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HolobiomicsLab

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1. ▌
   Floating Point Numerical Accuracy Assessment 2 · holobiomicslab

   Use when when implementing or validating a lossy numeric codec for mass-spectrometry data (e.g., MSNumpressCoder in OpenMS). Specifically: after implementing both encoder and decoder, before shipping to production, or when comparing alternative compression schemes.
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   Fragmentation Pattern Extraction And Ranking 2 · holobiomicslab

   Use when you have a collection of MS/MS spectra (≥2 spectra) and wish to identify fragmentation signatures common to subsets of those spectra.
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   Gibbs Sampler Implementation And Convergence 2 · holobiomicslab

   Use when your metabolomics dataset contains missing values below a known detection limit (left-censored MNAR data), and you need to recover these values while respecting the truncation constraint.
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   Graph Based Metabolite Similarity Assessment 2 · holobiomicslab

   Use when you have a collection of MS/MS spectra (stored as Spectrum2 objects in an ms2Lib class) and need to identify which spectra share identical fragmentation patterns—particularly when coupled to a GNPS molecular network to focus on explaining network components (connected components, cliques.
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5. ▌
   Intensity Dependent Missing Value Simulation 2 · holobiomicslab

   Use when augmenting mass spectrometry ion images in ISO mode (isotope ions from the same molecule) and you need to simulate intensity-dependent data loss that reflects real detector behavior where lower-intensity pixels are more likely to be missed or undetected.
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6. ▌
   Internal Standardization With Isotope Labels 2 · holobiomicslab

   Use when your IM-MS lipidomics samples have been spiked with fully labeled isotopic internal standards (e.
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7. ▌
   Ion Mobility Saturation Detection And Repair 2 · holobiomicslab

   Use when preprocessing raw Agilent MassHunter (.d) or UIMF IM-MS data files that exhibit signal saturation—ion intensity clipping caused by detector or amplifier limits—which distorts peak shape and abundance estimates across the m/z and drift-time axes.
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8. ▌
   Marker Feature Identification And Validation 2 · holobiomicslab

   Use when when processing GC–MS or LC–MS data as m/z vs retention time chromatograms and you need to identify biomarker or chemical marker features without conventional peak picking, particularly when false positive detection rates from peak detection algorithms are problematic.
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9. ▌
   Metabolite Concentration To Spectrum Mapping 2 · holobiomicslab

   Use when when you have a list of known metabolite concentrations and their corresponding J-coupling constants (spin systems) and need to generate realistic 1D 1H NMR spectra or 2D correlation spectra (COSY, HSQC, HMQC) for simulation, validation, or educational purposes, without access to actual.
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10. ▌
    Missing Value Imputation For Column Metadata 2 · holobiomicslab

    Use when when preparing raw HPLC column parameter arrays for featurization into feature vectors for retention time prediction models. Specifically apply this skill when column metadata contains empty strings (indicating missing diameter or pH values) or non-standard string encodings (e.g., '2.
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11. ▌
    Molecular Fingerprint Bit Frequency Analysis 2 · holobiomicslab

    Use when you have loaded a collection of molecular fingerprint vectors (e.g., from biosynfoni fingerprints deposited in Zenodo) and need to assess their statistical properties before using them for classification, similarity search, or method validation.
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12. ▌
    Multiple Linear Regression Model Application 2 · holobiomicslab

    Use when when you have an observed m/z value from mass spectrometry imaging and need to annotate it with a ranked list of candidate chemical formulae. Apply this skill when the KnownSet database (2.
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13. ▌
    Non Targeted Feature Detection And Screening 2 · holobiomicslab

    Use when you have raw LC/MS data in mzML format and your analysis goal is to comprehensively detect and annotate all mass spectral features present, rather than measuring predefined target analytes.
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14. ▌
    Peak Quality Metric Interpretation Alignment 2 · holobiomicslab

    Use when when you have loaded aligned peak-alignment data from a molecular networking task and need to distinguish high-confidence, reproducible peak alignments from noise or spurious matches.
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15. ▌
    Pipeline End To End Execution And Validation 2 · holobiomicslab

    Use when you have a published computational pipeline with deposited code and validation data, and you need to verify that the pipeline can be executed end-to-end to reproduce reported validation metrics (annotation accuracy, coverage, or equivalent performance benchmarks).
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16. ▌
    Pytorch Model Instantiation And Forward Pass 2 · holobiomicslab

    Use when after defining a multi-branch neural network architecture (e.
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17. ▌
    Quality Control Metric Distribution Analysis 2 · holobiomicslab

    Use when after composite-map peak detection has produced an unfiltered peak list with SNR, peakshape (goodness_fitting), peak_height, and prominence values.
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18. ▌
    Retention Time Alignment Mapping Application 2 · holobiomicslab

    Use when after mass tracks have been aligned across samples into a MassGrid structure and retention time calibration dictionaries (rt_cal_dict) have been computed for each sample, but before summing intensity vectors element-wise to construct the composite map.
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19. ▌
    Retention Time Calibration Lowess Regression 2 · holobiomicslab

    Use when after mass track construction and before composite map building, when you need to align retention times across multiple LC-MS samples. Trigger conditions: (1) you have identified high-selectivity landmark peaks (mSelectivity > 0.99) in a reference sample;
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20. ▌
    Retention Time Feature Distribution Analysis 2 · holobiomicslab

    Use when you have extracted retention times from top MS1 features in an LC-MS/MS experiment and need to assess whether the gradient configuration (start and end time in minutes) achieves adequate compound separation across the full chemical space.
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21. ▌
    Sample Metadata Stratification And Filtering 2 · holobiomicslab

    Use when when you have retrieved a large, heterogeneous collection of tandem MS files from ReDU or MassIVE and need to isolate a subset sharing specific sample characteristics (e.
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22. ▌
    Sample To Reference Retention Time Remapping 2 · holobiomicslab

    Use when after mass tracks have been aligned across samples into a MassGrid structure and retention time calibration dictionaries (rt_cal_dict) have been computed for each sample during prior alignment steps.
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23. ▌
    Sterol Structure Representation And Curation 2 · holobiomicslab

    Use when when you have a collection of N-Me derivatized unsaturated sterol structures from tissue samples or standards that must be fed into MS/MS fragmentation prediction or collision cross section (CCS) prediction workflows.
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24. ▌
    Tandem Mass Spectrometry Data Interpretation 2 · holobiomicslab

    Use when you have high-resolution MS2 data (.ms2 format) from tandem mass spectrometry analysis of lipid A-containing samples and need to perform automated structure annotation and identification at systems scale.
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25. ▌
    Spectral Peak Detection And Alignment 2 · holobiomicslab

    Use when after noise filtering and baseline correction have been applied to mass spectrometry data (DI-MS, ASAP-MS, LDI-MS, or other high-throughput MS formats in mzML, mzXML, or vendor formats).
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26. ▌
    U13c Labeled Standard Reference Matching 2 · holobiomicslab

    Use when you have IM-MS measurements of samples spiked with U13C-labeled internal standards (e.g., fully labeled yeast extract) and need to assess whether measured CCS values systematically deviate from their true reference values.
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27. ▌
    Raw Spectral Data Import And Preprocessing 2 · holobiomicslab

    Use when you have raw metabolomics data in mzML or mzXML format and need to convert it into a normalized feature table (CSV or mzTab) via automated batch processing.
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28. ▌
    Computational Reproducibility Verification 2 · holobiomicslab

    Use when you have access to both raw data (deposited in a repository like Zenodo) and analysis scripts (in a GitHub repository), and you need to confirm that the published figures, tables, or quantitative findings are reproducible.
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29. ▌
    Ion Image Augmentation Intensity Dependent 2 · holobiomicslab

    Use when training a contrastive encoder on mass spectrometry imaging (MSI) data in ISO mode (isotope ions from the same molecule).
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30. ▌
    Iterative Peak Selection From Latent Space 2 · holobiomicslab

    Use when you have latent low-dimension peak features extracted by a Graph-attention autoencoder from imaging mass spectrometry (IMS) datasets, and you need to automatically identify a ranked subset of marker ions without manual inspection.
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31. ▌
    Search Result Visualization Across Domains 2 · holobiomicslab

    Use when you have executed batch searches of MS/MS spectra against multiple domain-specific MASST indices and need to synthesize results across domains (e.
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32. ▌
    Tissue Spatial Analysis Pipeline Execution 2 · holobiomicslab

    Use when you have raw IMC (protein imaging) and SIMS (metabolite imaging) data from tissue regions that require spatial co-registration, single-cell-level intensity quantification, and joint analysis of protein–metabolite relationships.
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33. ▌
    Large Scale All Pairs Similarity Benchmarking 2 · holobiomicslab

    Use when you have multiple competing spectral similarity scoring methods (e.
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34. ▌
    Word2vec Embedding Training Mass Spectrometry 2 · holobiomicslab

    Use when you have a large collection of preprocessed MS/MS spectra (typically >10,000 spectra) with diverse chemical structures and you need to learn embeddings that capture fragmentation patterns and neutral loss relationships.
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35. ▌
    Col Mode Augmentation Pipeline Implementation 2 · holobiomicslab

    Use when when you have preprocessed mass spectrometry imaging (MSI) ion images and need to generate augmented image pairs for contrastive learning in co-localized ion discovery tasks.
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36. ▌
    Comparative Classifier Performance Assessment 2 · holobiomicslab

    Use when you have a labeled peak quality dataset (development set with ground-truth pass/fail labels), a defined set of peak-quality metrics (e.
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37. ▌
    Feature Network Construction And Partitioning 2 · holobiomicslab

    Use when you have a preprocessed LC-MS feature table (m/z, retention time, intensity columns) and need to identify which features belong together as isotopes or adducts of the same neutral compound.
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38. ▌
    Lc Ms Gradient Encoding Vector Representation 2 · holobiomicslab

    Use when when you have a set of candidate LC gradients (parameter combinations) that you wish to evaluate with a Gaussian process model, or when you need to convert raw gradient specifications into a standardized numerical format for Bayesian optimization acquisition function computation.
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39. ▌
    Mass Spectrum Normalization And Preprocessing 2 · holobiomicslab

    Use when you have raw tandem mass spectra data (mz/intensity pairs and precursor m/z values) and need to train interpretable machine learning models (regression or tree-based) where feature interpretability and direct chemical meaning are required.
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40. ▌
    Metadata Extraction From Fixed Offset Records 2 · holobiomicslab

    Use when you have a binary file (e.g., NV format) with a known fixed-size header block (e.
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41. ▌
    Oracle Mode Structural Constraint Integration 2 · holobiomicslab

    Use when when you have loaded both a known compound and its modified analog with MS/MS spectra, initially generated baseline modification probability scores, and then obtained or confirmed the structure of the modified compound.
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42. ▌
    Package Installation Verification And Testing 2 · holobiomicslab

    Use when a Python package has been relocated to a new repository location, reorganized to conform to new organizational standards (e.g., metabolomics-cloud conventions), or its dependencies, metadata, or CI/CD workflows have been modified.
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43. ▌
    Pairwise Alignment With Anchor Prioritization 2 · holobiomicslab

    Use when when processing LC-MS metabolomics datasets with 10 or fewer samples and requiring reproducible mass track alignment across the cohort.
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44. ▌
    Smiles String Translation And Standardization 2 · holobiomicslab

    Use when you have raw SMILES strings from multiple external database sources (e.g., PubChem, ChEMBL, vendor databases) that need to be integrated into a unified structure registry. Indicators include: (1) raw SMILES table exists at a known interim input path (e.
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45. ▌
    Transformer Input Representation Construction 2 · holobiomicslab

    Use when you have variable-length MS/MS peak lists (m/z arrays and intensity arrays) that must be fed into a transformer architecture for tasks like compound identification or spectral clustering.
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46. ▌
    Untargeted Metabolomics Marker Identification 2 · holobiomicslab

    Use when when you have untargeted GC–MS or LC–MS data in the form of a two-dimensional m/z vs retention time map and need to identify marker features without conventional peak picking.
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47. ▌
    Regression Performance Metric Computation 2 · holobiomicslab

    Use when after generating collision cross section predictions on a validation or test set using a trained graph neural network model, and you need to quantify prediction accuracy and compare against reported performance metrics in the literature or prior experimental runs.
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48. ▌
    Signal Noise Ratio Improvement Validation 2 · holobiomicslab

    Use when after executing multidimensional smoothing, spike removal, or saturation repair on raw TOF-MS or IM-MS data (.d format from Agilent MassHunter) to confirm that signal quality has improved.
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49. ▌
    Deep Learning Model Training And Validation 2 · holobiomicslab

    Use when you have paired mass-spectrometry spectral data (m/z and intensity arrays) with known molecular fingerprints or InChIKeys, and need to train a supervised deep learning model to predict fingerprints for novel spectra.
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50. ▌
    Laser Ablation Isotope Image Interpretation 2 · holobiomicslab

    Use when you have imported a raw LA-ICP-MS raster image (line-by-line, spot-wise, or ablation-time-aligned format) and need to isolate tissue regions from instrumental background or air before quantifying regional elemental abundance.
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51. ▌
    Molecular Structure Representation Learning 2 · holobiomicslab

    Use when when you have paired mass spectra and molecular structure data and need to train a model that can bidirectionally map between experimental spectra and chemical structures.
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52. ▌
    Structural Identifier Completeness Checking 2 · holobiomicslab

    Use when preprocessing open mass spectrometry libraries (OMSLs) or aggregated spectral datasets where structural identifiers are inconsistently populated.
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53. ▌
    Ion Image Augmentation Contrastive Learning 2 · holobiomicslab

    Use when when you have preprocessed mass spectrometry ion images (single-channel 2D arrays or multi-channel spectral images) and need to train a self-supervised encoder to learn low-dimensional representations for downstream tasks such as co-localized ion discovery (COL mode) or isotope ion.
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54. ▌
    Word2vec Model Inference Unknown Word Handling 2 · holobiomicslab

    Use when when applying a pre-trained Word2Vec model to mass spectra at inference time (e.g., library matching or molecular networking), especially when the query spectra may contain fragment peaks or neutral losses not represented in the model's training vocabulary.
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55. ▌
    Chemical Structure Sanitization And Validation 2 · holobiomicslab

    Use when you have translated or raw SMILES strings from a chemical structure curation pipeline and need to remove invalid chemical structures, resolve sanitization errors (e.
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56. ▌
    Chimeric Spectra Classification Model Training 2 · holobiomicslab

    Use when when your DDA-mode LC-MS/MS data exhibits chimeric spectra patterns that differ systematically from the reference training set used in DNMS2Purifier, or when you wish to optimize purification sensitivity/specificity for your particular instrument, ionization method, or sample matrix.
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57. ▌
    Chromatographic Peak Quality Metric Evaluation 2 · holobiomicslab

    Use when when processing untargeted LC-MS metabolomics data with XCMS and need to identify low-quality peak integrations that may introduce noise or bias into subsequent compound identification and quantification.
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58. ▌
    Confounder Adjustment Epidemiological Analysis 2 · holobiomicslab

    Use when when testing associations between metabolic features (from NMR or MS) and a phenotype of interest (e.g., BMI, disease status) in a cohort where age, gender, or clinical confounders are known to correlate with both the metabolite and phenotype.
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59. ▌
    Contrastive Learning For Cross Modal Retrieval 2 · holobiomicslab

    Use when you have paired MS/MS spectra and molecular structures (SMILES or SDF format) and need to perform compound identification by retrieving the correct structure for an unknown spectrum.
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60. ▌
    Domain Specific Spectrum Search Implementation 2 · holobiomicslab

    Use when you have acquired one or more tandem MS/MS spectra and need to identify metabolites against a reference library filtered by biological domain (e.
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61. ▌
    Feature Annotation Via Isotope Adduct Grouping 2 · holobiomicslab

    Use when after peak detection and feature extraction have produced a composite feature table with m/z, retention time, and intensity values for individual samples.
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62. ▌
    Fid To Frequency Domain Fourier Transformation 2 · holobiomicslab

    Use when after simulating and convolving individual metabolite multiplets with realistic lineshapes (Lorentzian or Gaussian) and combining them into a single time-domain FID array.
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63. ▌
    Ion Mobility Mass Spectrometry Data Processing 2 · holobiomicslab

    Use when you have IM-MS lipidomics samples spiked with U13C-labeled internal standards (e.
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64. ▌
    Mass Spectrometry Peak Alignment Visualization 2 · holobiomicslab

    Use when when you have aligned peak data from molecular networking (with m/z, intensity, retention time, and alignment quality metrics across multiple spectra) and need to interactively explore peak alignments under multiple filtering criteria (intensity thresholds, alignment score cutoffs, peak.
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65. ▌
    Metabolite Stability Assessment Across Cohorts 2 · holobiomicslab

    Use when you have uploaded a pre-analytical data table containing sample metadata, processing timestamps (pre- and post-centrifugation), and NMR metabolomic measurements for a cohort of peripheral blood samples (plasma/serum), and you need to quantify the magnitude and direction of metabolite.
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66. ▌
    Molecular Identifier Completeness Verification 2 · holobiomicslab

    Use when during MSP, MGF, JSON, or CSV file parsing when standardizing mass spectra from heterogeneous open mass spectral libraries (OMSLs).
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67. ▌
    Ms Ms Spectrum Tokenization And Representation 2 · holobiomicslab

    Use when when you have raw MS/MS spectra in MSP format (or similar) with m/z–intensity peak pairs and need to prepare them for neural embedding models that require fixed-size discrete token inputs. Applies before generating dense spectral embeddings for retrieval or similarity scoring tasks.
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68. ▌
    Multiplexed Spectra Recovery And Deconvolution 2 · holobiomicslab

    Use when you have raw IM-MS data in UIMF or Agilent MassHunter .d format acquired from a multiplexed (interleaved) ion mobility experiment, and you need to recover individual, demultiplexed frames to reconstruct conventional IM-MS spectra for downstream omics analysis.
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69. ▌
    Nearest Neighbor Clustering By Mass Difference 2 · holobiomicslab

    Use when processing LC-MS metabolomics studies with >10 samples where sample count and memory constraints make pairwise mass alignment infeasible.
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70. ▌
    Network Component Identification And Filtering 2 · holobiomicslab

    Use when you have a GNPS GraphML molecular network and need to isolate cohesive subsets of spectra (components) before analyzing which fragmentation patterns explain them.
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71. ▌
    Representation Vector Dimensionality Reduction 2 · holobiomicslab

    Use when after obtaining 512-dimensional representation vectors from the Encoder module, when you need to compress these vectors for visualization, clustering, or downstream classification tasks on mass spectrometry imaging data while maintaining interpretability of ion relationships.
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72. ▌
    Retention Time Regression Output Specification 2 · holobiomicslab

    Use when after initializing and executing a forward pass through a dual-branch RT-Transformer model (combining fingerprint and molecular graph inputs) on a batch of molecular samples, to verify that the output tensor conforms to the expected shape, data type, and numeric range for retention time.
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73. ▌
    Selectivity Metric Computation Chromatographic 2 · holobiomicslab

    Use when after peak detection on composite mass tracks when you need to evaluate whether a detected peak represents a pure, interference-free signal on its m/z channel.
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74. ▌
    Untargeted Metabolomics Feature Interpretation 2 · holobiomicslab

    Use when you have an untargeted metabolomics feature table (m/z values, retention times, p-values from statistical testing) and need to infer which metabolic pathways are active without performing metabolite identification.
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75. ▌
    Workflow Orchestration And Parallelization 2 · holobiomicslab

    Use when when you have a multi-step computational chemistry or molecular modeling pipeline (3+ sequential or parallel stages) that must process many molecules, each requiring repeated tool invocations with different parameters, and you need reproducibility, fault tolerance, and the ability to.
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76. ▌
    Mass Spectra Tokenization Unified Vocabulary 2 · holobiomicslab

    Use when when you have paired mass spectra and molecular structure data and need to train a unified model for structure elucidation. Use this skill at the data preparation stage before pretraining, when you want both modalities to share representational capacity rather than operate in isolation.
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77. ▌
    Metabolomics Feature Detection And Alignment 2 · holobiomicslab

    Use when you have raw metabolomics mass spectrometry data in mzML or mzXML format and need to extract, align, and normalize metabolic features across multiple samples or conditions.
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78. ▌
    Standardized Region Export For Deep Learning 2 · holobiomicslab

    Use when you have LC-HRMS profile-mode data with detected local maxima (from gradient-descent peak finding) and need to prepare them as input for a convolutional neural network trained to classify peaks vs. background signal or to estimate peak boundaries and centers.
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79. ▌
    Feature Metadata Alignment Across Dimensions 2 · holobiomicslab

    Use when you have loaded a feature-by-pixel intensity matrix from an MSI HDF5 container and need to perform dimension-preserving corrections (e.
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80. ▌
    Imaging Mass Spectrometry Ion Identification 2 · holobiomicslab

    Use when you have imaging mass spectrometry data from spatial metabolomics experiments and need to reduce the high-dimensional peak space to a ranked set of marker ions for downstream spatial analysis (e.g., tissue region annotation or biomarker discovery).
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81. ▌
    Mass Spectral Missing Word Fraction Computation 2 · holobiomicslab

    Use when when applying a pre-trained Spec2Vec Word2Vec model to new mass spectra (particularly those outside the model's training distribution), you need to assess whether peaks and neutral losses in query spectra have been seen during model training.
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82. ▌
    Chromatographic Peak Detection And Segmentation 2 · holobiomicslab

    Use when you have raw LC-MS data (mzML or vendor format) and need to discover and characterize all chromatographic features present, without prior knowledge of target analytes.
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83. ▌
    Collision Cross Section Matching And Annotation 2 · holobiomicslab

    Use when when you have LC-IM-MS/MS data with measured collision cross section (CCS) values and m/z assignments, and you need to disambiguate sterol isomers (particularly N-Me derived unsaturated sterols) by matching against a curated database of predicted CCS values and MS/MS fragmentation patterns.
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84. ▌
    Collision Energy Optimization For Fragmentation 2 · holobiomicslab

    Use when when you have N-Me derivatized unsaturated sterol lipid structures (as SMILES or molecular formula) and need to predict MS/MS fragmentation patterns with collision-energy-dependent m/z values and intensities for downstream CCS prediction or LC-IM-MS/MS library matching.
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85. ▌
    Marker Feature Identification Chromatography Ms 2 · holobiomicslab

    Use when when processing raw chromatography–mass spectrometry data (GC–MS or LC–MS) as a 2D m/z vs retention time map and you need to identify and visualize marker features for analyte discrimination without relying on conventional peak picking.
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86. ▌
    Mass Spectrometry Data Structure Interpretation 2 · holobiomicslab

    Use when you have converted multidimensional MS data (from Agilent .d, Bruker ion mobility .d, Thermo .
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87. ▌
    Mass Track Construction From Centroided Spectra 2 · holobiomicslab

    Use when when you have centroided mzML files from LC-MS metabolomics and need to construct high-mass-resolution mass tracks for each sample before alignment. Apply this skill at the start of an untargeted metabolomics workflow, before building a cross-sample MassGrid.
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88. ▌
    Metabolite Candidate Ranking Likelihood Scoring 2 · holobiomicslab

    Use when you have a query mass spectrum matched to multiple candidate metabolites (by accurate mass, database lookup, or spectral similarity), and you possess or can train a DNN model for retention time prediction on your target chromatographic method.
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89. ▌
    Molecular Conformer Generation And Optimization 2 · holobiomicslab

    Use when you have SMILES strings or 2D molecular structures of N-Me derived unsaturated sterol lipids (or other C=C-containing molecules) and need to generate 3D conformational and electronic structure data as input to a machine-learning CCS prediction model.
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90. ▌
    Molecular Duplicate Detection And Deduplication 2 · holobiomicslab

    Use when after SMILES standardization when you have a table of translated SMILES strings (e.g., interim/tables/1_translated/structure/smiles.tsv.
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91. ▌
    Molecular Network Construction For Metabolomics 2 · holobiomicslab

    Use when you have untargeted metabolomics data (e.g., LC-MS/MS spectra) and need to organize compounds by structural relatedness to enable structure discovery for unknown metabolites.
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92. ▌
    Organism Specific Metabolism Pathway Assignment 2 · holobiomicslab

    Use when you have observed metabolites (from LC-MS/MS, chromatography, or spectroscopy) whose identities are unknown, and you wish to constrain the candidate pool by leveraging organism-specific metabolism predictions.
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93. ▌
    Performance Metric Extraction From Publications 2 · holobiomicslab

    Use when you have identified a claim that one tool outperforms another (e.g., 'IDSL.IPA outperforms MZmine 2 and xcms') in a research article's abstract or introduction, but the specific metrics and results table are not included in the introductory text.
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94. ▌
    Transformer Encoder Architecture Implementation 2 · holobiomicslab

    Use when when processing sequential spectroscopic data (1H NMR spectra) where both local chemical shift patterns and global spectral dependencies are needed for compound classification.
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95. ▌
    Untargeted Metabolomics Workflow Implementation 2 · holobiomicslab

    Use when you have LC-MS/MS data acquired in DDA mode from untargeted metabolomics experiments and need to remove chimeric (co-fragmented) MS/MS spectra that result from multiple precursor ions fragmented simultaneously.
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96. ▌
    Composite Mass Track Summation Across Samples 2 · holobiomicslab

    Use when after mass tracks have been aligned across samples into a MassGrid structure (m/z-aligned, same mass-to-charge ratio) and retention time calibration dictionaries have been computed for each sample.
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97. ▌
    Molecular Fingerprint Representation Learning 2 · holobiomicslab

    Use when when you have labeled mass-spectrometry spectral data (precursor m/z and fragment m/z–intensity pairs) paired with known molecular structures (as InChIKeys or SMILES), and need to perform metabolite annotation by ranking candidate compounds based on spectral similarity.
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98. ▌
    Signal To Noise Filtering For Peak Candidates 2 · holobiomicslab

    Use when immediately after peak detection in the IDSL.IPA workflow, when you have a list of candidate peaks extracted from EIC data and need to remove noise-dominated signals before downstream peak property evaluation and annotation.
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99. ▌
    Mass Spectrometry Imaging Data Representation 2 · holobiomicslab

    Use when you have raw mass spectrometry imaging data (2D or 3D spatial coordinates with full mass-to-charge spectra) and want to train a probabilistic deep learning classifier for tumor delineation or tissue classification.
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100. ▌
     Chromatography Mass Spectrometry Data Processing 2 · holobiomicslab

     Use when when you have raw GC–MS or LC–MS data (m/z vs retention time chromatography-mass spectrometry maps) and need to identify analyte signals and marker features without conventional peak picking;
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