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HolobiomicsLab

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1. ▌
   Noise Smoothing Chromatographic Signals 2 · holobiomicslab

   Use when when you have raw LC-HRMS profile-mode data (rt × mz intensity matrices) and need to detect chromatographic peaks using gradient-descent or local-maxima algorithms.
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2. ▌
   Python Binding Generation With Nanobind 2 · holobiomicslab

   Use when when you have a C++ library (such as OpenMS) with nanobind binding specifications in a designated bindings directory and need to create a Python module that exposes C++ classes, functions, and data types to Python code.
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3. ▌
   Spectrum Metadata Extraction Validation 2 · holobiomicslab

   Use when when ingesting heterogeneous MS spectral data from multiple open-access libraries (OMS libraries) where metadata completeness and correctness are uncertain.
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4. ▌
   Cardinal Object Structure Understanding 2 · holobiomicslab

   Use when when you have loaded a raw or processed Cardinal MSImagingExperiment object from MS imaging data and need to (1) extract spectral intensities and m/z feature information for conversion to other formats (e.g., Seurat), (2) verify that normalization or spatial segmentation (e.
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5. ▌
   Cell Type Protein Profiling Integration 2 · holobiomicslab

   Use when you have co-registered IMC (protein imaging mass cytometry) and SIMS (secondary ion mass spectrometry for metabolites) data from the same tissue regions, cell segmentation masks, and need to assign cell types based on protein expression patterns, then overlay those assignments onto.
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6. ▌
   M Z Annotation Reduction Quantification 2 · holobiomicslab

   Use when you have spatial metabolomics data with semi-colon-delimited multi-isomer annotations (e.g., 'all_IsomerNames' column in SpaMTP Seurat objects) and you want to quantify the benefit of RefineLipids simplification with lipid_info='simple' parameter.
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7. ▌
   Msi Intensity Matrix Isotope Correction 2 · holobiomicslab

   Use when you have loaded a feature-by-pixel intensity matrix (HDF5 format following Cardinal::HDF5 layout) from imzML MSI data in positive ion mode and you have identified paired [M+H]+ and [M+Na]+ features for the same neutral lipid species (differing by ~22 Da in m/z).
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8. ▌
   Spatial Segmentation Shrunken Centroids 2 · holobiomicslab

   Use when apply SSC when you have preprocessed and normalized MS imaging data (e.g., after TIC normalization and peak processing) and need to discover spatially distinct metabolite regions without prior tissue annotation.
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9. ▌
   Spectral Vector Normalization By Intensity 2 · holobiomicslab

   Use when when converting pre-processed MS/MS spectra into fixed-length vector representations using Word2Vec embeddings for Spec2Vec similarity scoring. Specifically, apply this skill after you have represented individual peaks and neutral losses as words ('peak@xxx.xx', 'loss@xxx.
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10. ▌
    Chromatographic Window Coverage Assessment 2 · holobiomicslab

    Use when after extracting retention times from top MS1 features detected in an LC-MS run, and when you need to evaluate whether a given gradient time range (e.g., 0–30 minutes) is being used efficiently to separate compounds. Apply this skill as the objective function in gradient optimization (e.
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11. ▌
    Classification Algorithm Tuning Validation 2 · holobiomicslab

    Use when you have labeled training data (e.g., pqm_development with 500 peaks and 89 samples) and need to select which of multiple classification algorithms (e.g., AdaBoost, Random Forest, SVM) and their hyperparameters (e.
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12. ▌
    Compound Identification Ranking Evaluation 2 · holobiomicslab

    Use when after training a FlavorFormer model end-to-end with weighted loss on 1H NMR spectra and compound labels, apply this skill to a held-out test set to measure compound identification accuracy and ranking quality.
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13. ▌
    Descriptor Fingerprint Feature Engineering 2 · holobiomicslab

    Use when use when the workflow requires descriptor-fingerprint-feature-engineering.
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14. ▌
    Filter Criteria Composition And Validation 2 · holobiomicslab

    Use when you are preparing to reuse public tandem MS data from MassIVE via ReDU and need to partition files by sample metadata (e.g., organism, tissue type, extraction method, ionization source, pre-MS separation) into groups for co-analysis.
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15. ▌
    Gc Ms Data Preprocessing And Normalization 2 · holobiomicslab

    Use when you have raw GC-MS data (aroma, breath, or other volatile analyte samples) in NetCDF or vendor-native format and need to identify multivariate chemo-/biomarker features without conventional peak picking.
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16. ▌
    Graph Neural Network Architecture Assembly 2 · holobiomicslab

    Use when when you have: (1) a collection of molecules represented as molecular graphs (nodes=atoms, edges=bonds with chirality/order attributes); (2) structured metadata describing experimental conditions (e.
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17. ▌
    Group Wise Chemical Enrichment Calculation 2 · holobiomicslab

    Use when when comparing GNPS chemical annotations across two or more groups of samples (defined by ReDU sample-information categories such as sample type, extraction method, or ionization source) where the groups contain different numbers of files.
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18. ▌
    High Quality Spectral Library Benchmarking 2 · holobiomicslab

    Use when you have a pre-trained MS/MS spectral embedding model and need to validate that it achieves strong and consistent retrieval performance on curated spectral libraries that represent real-world data quality standards.
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19. ▌
    Interactive Spectral Visualization Emperor 2 · holobiomicslab

    Use when when you have computed PCA coordinates from chemical annotation matrices (e.
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20. ▌
    Ionization Mode And Column Mode Separation 2 · holobiomicslab

    Use when your LC-MS peak table from MS-DIAL or similar software contains data from multiple ionization modes (positive and/or negative) and/or multiple chromatographic columns (e.
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21. ▌
    Left Censored Missing Value Classification 2 · holobiomicslab

    Use when you have a metabolomics dataset (LC/MS or GC/MS) with missing values and need to determine which are below the limit of detection (LOD) or limit of quantification (LOQ). Left-censored classification is necessary when the missingness is informative—i.
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22. ▌
    Mass Spectrometry Ionization Mode Handling 2 · holobiomicslab

    Use when when you have a feature table from LC-MS preprocessed data (e.g. from asari v1.9.2) and need to annotate ions and infer neutral mass.
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23. ▌
    Mass Spectrometry Library Search Retrieval 2 · holobiomicslab

    Use when when you have an unknown MSMS spectrum (precursor m/z and fragment ions) and need to discover structurally related compounds from a spectral library.
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24. ▌
    Metabolite Annotation Network Architecture 2 · holobiomicslab

    Use when when annotating large-scale untargeted metabolomics datasets where reference library coverage is incomplete and you need to infer metabolite identities for unannotated compounds by propagating annotations from seed metabolites (database matches or prior curation) across both.
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25. ▌
    Metabolite Candidate Ranking By Confidence 2 · holobiomicslab

    Use when you have a set of candidate metabolites for an unknown compound detected in a liquid chromatography–mass spectrometry (LC-MS) experiment, predicted RTs from a trained DNN model, and access to calibration molecules (minimum 10) that connect your observed chromatographic method to a source.
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26. ▌
    Metabolite Set File Parsing And Validation 2 · holobiomicslab

    Use when when a user has prepared a custom collection of metabolite sets (e.g., from spectral fragmentation clustering, literature curation, or domain-specific grouping) in CSV or JSON format and wants to score their activity levels using PALS without modifying the core PALS codebase.
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27. ▌
    Metabolomics Imputation Method Application 2 · holobiomicslab

    Use when your metabolomics dataset (LC/MS or GC/MS) contains missing values encoded as NA or zero that represent compounds below the instrument's limit of detection (LOD) or limit of quantification (LOQ), rather than values missing completely at random.
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28. ▌
    Metabolomics Noise Perturbation Simulation 2 · holobiomicslab

    Use when when benchmarking or validating a pathway analysis method (such as PALS, ORA, or GSEA) on metabolomics data, you need quantitative evidence that the method's pathway rankings remain stable despite noise and missing peaks—conditions prevalent in real LC-MS/MS datasets.
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29. ▌
    Positional Encoding For Spectral Sequences 2 · holobiomicslab

    Use when preparing mass spectrum input tensors for transformer encoder layers in IDSL_MINT.
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30. ▌
    Pre Analytical Delay Effect Quantification 2 · holobiomicslab

    Use when you have uploaded a pre-analytical data table containing sample metadata, processing delay annotations (pre- and post-centrifugation timestamps or duration), and paired NMR metabolomic measurements for a sample cohort, and you need to quantify how delays at different time-points affect.
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31. ▌
    Pytorch Transformer Backbone Instantiation 2 · holobiomicslab

    Use when when building a graph-based molecular property prediction model that must process both molecular structures (as heterogeneous graphs) and tabular metadata (chromatographic column parameters).
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32. ▌
    Quality Control Sample Identification Lcms 2 · holobiomicslab

    Use when when you have XCMS-preprocessed LC-MS metabolomics data with a peak table and an accompanying covariate/metadata file that contains a 'SampleType' column, and you plan to apply QC-based quality filters (such as RSD filtering) or train classifiers for peak quality assessment.
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33. ▌
    Reference Prediction Comparison Evaluation 2 · holobiomicslab

    Use when you have executed a structure annotation pipeline (like BAM) on a validation dataset for which ground-truth molecular structure annotations exist, and you need to assess whether the pipeline's predictions match the reference annotations at the required sensitivity and specificity.
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34. ▌
    Spectral Feature Extraction And Annotation 2 · holobiomicslab

    Use when you have raw LC/MS data in mzML format and need to perform non-targeted screening to discover unknown chemical features without a predefined list of target compounds.
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35. ▌
    Spectral Library Annotation Interpretation 2 · holobiomicslab

    Use when you have received chemical annotations from GNPS spectral library matching and need to (1) assess annotation confidence and validity for downstream analysis, (2) understand why the same chemical may appear under multiple GNPS annotation IDs, or (3) decide whether to collapse or deduplicate.
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36. ▌
    Spectral Similarity Measure Implementation 2 · holobiomicslab

    Use when you have MSMS spectra from two or more compounds and need to identify which are structurally related. Use this skill when you want to rank spectrum pairs by similarity to discover novel analogs or validate structural assignments in untargeted metabolomics or natural products discovery.
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37. ▌
    Spectrum Object Creation And Preprocessing 2 · holobiomicslab

    Use when when you have raw tandem mass spectrometry peak data (m/z and intensity pairs), precursor m/z, charge state, and adduct annotation for one or more compounds, and need to construct normalized spectrum objects suitable for downstream spectral alignment, modification site prediction, or.
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38. ▌
    Stochastic Weight Averaging Implementation 2 · holobiomicslab

    Use when training a deeply regularized deep neural network on a large molecular feature dataset (e.
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39. ▌
    Tabular Results Aggregation And Comparison 2 · holobiomicslab

    Use when when you have chemical annotations (GNPS matches) distributed across multiple sample groups (e.g., by sample type, extraction method, ionization source) with unequal numbers of files per group, and you need to compare enrichment fairly without group-size bias.
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40. ▌
    Tanaka Physicochemical Descriptor Handling 2 · holobiomicslab

    Use when you have raw HPLC column metadata arrays containing Tanaka parameter blocks that will be fed into a featurizer for machine learning on retention times. Tanaka parameters are present but may contain string artifacts ('2.7 spp', '2.
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41. ▌
    Cross Instrument Data Harmonization 2 · holobiomicslab

    Use when you have mass spectrometry spectral data from multiple instrument types (e.g., direct infusion MS, ambient ionization MS, laser desorption/ionization MS) and need to perform unified species discrimination or database scoring across all samples regardless of their source instrument.
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42. ▌
    Unknown Sample Reference Comparison 2 · holobiomicslab

    Use when you have a preprocessed unknown sample spectrum (m/z peaks and intensities) from high-throughput mass spectrometry (DI-MS, ASAP-MS, or ambient ionization methods) and need to identify the species or authenticate a sample against a curated reference database of known spectra.
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43. ▌
    Ion Mobility Machine Learning Training 2 · holobiomicslab

    Use when you have a curated dataset of molecular structures (or molecular descriptors) paired with experimentally measured or reference collision cross section values, and you need to predict CCS values for a set of ≤10,000 novel molecules to filter or prioritize metabolomics identifications.
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44. ▌
    Deletion Reason Logging And Traceability 2 · holobiomicslab

    Use when when processing OMSLs (Open Mass Spectra Libraries) with heterogeneous data quality, inconsistent annotations, or mixed ionmode/chromatographic modes (LC/GC), and you need to track which spectra were discarded, why, and potentially recover or reprocess them in future iterations.
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45. ▌
    Mass Spectrometry Spectral Preprocessing 2 · holobiomicslab

    Use when you have raw mass spectrometry spectra from an unknown analyte or a synthetic compound library and need to feed them into PS2MS or similar deep learning classifiers for NPS detection.
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46. ▌
    Metabolomics Library Generation Pipeline 2 · holobiomicslab

    Use when when you have cloned or accessed the Reverse_metabolomics_library_generation repository and need to verify that the Nextflow workflow is properly configured, executable, and produces expected spectral library outputs.
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47. ▌
    Similarity Score Computation For Spectra 2 · holobiomicslab

    Use when you have a preprocessed unknown sample spectrum (m/z peaks and intensities) and need to identify the most likely species or reference entries by scoring it against a database of known spectra.
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48. ▌
    Spectrum Filtering With Deletion Logging 2 · holobiomicslab

    Use when processing heterogeneous mass spectrometry libraries (e.g., from OMSLs) where chemical identifiers are unevenly populated across records.
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49. ▌
    Tomcat Application Server Initialization 2 · holobiomicslab

    Use when when you need to deploy a Java web application packaged in a Tomcat Docker container to a specified HTTP endpoint, and must verify that the container starts successfully, the Tomcat server initializes, and the application becomes accessible at the mapped host port before proceeding with.
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50. ▌
    High Resolution Spot To Spot Integration 2 · holobiomicslab

    Use when you have two spatial omics datasets (e.g., spatial transcriptome and metabolome spot matrices) collected from the same or adjacent tissue sections, with both feature matrices (X: np.ndarray) and spatial coordinates (D: np.ndarray containing location information in .
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51. ▌
    Metabolomics Preprocessing Normalization 2 · holobiomicslab

    Use when you have acquired raw SIMS (secondary ion mass spectrometry) metabolite images aligned with tissue regions and segmented single-cell masks, and you need to extract normalized metabolite intensity values per cell before performing cell-type assignment, VAE embedding, or protein–metabolite.
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52. ▌
    Spectral Peak Word Embedding Representation 2 · holobiomicslab

    Use when when you have MS/MS spectra (LC-MS or equivalent positive ionization mode data) that you intend to embed using Word2Vec or similar distributional semantic models, or when you need to prepare spectral data for training similarity models that learn peak co-occurrence patterns rather than.
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53. ▌
    Adduct Form Prediction And Mass Calculation 2 · holobiomicslab

    Use when you have a characterized lipid species (with defined class and fatty acid composition) and need to predict which adduct forms will ionize under your experimental ionization mode (positive or negative ESI), and you require accurate precursor m/z values for DDA method configuration or.
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54. ▌
    Baseline Method Comparison And Benchmarking 2 · holobiomicslab

    Use when you have developed or adapted an analytical method (e.g., NPFimg for GC–MS marker identification) and need to demonstrate its reliability or improved performance over a widely-used reference method (e.g., XCMS). Apply this skill when you have access to both the same raw input data (e.
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55. ▌
    Binary Classification Output Interpretation 2 · holobiomicslab

    Use when you have executed a binary classifier (such as BitterPredict.m) on a set of molecules with chemical structure descriptors and need to translate the raw predictions into a structured CSV output file that maps molecule identifiers to their predicted class labels (bitter or not-bitter).
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56. ▌
    Chemical Informatics Data Format Conversion 2 · holobiomicslab

    Use when when you have raw SMILES strings collected from multiple external databases that require standardization and deduplication before integration into a unified chemical structure database. Specifically, apply this skill when you need to convert interim/tables/0_original/structure/smiles.tsv.
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57. ▌
    Coefficient Of Variation Threshold Analysis 2 · holobiomicslab

    Use when you have per-feature CV values from quality control analysis of NMR or MS metabolomic data and need to: (1) establish whether your dataset meets FDA reproducibility standards for downstream biomarker discovery or quantification; (2) benchmark data quality against regulatory thresholds;
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58. ▌
    Collision Cross Section Bias Quantification 2 · holobiomicslab

    Use when you have ion mobility-mass spectrometry lipidomics data from samples spiked with U¹³C-labeled lipid internal standards (fully labeled yeast extract) and want to assess whether measured CCS values systematically deviate from a validated DT CCS N₂ reference library, indicating bias that may.
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59. ▌
    Count Verification Against Published Values 2 · holobiomicslab

    Use when when you have access to a curated dataset (such as LOTUS) with published headline statistics in a peer-reviewed article or enriched index, and you need to validate data integrity, trace reporting accuracy, or establish a reproducible baseline before downstream analysis.
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60. ▌
    Data Driven Similarity Layer Implementation 2 · holobiomicslab

    Use when you have untargeted metabolomics mass spectrometry data (MS2 spectra with m/z values and intensities) and an existing knowledge-driven metabolite network, and you need to enhance annotation accuracy and coverage by leveraging experimental similarity patterns rather than relying solely on.
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61. ▌
    Deep Neural Network Training Regularization 2 · holobiomicslab

    Use when training a DNN on retention time prediction or similar continuous regression tasks where: (1) the feature space is very high-dimensional (thousands of molecular descriptors and fingerprints), (2) the training set is moderately sized (tens of thousands of molecules), (3) you observe or.
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62. ▌
    Gaussian Process Uncertainty Quantification 2 · holobiomicslab

    Use when after collecting observed separation efficiency scores at sampled gradient conditions and you need to propose the next gradient to evaluate.
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63. ▌
    Graph Neural Network Encoding For Molecules 2 · holobiomicslab

    Use when you have molecular structures (SMILES or SDF format) that need to be matched against MS/MS spectra, or you need to compute similarity between query spectra and a reference library of compounds.
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64. ▌
    Mass Spectrometry File Inventory Management 2 · holobiomicslab

    Use when you have uploaded MS/MS data to MassIVE with validated sample-information metadata and need to subset public files by sample attributes (organism, tissue type, extraction method, ionization source, pre-MS separation) to create reproducible, manageable cohorts for molecular networking.
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65. ▌
    Mass Spectrometry Scan Extraction By Target 2 · holobiomicslab

    Use when you have raw LC-MS/MS chromatogram files in mzML/mzXML format (converted from Thermo, Waters, or Bruker instruments) acquired in DDA or targeted MS/MS mode, and you need to isolate specific MS1 precursors and their corresponding MS2 fragments based on known m/z values and optional.
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66. ▌
    Metabolite Feature Filtering By Missingness 2 · holobiomicslab

    Use when you have a raw metabolite abundance matrix (e.g., from MSPrep or another LC-MS/MS pipeline) with many features and samples, and you observe that a substantial fraction of metabolites are missing (NA or zero-valued) across replicates.
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67. ▌
    Metabolite Structure Annotation Integration 2 · holobiomicslab

    Use when you have a set of candidate transformed structures generated by biotransformation rules (e.
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68. ▌
    Metabolomics Feature Integration Assessment 2 · holobiomicslab

    Use when after XCMS peak picking and fillPeaks() when you have xcmsEIC and filled xcmsSet objects and need to systematically flag low-quality or unreliable peak integrations prior to statistical modeling or machine learning classification.
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69. ▌
    Missing Value Imputation Strategy Selection 2 · holobiomicslab

    Use when after mark_nas() has replaced non-NA missing-value codes (e.g., 0, 1) with R's NA in the exprs matrix of a MetaboSet object, and you need to decide whether to apply random forest imputation, simple imputation strategies, or defer imputation.
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70. ▌
    Molecular Descriptor Fingerprint Generation 2 · holobiomicslab

    Use when you have a collection of chemical structures (SMILES, InChI, SDF, or mol formats) and need to train or apply a machine learning model for retention time prediction or molecular property estimation.
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71. ▌
    Open Format Mass Spectrometry File Handling 2 · holobiomicslab

    Use when you have raw mass spectrometry data in vendor-proprietary formats (e.g., .raw, .d, .ms) that you need to upload to MassIVE for public sharing or submit to GNPS for spectral library matching and chemical annotation.
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72. ▌
    Peak Annotation Refinement Structure Driven 2 · holobiomicslab

    Use when you have two related compounds (a known reference and its structural analog with unknown modification site), baseline peak annotations from cosine alignment, and newly available structural information (SMILES or molecular structure) for the modified compound.
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73. ▌
    Python Workflow Execution And Orchestration 2 · holobiomicslab

    Use when you have raw LC/MS data in mzML format and need to execute a complete non-targeted screening workflow to extract and annotate chemical features.
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74. ▌
    Reaction Pathway Assignment And Propagation 2 · holobiomicslab

    Use when you have detected and clustered unknown MS features from untargeted xenobiotic metabolomics data, computed fragmentation pattern similarity scores between features and reference spectra, and now need to systematically assign individual features to specific biotransformation reactions (e.
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75. ▌
    Recursive Propagation Algorithm Development 2 · holobiomicslab

    Use when when you have constructed a two-layer metabolite annotation network (knowledge-driven and data-driven) and need to propagate initial seed annotations (e.
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76. ▌
    Scan Number Mapping Dictionary Construction 2 · holobiomicslab

    Use when after performing LOWESS regression on landmark peak RT pairs between a sample and reference, you need to encode the learned RT transformation as a reusable, memory-efficient lookup table that can be applied during feature alignment without recomputing the regression for every sample.
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77. ▌
    Similarity Ranking And Retrieval Evaluation 2 · holobiomicslab

    Use when after training contrastive embeddings that unify MS/MS spectra and molecular structures into a shared embedding space.
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78. ▌
    Spectrum Alignment Performance Benchmarking 2 · holobiomicslab

    Use when you have tandem MS spectra from structurally related or known compounds and need to decide which similarity metric will maximize correct ranking of related molecules in a spectral library search.
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79. ▌
    Structured Molecule Relationship Evaluation 2 · holobiomicslab

    Use when when you have tandem mass spectra (MSMS) from related or candidate molecules and need to determine which similarity metric—cosine, modified cosine, or neutral loss— ranks structurally similar compounds in your dataset. Particularly useful when structural relationships are known a priori (e.
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80. ▌
    Thread Utilization Profiling And Validation 2 · holobiomicslab

    Use when when deploying Mass2SMILES inference on CPU-only hardware (e.g., when GPU/CUDA support is unavailable), you need to verify that the InferenceModel cpu_threads parameter (e.g., cpu_threads=128) actually controls thread allocation during execution.
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81. ▌
    Xenobiotic Metabolite Annotation From Ms Ms 2 · holobiomicslab

    Use when you have aligned MS/MS feature tables (e.g., from MSDial ver. 4.80) representing unknown metabolites suspected to be Phase I/II transformation products of xenobiotics, and you need to assign both chemical identity and biotransformation pathway context to each feature.
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82. ▌
    Spectral Database Search And Scoring 2 · holobiomicslab

    Use when you have preprocessed mass spectrometry data (peak-picked, baseline-corrected) from DI-MS, ASAP-MS, or LDI-MS instruments and need to identify unknown samples by comparing their spectral fingerprints against a validated reference database of known species or compounds.
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83. ▌
    Collision Cross Section Bias Estimation 2 · holobiomicslab

    Use when when you have IM-MS lipidomics data spiked with U13C labeled lipid internal standards (e.g., fully labeled yeast extract) and want to assess whether measured CCS values deviate systematically from expected values in the DTCCS_N2 reference library.
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84. ▌
    Conformer Filtering Threshold Selection 2 · holobiomicslab

    Use when after RDKit has generated a large ensemble of 3D conformers for a molecule (typically hundreds to thousands), you need to reduce computational burden before quantum-chemical single-point energy evaluation.
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85. ▌
    Machine Learning Model Training Sklearn 2 · holobiomicslab

    Use when your metabolomics analysis pipeline requires CCS value prediction for ion-mobility mass spectrometry data, you have access to a curated training set of known metabolites with experimentally validated CCS values, and you plan to predict CCS values on target datasets containing 10,000+.
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86. ▌
    Noise Threshold Filtering Spectral Data 2 · holobiomicslab

    Use when working with raw IM-MS data (Agilent MassHunter .d or UIMF format) that contains low-abundance background noise, isolated high-intensity artifacts, or jagged peaks characteristic of low-abundance ions.
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87. ▌
    Small Molecule 3d Structure Preparation 2 · holobiomicslab

    Use when when you have ionized adduct structures (SMILES or MOL format) from a prior ionization-state determination step and need to create multiple low-energy 3D conformations before filtering with machine-learning potentials (ASE-ANI) or quantum calculations.
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88. ▌
    Cnn Architecture Design For Spectral Data 2 · holobiomicslab

    Use when when you have mass-spectrometry data (m/z and intensity pairs or spectral matrices) paired with ground-truth molecular fingerprints or InChIKeys, and you need to learn a non-linear mapping from spectral patterns to structural fingerprints for downstream metabolite ranking or annotation.
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89. ▌
    Composite Map Data Structure Construction 2 · holobiomicslab

    Use when after mass track extraction and alignment across samples, when you have a MassGrid structure (m/z-aligned mass tracks) and corresponding retention time calibration dictionaries for each sample, and need to prepare input for composite peak detection rather than per-sample peak detection.
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90. ▌
    Metabolomic Meta Analysis Workflow Design 2 · holobiomicslab

    Use when you have multiple metabolomic studies with aggregate summary statistics (p-values, fold-change estimates) and need to perform meta-analysis while harmonizing compound nomenclature across datasets. Use this skill when the underlying R package (e.
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91. ▌
    Parameter Configuration Conditional Logic 2 · holobiomicslab

    Use when setting up a LipidMatch analysis run and you need to select among three mutually-exclusive analysis modes (PFAS, Lipid, or Tween-positive detection). The trigger is application-level metadata or user specification that indicates which detection mode should execute.
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92. ▌
    Graph Database Indexing And Serialization 2 · holobiomicslab

    Use when you have retrieved and deduplicated chemical formulae and metadata from multiple heterogeneous sources (HMDB, ChEMBL, PubChem) and extracted both structural relationships (DBEdges) and biological reactant pairs (BioEdges from KEGG), and now need to merge them into a single queryable.
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93. ▌
    Mass Spectrometry Peak Intensity Encoding 2 · holobiomicslab

    Use when working with imaging mass spectrometry (IMS) datasets where you need to (1) automatically identify marker ions without manual annotation, (2) reduce peak intensity dimensionality while preserving spatial relationships between measurement points, or (3) apply iterative peak picking.
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94. ▌
    Word Embedding Aggregation For Spectral Data 2 · holobiomicslab

    Use when when you have pre-processed MS/MS spectra and a pre-trained Word2Vec model, and need to compute fast, scalable similarity scores for library matching or molecular networking that correlate better with structural similarity than cosine-based methods.
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95. ▌
    Word Embedding Based Spectrum Representation 2 · holobiomicslab

    Use when when comparing large numbers of MS/MS spectra against spectral libraries or in molecular networking, particularly when molecules differ by multiple structural modifications and cosine-based scores produce excessive false positives.
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96. ▌
    Bayesian Model Update With Experimental Data 2 · holobiomicslab

    Use when you have completed one or more LC-MS gradient runs, extracted separation efficiency metrics from the resulting MS1 and MS2 spectra, and need to incorporate those real experimental observations into your Gaussian process model to improve the next gradient proposal.
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97. ▌
    Bi Encoder Cross Encoder Architecture Design 2 · holobiomicslab

    Use when you have paired spectrum-compound reference data and need to simultaneously retrieve candidate compounds rapidly (bi-encoder) while also refining relevance scores through joint context modeling (cross-encoder).
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98. ▌
    Blood Sample Processing Parameter Extraction 2 · holobiomicslab

    Use when when you have peripheral blood sample cohorts (plasma/serum) with multiple timestamps (e.
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99. ▌
    Community Metabolic Reconstruction Synthesis 2 · holobiomicslab

    Use when you have draft metabolic reconstructions (in SBML or equivalent format) for multiple organisms sampled from a single ecological community (e.
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     Exploitation Exploration Trade Off Balancing 2 · holobiomicslab

     Use when you are in the Bayesian optimization loop after fitting a Gaussian Process model to observed LC gradient runs, and you need to propose the next gradient to evaluate.
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