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HolobiomicsLab

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1. ▌
   Compound Retrieval Hit At K Evaluation 2 · holobiomicslab

   Use when when you have generated spectral embeddings for a query set and a reference spectral library, computed pairwise cosine similarity scores between them, and need to quantify retrieval performance across ranked candidates (top-1, top-5, top-10).
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2. ▌
   Conditional Allocation Design Patterns 2 · holobiomicslab

   Use when when designing a dataset storage layer that must handle variable dataset sizes, block layouts, and platform-specific constraints (e.g., Windows vs. non-Windows). Specifically: (1) you have parsed dataset metadata including cache-file flags, total point counts, and block configuration;
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3. ▌
   Cosine Annealing Schedule Optimization 2 · holobiomicslab

   Use when when training a regularized deep neural network for molecular property regression (e.g., retention time prediction on the METLIN SMRT dataset with 80,038+ samples), use cosine annealing warm restarts to escape plateaus and improve convergence.
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4. ▌
   Dataset Storage Interface Architecture 2 · holobiomicslab

   Use when you have NMR dataset metadata (cache-file flag, total point count, block layout configuration) and need to select an appropriate storage backend that balances memory efficiency, access patterns, and platform-specific constraints.
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5. ▌
   Deep Learning Model Checkpoint Loading 2 · holobiomicslab

   Use when when you have MS/MS spectra from GNPS or other libraries and need to apply a pre-trained FIDDLE model (TCN formula predictor or Siamese rescore architecture) without training from scratch. Use this skill before running inference on new samples or benchmarks.
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6. ▌
   Feature Condition Comparative Analysis 2 · holobiomicslab

   Use when when you have molecular structures, a regression target (e.g. retention time), and want to establish whether one class of molecular features (e.g., fingerprints) outperforms another (e.g., descriptors) or whether combining them yields marginal gains.
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7. ▌
   Feature Correlation Graph Construction 2 · holobiomicslab

   Use when after imputing missing values and before assigning Cluster_IDs in the notame preprocessing pipeline.
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8. ▌
   Feature Flagging Threshold Calibration 2 · holobiomicslab

   Use when after drift correction in non-targeted LC-MS metabolomics workflows, when you need to decide which molecular features are sufficiently reproducible (low instrument/QC variance) and biologically informative (high QC-versus-biological signal ratio) to retain for downstream statistical.
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9. ▌
   Fingerprint Vector Loading And Parsing 2 · holobiomicslab

   Use when when you have deposited or archived biosynfoni fingerprint vectors (such as from Zenodo 10.5281/zenodo.14822624) and need to ingest them into a Python workflow to compute distributional statistics, bit-frequency profiles, sparsity metrics, or pairwise similarity coefficients.
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10. ▌
    Fragmentation Pattern Annotation Magma 2 · holobiomicslab

    Use when you have a tandem MS/MS spectrum of a structurally modified compound and a known reference structure (SMILES), and you need to annotate which fragment ions correspond to specific bonds or atoms in the molecule.
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11. ▌
    Fragmentation Pattern Query Validation 2 · holobiomicslab

    Use when after converting a decision tree path into a MassQL query string, before deployment to production mass spectrometry workflows.
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12. ▌
    Fusion Pooling Strategy Implementation 2 · holobiomicslab

    Use when you have extracted parallel feature streams from a CNN backbone (local spectral patterns) and a Transformer backbone (global dependencies) in 1H NMR spectra, and you need to fuse them into a single embedding for bi-encoder or cross-encoder processing before compound identification scoring.
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13. ▌
    Image Based Feature Extraction Ms Maps 2 · holobiomicslab

    Use when you have a two-dimensional MS map (m/z vs retention time) from GC–MS or LC–MS data and need to discriminate analytes and identify marker features without false positives from peak picking; particularly useful for untargeted metabolomics at ppb sensitivity (e.
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14. ▌
    Internal Standardization Lipid Mapping 2 · holobiomicslab

    Use when you have (LC-)IM-MS lipidomics data from samples spiked with U13C-labeled yeast extract, measured CCS values for both labeled and unlabeled lipids, and need to quantify systematic CCS bias between your instrument and a reference library before applying bias correction.
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15. ▌
    Isotope And Adduct Pattern Recognition 2 · holobiomicslab

    Use when when processing MS1 mass tracks from a single sample and you have already constructed per-bin mass tracks with consensus m/z and intensity vectors.
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16. ▌
    Lc Ms Dataset Acquisition And Curation 2 · holobiomicslab

    Use when when beginning an untargeted LC-MS metabolomics study and need to assemble a cohort of mzML files for processing; particularly when establishing performance baselines across sample counts (10, 50, 100+ samples), validating reproducibility, or preparing data for publication.
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17. ▌
    Mass Spectrometry Data Format Handling 2 · holobiomicslab

    Use when you have raw MRM sample files from an LC-MS/MS instrument and need to programmatically identify and tabulate all precursor m/z and product m/z pairs for each MRM transition.
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18. ▌
    Mass Spectrometry Imaging Data Loading 2 · holobiomicslab

    Use when you have raw or preprocessed MS imaging data archived as an RDS file or from a Zenodo deposit that includes the full m/z feature set (e.g., 10,200 m/z values spanning 150–1000 m/z range) and spectrum count (e.
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19. ▌
    Metabolite Feature Table Normalization 2 · holobiomicslab

    Use when you have selected a subset of ReDU public tandem MS files with GNPS chemical annotations (level 2 or 3 spectral library matches) and wish to explore sample relationships via principal component analysis.
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20. ▌
    Multi Domain Search Result Aggregation 2 · holobiomicslab

    Use when you have executed batch searches of MS/MS spectra against multiple domain-specific MASST tools and need to synthesize results across domains (e.
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21. ▌
    Multidimensional Signal Smoothing Imms 2 · holobiomicslab

    Use when processing raw IM-MS data (Agilent MassHunter .d or UIMF format) that contains jagged, low-abundance ion peaks or when saturation repair has been applied and the resulting reconstructed signals need artifact removal and enhancement.
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22. ▌
    Multivariate Ordination Interpretation 2 · holobiomicslab

    Use when you have a collection of tandem MS/MS samples (stored in MassIVE) with GNPS spectral library annotations (m/z, retention time, compound identity), and you want to explore whether samples cluster by shared chemical features without predefined class labels.
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23. ▌
    Nmr Spectral Data Format Serialization 2 · holobiomicslab

    Use when you have generated 1D FID time-domain data and Fourier-transformed frequency-domain 1H NMR spectra, or computed 2D COSY/HSQC correlation matrices, and need to write them to disk in a format that standard NMR software (e.
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24. ▌
    Nmr Spectral Preprocessing And Phasing 2 · holobiomicslab

    Use when when working with raw 1H NMR FID data acquired on instruments like Bruker Avance spectrometers that require baseline correction, phase adjustment, and signal alignment before metabolite identification or statistical association testing can proceed reliably.
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25. ▌
    Pathway Activity Propagation Inference 2 · holobiomicslab

    Use when when you have an untargeted metabolomics feature table (m/z values, retention times, intensity measurements, and p-values from statistical testing) and want to predict which metabolic pathways are active, but metabolite identification is incomplete, unreliable, or computationally expensive.
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26. ▌
    Retention Time Alignment Index Mapping 2 · holobiomicslab

    Use when when processing multiple LC-MS samples with varying scan numbers or retention-time drift, before constructing composite mass tracks for peak detection.
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27. ▌
    Retention Time Prediction Optimization 2 · holobiomicslab

    Use when when you have a retention-time dataset (e.g., SMRT or Eawag_XBridgeC18_364) in .xlsx format and need to train or adapt a graph neural network model to predict chromatographic retention times for new compounds.
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28. ▌
    Robustness Analysis Under Perturbation 2 · holobiomicslab

    Use when you have completed pathway analysis using multiple competing methods (e.g., PALS, ORA, GSEA) on a metabolomics peak intensity dataset and need to verify that ranking results remain stable when input data is intentionally corrupted.
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29. ▌
    Spectral Database Output Normalization 2 · holobiomicslab

    Use when you have executed batch spectral searches against two or more domain-specific MASST tools and received heterogeneous output formats (domain-specific HTML trees, JSON objects, TSV match tables) that need to be reconciled into a single normalized schema for downstream aggregation.
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30. ▌
    Spectral Library Export And Formatting 2 · holobiomicslab

    Use when after theoretical spectra have been generated for lipid–adduct combinations with enumerated fragment masses and intensities, and you need to deploy them for downstream MS/MS analysis on Orbitrap (DDA via Excalibur) or SkyLine (targeted/PRM) workflows.
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31. ▌
    Spectral Molecular Embedding Alignment 2 · holobiomicslab

    Use when you have MS/MS spectra in MGF or similar format and a reference library of molecular structures (SMILES or SDF), and your goal is to retrieve the most likely structures for an unknown compound spectrum by learning a joint embedding space rather than using traditional spectral similarity or.
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32. ▌
    Structure Prediction From Mass Spectra 2 · holobiomicslab

    Use when you have GNPS-style MGF spectral files from MS/MS experiments and need to predict the molecular structure (as SMILES) of unknown compounds.
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33. ▌
    Tandem Ms Spectral Data Interpretation 2 · holobiomicslab

    Use when you have untargeted MS2 spectral data in MS2MP-compatible format and need to assign KEGG pathway annotations to spectra without spectral library matching or manual compound identification.
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34. ▌
    Unit Test Fixture Design And Execution 2 · holobiomicslab

    Use when when implementing or modifying a numerical compression/decompression component (e.g., Numpress for mass-spectrometry m/z and intensity arrays) and you need to verify that round-trip encoding and decoding preserves numerical fidelity.
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35. ▌
    Graph Neural Network Model Loading 2 · holobiomicslab

    Use when when you need to evaluate GNN performance on collision cross section prediction using the enveda/ccs-prediction repository, either by loading an existing pre-trained model checkpoint or by retraining from scratch using deposited datasets and published hyperparameters.
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36. ▌
    Container Port Mapping Configuration 2 · holobiomicslab

    Use when you need to deploy a containerized web application (such as MetFrag webapp on Tomcat) and make it accessible at a specific HTTP endpoint on the host machine. Use this skill when you have a Docker image with an internal service listening on a known port (e.
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37. ▌
    In Silico Spectrum Metadata Curation 2 · holobiomicslab

    Use when when processing collections of in-silico mass spectra from OMSLs (Open Mass Spectra Libraries) where the adduct field is absent, null, or not explicitly specified in the source file;
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38. ▌
    Retention Time Mz Feature Extraction 2 · holobiomicslab

    Use when you have LC-HRMS profile-mode data (e.g., netCDF or mzML format) and need to convert detected or reference chromatographic peaks into fixed-size 2D arrays (rt × mz regions) to train or apply a convolutional neural network for peak classification and bounding-box prediction.
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39. ▌
    Cardinal To Seurat Object Conversion 2 · holobiomicslab

    Use when after completing Cardinal-based preprocessing (feature summarization, TIC normalization, peak processing, spatial segmentation, and SSC annotation), use this conversion when you need to leverage Seurat's downstream statistical methods—such as differential metabolite expression testing.
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40. ▌
    Cross Database Entity Reconciliation 2 · holobiomicslab

    Use when you have chemical entity records scattered across two or more public repositories (e.g., HMDB, ChEMBL, PubChem, KEGG) and need a single authoritative, deduplicated knowledge base indexed by a queryable identifier (e.g., m/z value or chemical formula).
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41. ▌
    Hdf5 File Format Reading And Writing 2 · holobiomicslab

    Use when you have isotope-corrected or raw ion-image intensity matrices from LipidQMap or similar MSI software and need to: (1) export them as persistent HDF5 containers for archival or sharing, (2) programmatically read an existing Cardinal::HDF5 export to extract intensity matrices and feature.
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42. ▌
    Isotope Corrected Image Quantitation 2 · holobiomicslab

    Use when after isotopic correction has been performed on MSI ion images and you need to convert normalized intensities into absolute quantitative values.
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43. ▌
    Lipid Adduct Pairing By Neutral Mass 2 · holobiomicslab

    Use when when processing mass spectrometry imaging data with multiple adduct forms of the same lipid species, and you need to correct one adduct form (e.g. [M+H]+) for isotopic interference from a co-occurring adduct (e.g. [M+Na]+).
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44. ▌
    Lipid Feature Annotation And Sorting 2 · holobiomicslab

    Use when after quantifying ion images in LipidQMap and before exporting to HDF5 format, when you need to organize per-feature metadata (lipid ID, class, adduct, m/z, internal standard flag) into aligned datasets that can be linked to intensity data via dimension scales and sorted for reproducible.
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45. ▌
    Morphological Alignment Optimization 2 · holobiomicslab

    Use when you have two or more spatial omics datasets (e.
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46. ▌
    Spatial Spot Coordinate Registration 2 · holobiomicslab

    Use when when you have paired spatial transcriptome and metabolome datasets with spot-based coordinates that need to be aligned for multi-modal integration.
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47. ▌
    Mass Spectrometry Spectrum Tokenization 2 · holobiomicslab

    Use when you have pre-processed MS/MS spectra and need to prepare them for word-embedding-based similarity methods (e.g., Spec2Vec).
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48. ▌
    Ms Dial Feature Detection And Alignment 2 · holobiomicslab

    Use when when you have raw LC-HRMS metabolomics data in .mzML or .abf format and need to perform untargeted feature detection with chromatographic alignment across multiple samples.
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49. ▌
    Neutral Loss Calculation From Precursor 2 · holobiomicslab

    Use when when converting MS/MS spectra into spectral documents for Spec2Vec embedding, and you want to capture chemical relationships implicit in the fragmentation pattern (e.g., loss of water, ammonia, or CO2) that may correlate with structural similarity. Use this when the neutral loss range (5.
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50. ▌
    Spectral Similarity Scoring Computation 2 · holobiomicslab

    Use when you have a query spectrum (or set of query spectra) and need to rank candidate library spectra by their likelihood of sharing the same chemical structure.
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51. ▌
    Untargeted Metabolomics Data Processing 2 · holobiomicslab

    Use when you have two separate LC-MS untargeted metabolomic feature tables (e.
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52. ▌
    Analyte Discrimination Machine Learning 2 · holobiomicslab

    Use when you have raw chromatography–mass spectrometry data (GC-MS or LC-MS) in 2D m/z–retention time format and need to identify and discriminate multiple analytes while avoiding false peak detections inherent in conventional peak picking.
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53. ▌
    Chemical Annotation Matrix Construction 2 · holobiomicslab

    Use when you have selected a subset of public tandem MS files from ReDU/MassIVE that have been processed through GNPS spectral library matching, and you need to organize their chemical annotations into a matrix (rows = files, columns = annotated features) before performing PCA or other multivariate.
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54. ▌
    Computational Environment Configuration 2 · holobiomicslab

    Use when at the start of any DaDIA pipeline execution, or whenever you are preparing to run a complex multi-package R workflow on a new system or after updating package managers.
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55. ▌
    Cross Dataset Generalization Assessment 2 · holobiomicslab

    Use when you have a pre-trained MS/MS spectral embedding model evaluated on one or more source datasets (GNPS, MoNA, MTBLS1572) and need to verify that it performs well on independent, high-quality curated spectral libraries to claim robustness.
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56. ▌
    CSV Serialization For Mass Spectrometry 2 · holobiomicslab

    Use when you have generated or curated a lipid spectral library (with precursor m/z, adduct information, charge states, retention times, and fragmentation patterns) and need to export it for use in either Excalibur-based DDA experiments on an Orbitrap mass spectrometer, or in Skyline for targeted.
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57. ▌
    Data Normalization In Mass Spectrometry 2 · holobiomicslab

    Use when you have raw or partially processed metabolomics data (mzML/mzXML format) from LC-MS or GC-MS runs and need to apply standardized feature detection, alignment, and intensity normalization as part of a reproducible workflow.
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58. ▌
    Fda Repeatability Compliance Assessment 2 · holobiomicslab

    Use when you have completed NMR data quality control analysis and possess per-feature CV values, and you need to formally assess whether the metabolomic dataset meets FDA regulatory standards for downstream biomarker discovery or quantitative assays.
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59. ▌
    Formula Assignment In Mass Spectrometry 2 · holobiomicslab

    Use when you have m/z values from mass spectrometry imaging (or similar MSI experiments) and need to assign molecular formulae to them. This is especially valuable when working with spatially-resolved metabolomics data where annotation precision lags behind traditional LC-MS approaches.
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60. ▌
    Fragmentation Pattern Spectral Matching 2 · holobiomicslab

    Use when when you have detected m/z values from LC-IM-MS/MS that match a candidate CCS prediction database but require structural confirmation. Apply this skill to disambiguate isomers (e.
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61. ▌
    Injection Order Direction Specification 2 · holobiomicslab

    Use when when configuring a multi-well plate design (96-well, 384-well, or other format) in InjectionDesign for LC/GC-MS analysis and you need to specify whether analytical samples and QC controls should be injected row-by-row or column-by-column.
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62. ▌
    Knowledge Data Driven Layer Integration 2 · holobiomicslab

    Use when you have untargeted metabolomics data (MS/MS spectra) and need to annotate metabolites at scale.
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63. ▌
    Lipid Derivatization Chemistry Modeling 2 · holobiomicslab

    Use when you have N-methyl-derivatized unsaturated sterol lipid structures (as SMILES or molecular formula) and need to predict their MS/MS fragmentation behavior before experimental acquisition, or to build a reference spectral library for isomer-level sterol identification in tissue samples.
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64. ▌
    Lipid Fragmentation Pattern Application 2 · holobiomicslab

    Use when you have an enumerated list of lipid species (identified by class, fatty acid composition, and chain length) and need to generate theoretical precursor m/z values, fragment ion masses, and relative intensities for targeted or untargeted lipidomics experiments.
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65. ▌
    Mass Spectrometry Feature Deconvolution 2 · holobiomicslab

    Use when you have a peak table from LC-MS peak picking software (e.
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66. ▌
    Metabolite Prediction Pathway Selection 2 · holobiomicslab

    Use when when you have a small-molecule structure (SMILES, MOL, or SDF format) and need to predict its metabolic fate across one or more biological systems.
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67. ▌
    Metabolomics Data Integration With Xcms 2 · holobiomicslab

    Use when you have untargeted LC-MS metabolomics data preprocessed with XCMS and need to filter out low-quality peak integrations that could introduce false positives or noise into metabolite quantification.
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68. ▌
    Metabolomics Feature Quality Assessment 2 · holobiomicslab

    Use when after drift correction and before missing value imputation when your LC-MS peak table contains features with variable detection rates across samples.
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69. ▌
    Molecular Graph Representation Encoding 2 · holobiomicslab

    Use when when you have a collection of molecular structures (as InChI strings, SMILES, or RDKit Mol objects) and need to feed them into a pretrained or transfer-learning neural network that expects both molecular graph topology and structural fingerprints as inputs.
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70. ▌
    Ms Ms Spectrum Annotation Preprocessing 2 · holobiomicslab

    Use when you have raw LC-MS/MS data acquired in Data-Dependent Acquisition (DDA) mode and need to create a labeled training dataset for customized purification model development.
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71. ▌
    Ms Spectra Extraction And Preprocessing 2 · holobiomicslab

    Use when you have raw LC-MS/MS data in mzML or mzXML format and need to: (1) identify the top-abundance MS1 signals in an LC run, (2) compute a single scalar metric (separation efficiency) that summarizes how well compounds are resolved across the chromatogram, and (3) feed that metric into a.
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72. ▌
    Multi Dimensional Sample Stratification 2 · holobiomicslab

    Use when you have a parsed sample list with metadata (sample IDs, classification dimensions, QC designations) and need to generate a physical injection-plate layout that (1) will be run across multiple batches, (2) has a known confounding dimension (e.
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73. ▌
    Network Based Metabolite Identification 2 · holobiomicslab

    Use when when you have m/z values from spatially-resolved mass spectrometry imaging (MSI) and need to predict their molecular formulae with high precision.
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74. ▌
    Network Graph Re Annotation Propagation 2 · holobiomicslab

    Use when when you have completed an initial ModiFinder analysis on a compound pair (known compound + modified analog with unknown structure), and you subsequently acquire or determine the structure of the modified compound.
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75. ▌
    Neural Network Projection Module Design 2 · holobiomicslab

    Use when when you have 512-dimensional (or other fixed-size) representation vectors output from paired encoders processing augmented versions of the same input (e.
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76. ▌
    Nontargeted Analysis Workflow Execution 2 · holobiomicslab

    Use when you have UPLC-HRMS data from ThermoFisher, Agilent, or other vendor instruments (converted via MSConvert if needed), organized as batch-processed files ready for MSThunder input, and you need to identify unknown organic pollutants with deep learning-assisted structure prediction and.
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77. ▌
    Nps Structural Diversity Stratification 2 · holobiomicslab

    Use when you have a trained PS2MS deep learning model, a set of evaluation compounds (especially novel NPS analogues), and want to understand whether prediction confidence (softmax probabilities, uncertainties) degrades gracefully or sharply as structural distance from the training set increases.
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78. ▌
    Numpress Compression Algorithm Encoding 2 · holobiomicslab

    Use when you have raw floating-point m/z and intensity arrays extracted from mass-spectrometry experiments (e.g., from mzML or mzXML files) and need to compress them for storage or transmission.
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79. ▌
    Peak Boundary Definition And Validation 2 · holobiomicslab

    Use when after nontargeted peak detection has identified candidate peaks in LC-MS chromatograms, when you need to establish exact peak start/end retention times and extract peak-level metadata (intensity, width, shape) for downstream feature quality evaluation and annotation workflows.
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80. ▌
    Qc Coefficient Of Variation Calculation 2 · holobiomicslab

    Use when after extracting NMR spectra and designating replicate QC samples (typically 10 samples run throughout the study), calculate CV for each metabolite feature to assess which signals are reproducible enough for downstream metabolite-phenotype association testing.
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81. ▌
    Quantum Chemistry Structure Preparation 2 · holobiomicslab

    Use when when you have a collection of N-Me derived unsaturated sterol lipid identifiers or structures and need to generate predicted collision cross section (CCS) values for LC-IM-MS/MS analysis.
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82. ▌
    Regression Model Evaluation Mae Metrics 2 · holobiomicslab

    Use when when you have trained a regression model on experimental retention times or similar continuous molecular property predictions and need to quantify its generalization performance on held-out test data.
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83. ▌
    Retention Time Alignment Across Batches 2 · holobiomicslab

    Use when you have extracted peaks from multiple LC/HRMS batches (n > 500 samples across different analytical runs or days) and observe systematic retention time drift or offset between batches, preventing reliable cross-batch peak matching on m/z and RT alone.
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84. ▌
    Signal Acquisition Error Quantification 2 · holobiomicslab

    Use when you have processed the same GC–MS dataset (m/z vs retention time) through two independent signal acquisition pipelines and need to compute and compare their detection accuracy. Use this skill when one method (e.
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85. ▌
    Signal To Noise Ratio Calculation Peaks 2 · holobiomicslab

    Use when after elution peaks have been detected on composite mass tracks using local maxima and prominence detection, before reporting features in the final feature table.
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86. ▌
    Spatial Metabolomics Feature Annotation 2 · holobiomicslab

    Use when your spatial metabolomics dataset contains raw m/z features (e.g., from MALDI-MS imaging or LC-MS/MS) without metabolite annotations, and you have selected a reference database and adduct type appropriate for your ionization mode and biological sample.
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87. ▌
    Spectral Data Input Handling Mgf Format 2 · holobiomicslab

    Use when when you have raw MS/MS mass spectrometry data and need to submit it to the Mass2SMILES Docker inference container for structure and functional group prediction.
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88. ▌
    Spectral Overlay Rendering Multi Sample 2 · holobiomicslab

    Use when when you have aligned peak-alignment data from a preceding molecular networking task (structured as a table with peak intensity, m/z, retention time, and alignment quality metrics) and need to visualize and interactively filter peaks across multiple spectra to support comparative mass.
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89. ▌
    Tensorflow Cpu Runtime Parameter Tuning 2 · holobiomicslab

    Use when deploying Mass2SMILES on a TensorFlow-CPU build and you need to optimize inference throughput on multi-core systems. This is particularly necessary when GPU inference is unavailable due to CUDA driver incompatibility, or when inference hardware has variable core counts (e.
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90. ▌
    Tof Ms Signal Enhancement Low Abundance 2 · holobiomicslab

    Use when your raw TOF-MS data (Agilent MassHunter .d format) exhibits jagged, artifact-prone peaks in low-abundance ions that compromise peak quality assessment or when you need to improve signal-to-noise before ion mobility demultiplexing or peak deconvolution.
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91. ▌
    Training Data Loading And Preprocessing 2 · holobiomicslab

    Use when you have downloaded raw LC-MS spectral peak data from a public repository (e.g., DOI 10.25345/C5FD2F) and need to ingest it into memory and prepare it in the format expected by a TensorFlow/Keras neural network classifier.
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92. ▌
    Transformer Architecture Implementation 2 · holobiomicslab

    Use when when building a neural network to map between mass spectrometry spectra and molecular properties (e.g., fingerprints, SMILES, or fragment ions) where sequential or spectral feature dependencies must be captured.
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93. ▌
    Untargeted Metabolomics Data Processing 3 · holobiomicslab

    Use when you have untargeted MS2 spectral data (from LC-MS/MS or similar instruments) and need to assign metabolic pathway context to detected compounds when standard spectral library matching is unavailable or insufficient.
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94. ▌
    Graph Neural Network Implementation 2 · holobiomicslab

    Use when when your input includes molecular structures (SMILES, conformers) and you need to predict a continuous property (e.g., CCS, binding affinity, solubility) that depends on molecular connectivity and spatial relationships.
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95. ▌
    Jupyter Notebook Development Python 2 · holobiomicslab

    Use when when building reproducible Python-based computational workflows that must serve both beginner and expert users; when the analysis requires interactive parameter tuning, file upload capability, or real-time result visualization;
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96. ▌
    Jupyter Notebook Workflow Execution 2 · holobiomicslab

    Use when you have a curated training dataset of molecular structures with known CCS values, a target set of ≤10,000 molecules requiring CCS predictions, and need to apply a pre-configured Sklearn-based machine learning model within a reproducible, browser-accessible environment.
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97. ▌
    Metabolite False Positive Filtering 2 · holobiomicslab

    Use when you have ion-mobility mass spectrometry metabolomics data with putative metabolite identifications (e.g., from database matching) and want to reduce false positives by cross-validating compound identities against machine-learned CCS predictions.
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98. ▌
    Chemical Space Similarity Computation 2 · holobiomicslab

    Use when you have retrieved multiple candidate structures from a molecular structure database (e.g., PubChem, HMDB) for an unknown compound, and you have predictions of structurally related metabolites from a deep-learning mass spectral model (e.g., DeepMASS2).
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99. ▌
    Classification Performance Evaluation 2 · holobiomicslab

    Use when after running inference on test mass spectrometry spectra with a trained deep learning model (e.g., PS2MS) to verify that class label predictions and confidence scores match expected reference outputs.
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100. ▌
     Interactive Filter Control Design Web 2 · holobiomicslab

     Use when when you have a web-based visualization of aligned mass spectrometry peaks (m/z, intensity, retention time, alignment quality metrics) and need users to interactively explore subsets of those peaks by applying constraints on intensity thresholds, alignment score cutoffs, or peak.
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