
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97. ▌
    Heterogeneous Graph Embedding Design 2 · holobiomicslab

    Use when when building a Graph Transformer model for continuous property prediction on molecules with associated experimental or instrumental metadata (e.g., retention time prediction across different chromatographic columns, methods, or conditions).
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98. ▌
    Hit Metric Evaluation Fold Averaging 2 · holobiomicslab

    Use when when you have a trained MS/MS spectral embedding model and need to measure compound identification accuracy on a held-out test set, but want to mitigate sensitivity to a single random train/test split. Use this skill if the original training set split is fixed (e.
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99. ▌
    Ion Mobility Dimension Interpolation 2 · holobiomicslab

    Use when when processing raw IM-MS data (Agilent MassHunter .
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100. ▌
     Isotope Adduct Anchor Identification 2 · holobiomicslab

     Use when when you have extracted mass tracks (EICs) from individual LC-MS samples and need to establish reliable landmarks for subsequent pairwise or global alignment across a cohort.
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