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HolobiomicsLab

@holobiomicslab source repo

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1. ▌
   Kegg Pathway Prediction From Spectra 2 · holobiomicslab

   Use when you have untargeted MS2 spectral data (in MS2MP-compatible format) and need to assign KEGG pathway annotations to unknown metabolites.
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   Left Censored Missingness Simulation 2 · holobiomicslab

   Use when when you have a complete metabolomics abundance table (e.g., targeted LC/MS or untargeted GC/MS counts) and need to generate synthetic left-censored missingness for evaluating imputation algorithm performance.
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3. ▌
   Lineshape Convolution And Broadening 2 · holobiomicslab

   Use when after generating theoretical spin multiplets for individual metabolites via first-order or density-matrix NMR simulation, but before combining spectra or applying Fourier transformation.
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4. ▌
   M Z Alignment And Mass Grid Assembly 2 · holobiomicslab

   Use when when processing multiple centroided mzML LC-MS files from the same study and you need to identify which mass tracks represent the same metabolite across samples.
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5. ▌
   Mass Spectrometry Data Normalization 2 · holobiomicslab

   Use when when raw MS/MS spectra from GNPS or similar databases contain variable-scale peak intensities, missing metadata, or inconsistent m/z calibration, and you intend to feed peak information into a transformer-based spectral embedding model that expects normalized, fixed-length tensor inputs.
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6. ▌
   Mass Spectrometry Data Preprocessing 3 · holobiomicslab

   Use when you have raw LCMS data in mzML or mzXML format from DDA, DIA, or fullscan analyses and need to extract metabolite features with unified m/z, retention time, and intensity values across multiple samples before performing MS2 annotation or in-source fragment analysis.
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7. ▌
   Mass Spectrometry Spectral Alignment 2 · holobiomicslab

   Use when you have a pair of MS/MS spectra—one from a known compound and one from a structurally modified variant of that compound—and you need to identify which atoms in the structure likely bear the modification.
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8. ▌
   Massgrid Construction And Validation 2 · holobiomicslab

   Use when after individual mass tracks (EICs) have been extracted from each sample's mzML file and you need to create a unified, cross-sample m/z reference structure. Triggered when: (1) you have ≥2 samples in a cohort; (2) mass tracks have been binned at 0.
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9. ▌
   Metabolic Network Graph Construction 2 · holobiomicslab

   Use when when you have an untargeted metabolomics feature table (m/z values, retention times, intensities) and aim to predict functional pathway activity without explicit metabolite identification.
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10. ▌
    Metabolite Quality Metric Assessment 2 · holobiomicslab

    Use when after drift correction has been applied to a MetaboSet object, and before imputation and batch correction.
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11. ▌
    Metabolomics Data Structure Handling 2 · holobiomicslab

    Use when you have metabolomics data already formatted as a SummarizedExperiment object containing multiple assays (raw counts, log-transformed, imputed, or normalized versions) and need to access specific assay layers alongside batch annotation metadata to perform comparisons (e.
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12. ▌
    Metabolomics Intensity Normalization 2 · holobiomicslab

    Use when your input is a raw metabolomics intensity matrix (compounds × samples) with known batch assignment and QC sample labels, and you observe signal drift across the analytical sequence or batch-to-batch variation.
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13. ▌
    Missing Data Mechanism Specification 2 · holobiomicslab

    Use when when you have a metabolomics abundance table with missing values and need to decide which imputation method to apply, or when designing a simulation to evaluate imputation performance.
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14. ▌
    Ms Adduct Annotation And Recognition 2 · holobiomicslab

    Use when when processing LC-MS peak tables from isotope tracing experiments where multiple ionization adducts ([M+H]+, [M+Na]+, [M+NH4]+, etc.) and in-source fragments have generated redundant features at different m/z values that represent the same underlying metabolite.
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15. ▌
    Neural Network Hyperparameter Tuning 2 · holobiomicslab

    Use when when training a multilayer perceptron to predict metabolomic features from microbiome abundances, you need dataset-specific hyperparameter configurations because prediction performance (Spearman correlation and well-predicted metabolite counts) varies substantially across microbiome.
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16. ▌
    Nmr Metabolite Identity Confirmation 2 · holobiomicslab

    Use when you have preprocessed 1H NMR spectral data with an unknown or ambiguous peak (e.g., at a specific chemical shift δ), and you need to determine its chemical identity.
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17. ▌
    Organism Count Distribution Analysis 2 · holobiomicslab

    Use when you have a flat file of structure-organism pairs (e.
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18. ▌
    Parent Product Relationship Tracking 2 · holobiomicslab

    Use when when you have applied biotransformation rules to generate candidate product structures from a set of input molecules (represented as SMILES strings) and need to document which product structures were derived from which parent structures.
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19. ▌
    Peak Quality Classifier Optimization 2 · holobiomicslab

    Use when after calculating 12 peak-quality metrics on a development set of extracted ion chromatograms (EICs) and labeled peaks, when you need to select both the classification algorithm and its optimal hyperparameters before training a final model on held-out test data.
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20. ▌
    Plate Layout Parameter Configuration 2 · holobiomicslab

    Use when after uploading a sample list to InjectionDesign and before performing inter-batch balancing and intra-batch randomization.
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21. ▌
    Ppm Error Validation In Metabolomics 2 · holobiomicslab

    Use when when annotating m/z features from Cardinal MSImagingExperiment objects or LC-MS datasets against metabolite databases (HMDB, Lipidmaps) and you need to exclude matches where the mass difference exceeds your instrumental accuracy or analysis tolerance.
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22. ▌
    Prominence Controlled Peak Selection 2 · holobiomicslab

    Use when after initial peak detection on composite mass tracks via local maxima and smoothing, when you have unfiltered peak lists (JSON or structured format) containing prominence values and need to reduce the number of detected features while maintaining signal quality.
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23. ▌
    Repository Structure Standardization 2 · holobiomicslab

    Use when a scientific Python package is being moved to a new GitHub organization with different structural conventions (e.g., from a personal lab account to a community-led organization like metabolomics-cloud or scverse).
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24. ▌
    Retention Time Correlation Filtering 2 · holobiomicslab

    Use when you have detected multiple LC-MS features (m/z peaks) across a chromatogram and need to distinguish true chemical relationships (isotopes differing by 1.003 Da, adducts with characteristic mass shifts, neutral loss fragments) from noise or unrelated peaks.
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25. ▌
    Rsd Threshold Parameter Optimization 2 · holobiomicslab

    Use when when preparing XCMS peak tables for quality classification and you observe that the default RSD threshold (0.3 or 30%) is either too permissive (retaining noisy EICs) or too stringent (discarding valid signals).
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26. ▌
    Spectral Library Matching Annotation 2 · holobiomicslab

    Use when you have MS2 spectral data (precursor m/z, retention time, and fragment ion patterns) from UPLC-HRMS analysis of environmental or biological samples and need to assign compound identities by comparing against known reference spectra.
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27. ▌
    Spectral Neutral Loss Transformation 2 · holobiomicslab

    Use when when comparing two or more MSMS spectra and you need to emphasize structural relationships revealed by neutral losses (mass differences between precursor and fragment ions) rather than absolute m/z values.
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28. ▌
    Sterol Lipid Isomer Characterization 2 · holobiomicslab

    Use when you have LC-IM-MS/MS experimental data (raw mzML or vendor format) containing signals from N-Me derived unsaturated sterol lipids and need to assign double-bond positions and stereochemistry to individual sterol isomers rather than sum compositions.
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29. ▌
    Tsv CSV File Parsing And Aggregation 2 · holobiomicslab

    Use when you have TSV or CSV files containing structure-organism pairs (with columns for structure identifier and organism identifier) and need to count unique pairs, unique structures, unique organisms, or enumerate source database identifiers to validate or report dataset scale.
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30. ▌
    Web Application Documentation Mining 2 · holobiomicslab

    Use when you have access to a project README or repository documentation (Zenodo deposit, GitHub, or local clone) describing multiple domain-specific web applications, and you need to produce a machine-readable inventory of those applications with verified live URLs and associated publications for.
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31. ▌
    Ion Mobility Feature Engineering 2 · holobiomicslab

    Use when you have a set of metabolite structures (or their molecular descriptors) and need to construct training or target feature matrices for CCS prediction. Specifically, use it when you are preparing data to fit or apply a machine learning model (e.g., Sklearn v1.0.
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32. ▌
    Polarizability Tensor Extraction 2 · holobiomicslab

    Use when after ASE-ANI has filtered conformers to remove high-energy geometries, and you need to compute electronic properties required for CCS prediction.
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33. ▌
    Database Record Conditional Update 2 · holobiomicslab

    Use when when processing mass spectrometry spectral records from a database where critical fields (e.g., adduct annotation) are absent or null, and you have a secondary field (e.g., ionmode: 'pos'/'neg') that can deterministically populate the missing field.
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34. ▌
    Recursive Algorithm Implementation 2 · holobiomicslab

    Use when after peak detection on LC/HRMS data (mzXML, mzML, netCDF formats) when m/z values require refinement for improved mass accuracy in untargeted metabolomics workflows.
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35. ▌
    Autoencoder Encoder Decoder Design 2 · holobiomicslab

    Use when when working with imaging mass spectrometry (IMS) datasets where you need to extract latent low-dimensional peak features from high-dimensional peak intensity data while preserving spatial adjacency information.
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36. ▌
    Imaging Data Workspace Integration 2 · holobiomicslab

    Use when you have paired cdf files (raw mass spectrometry imaging data) and Matlab workspace (.mat) files for the same root sample, and you need to reproduce published linear-axis imaging analysis results (e.g., per-root mass spectrometry imaging metrics along a developmental or spatial axis).
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37. ▌
    Keras Tensorflow Model Compilation 2 · holobiomicslab

    Use when you have defined a Keras model architecture (convolutional and dense layers) accepting raw mass spectrometry imaging data tensors and need to prepare it for training on tumor/non-tumor probabilistic classification without prior peak picking.
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38. ▌
    Msi Intensity Matrix Normalization 2 · holobiomicslab

    Use when after isotope correction when you have extracted intensity matrices from imzML or HDF5 MSI data and need to convert raw or isotope-corrected ion-image intensities into quantified values using a known internal standard lipid species.
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39. ▌
    Corpus Size Coverage Scaling Analysis 2 · holobiomicslab

    Use when when deploying a Word2Vec-based spectral similarity model (such as Spec2Vec) on a new mass spectrometry dataset and needing to assess whether the pre-trained model's learned peak embeddings sufficiently represent the peaks in your query spectra.
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40. ▌
    Mass Spectrometry Data Format Parsing 2 · holobiomicslab

    Use when you have raw mass spectrometry instrument output (mzML, vendor binary formats, or mzPeak archives) and need to load spectrum metadata, chromatogram data, or signal arrays into memory for quality control, format conversion, or statistical analysis.
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41. ▌
    Neutral Loss Extraction And Weighting 2 · holobiomicslab

    Use when you have a collection of annotated MS/MS spectra with precursor m/z values and fragment peak lists, and you aim to train or apply a spectral similarity model (such as Spec2Vec Word2Vec) that exploits fragmentation chemistry.
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42. ▌
    Biological Reactant Pair Mapping Kegg 2 · holobiomicslab

    Use when constructing or enriching a chemical formula database that must capture not just structural similarity (DBEdges) but also biological co-occurrence patterns.
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43. ▌
    Chromatographic Resolution Evaluation 2 · holobiomicslab

    Use when when you have a feature table from LC-MS peak detection (e.g., output from MassCube's nontargeted peak segmentation step) and need to assess which features have adequate chromatographic separation from coeluting or nearby peaks.
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44. ▌
    Compound Structure Comparison Metrics 2 · holobiomicslab

    Use when you have two MS/MS spectra from related compounds (e.g., a reference compound and a suspected modified version) and need to quantify where and how their structures differ.
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45. ▌
    Convolutional Neural Network Encoding 2 · holobiomicslab

    Use when you have pairs of augmented ion images from mass spectrometry imaging data and need to generate low-dimensional representation vectors that maximize similarity between augmentations of the same image while avoiding representation collapse.
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46. ▌
    Cumulative Distribution Visualization 2 · holobiomicslab

    Use when when you have per-feature quality metrics (such as CV values from NMR or MS reproducibility analysis) and need to: (1) confirm that a specified proportion of features meet regulatory thresholds (e.g., 99% < 0.30, 92% < 0.15 for CV);
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47. ▌
    Dda Mode Metabolomics Data Processing 2 · holobiomicslab

    Use when when you have LC-MS/MS data collected in DDA mode and suspect that MS/MS spectra contain chimeric (multiply-charged or co-fragmented) ion signals that will degrade downstream spectral matching, library searching, or metabolite identification.
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48. ▌
    Deep Learning Spectral Language Model 2 · holobiomicslab

    Use when when you have an unknown compound's mass spectrum (m/z peaks and intensities in .mgf or equivalent format with mandatory PRECURSOR_MZ and IONMODE tags) and need to identify structurally related metabolites from a reference database.
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49. ▌
    Distribution Statistics Summarization 2 · holobiomicslab

    Use when you have loaded a collection of molecular fingerprint vectors (such as biosynfoni fingerprints from a deposited dataset) and need to characterize their statistical and distributional properties before using them for machine learning, similarity searching, or method validation.
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50. ▌
    Feature Group Fragment Classification 2 · holobiomicslab

    Use when you have a detected LC-MS feature table (with m/z, retention time, and intensity columns) and need to identify which features are derivatives of the same parent molecule rather than distinct metabolites.
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51. ▌
    Feature Normalization Standardization 2 · holobiomicslab

    Use when after extracting and encoding molecular descriptors and structural features (atom types, bond connectivity, graph topology) from SMILES strings into a fixed-size numerical tensor, before passing the feature matrix to the PS2MS deep learning model for NPS prediction.
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52. ▌
    Gaussian Peakshape Fitting Evaluation 2 · holobiomicslab

    Use when after peak detection on mass track segments using find_peaks, when you need to distinguish genuine chromatographic peaks from noise-induced false positives or irregular shapes.
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53. ▌
    Intensity Threshold Filtering Ms Data 2 · holobiomicslab

    Use when when you have loaded aligned peak data (from a preceding molecular networking alignment task) as a structured table with peak intensity, m/z, retention time, and alignment quality metrics, and you need to reduce false positives, remove noise, or focus analysis on peaks above a.
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54. ▌
    Intensity To Concentration Conversion 2 · holobiomicslab

    Use when you have raw mass spectrometry intensity measurements from sample analyses and need absolute quantitative concentrations.
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55. ▌
    Ion Mobility Demultiplexing Algorithm 2 · holobiomicslab

    Use when you have raw IM-MS data in UIMF or Agilent MassHunter .d format acquired using multiplexed (compressed) ion mobility pulse sequences, and you need to recover conventional (non-multiplexed) IM-MS spectra with resolved mobility and mass dimensions.
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56. ▌
    Landmark Peak Selection And Filtering 2 · holobiomicslab

    Use when when preparing mass tracks for retention-time (RT) alignment across multiple LC-MS samples.
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57. ▌
    Mass Spectrometry Data Interpretation 2 · holobiomicslab

    Use when you have a set of ions already matched to a khipu instance (i.e., ions whose isotope and adduct assignments are known and positioned on the theoretical khipu grid), and you need to estimate the neutral mass of the parent compound.
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58. ▌
    Mass Spectrometry Image Preprocessing 2 · holobiomicslab

    Use when you have raw or preprocessed single-channel (2D array) or multi-channel (spectral) ion images from mass spectrometry imaging (MSI) data and need to train a contrastive deep learning model.
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59. ▌
    Mass Spectrometry Prediction Modeling 2 · holobiomicslab

    Use when when you have a collection of compound structures in SDF format (e.
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60. ▌
    Memory Mapped File Selection Strategy 2 · holobiomicslab

    Use when initializing a Dataset object in NMRFx and must decide which storage backend to use for in-memory or memory-mapped file access.
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61. ▌
    Metabolic Model Consensus Integration 2 · holobiomicslab

    Use when you have draft metabolic reconstructions in SBML or standard format for multiple organisms in a microbial community (e.
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62. ▌
    Metabolomics Training Set Preparation 2 · holobiomicslab

    Use when when you have LC-MS/MS acquisitions in DDA mode and need to train a customized DNMS2Purifier model to purify chimeric MS/MS spectra specific to your experimental conditions, metabolite classes, or ionization settings.
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63. ▌
    Missing Pattern Generation Validation 2 · holobiomicslab

    Use when you have a complete metabolomics data matrix (simulated or real abundance table) and need to create reproducible, controlled MNAR scenarios for evaluating imputation algorithm performance.
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64. ▌
    Multi Head Attention Mechanism Design 2 · holobiomicslab

    Use when when building a transformer-based model to process mass spectrometry data (MS/MS spectra or fingerprints) where you need the model to learn multiple independent attention patterns across spectrum features simultaneously.
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65. ▌
    Mzmine Batch Processing Configuration 2 · holobiomicslab

    Use when you have raw metabolomics data in mzML or mzXML format and need to extract ion features, align them across samples, and produce a normalized feature table for downstream statistical or chemotaxonomic analysis.
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66. ▌
    Non Pathway Metabolite Classification 2 · holobiomicslab

    Use when you have metabolomics intensity data with peak annotations, and you want to rank and prioritize metabolite groupings (Molecular Families, Mass2Motifs, or other non-pathway metabolite sets) by their activity levels across experimental contrasts.
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67. ▌
    Normalization For Unequal Group Sizes 2 · holobiomicslab

    Use when when tabulating chemical annotation enrichment (e.g., GNPS spectral library matches) across sample groups stratified by metadata category (e.g., sample type, extraction method, ionization source), and the groups contain different numbers of files or samples.
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68. ▌
    Precursor Mass Difference Calculation 2 · holobiomicslab

    Use when when comparing two MS/MS spectra using modified cosine similarity and the precursor m/z values differ, indicating potential neutral losses, adduct variations, or analogs with different substituents.
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69. ▌
    Quality Control Threshold Application 2 · holobiomicslab

    Use when when you have a raw feature abundance matrix with missing values (NAs or zeros) and need to remove features with excessive sparsity before imputation or reproducibility analysis.
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70. ▌
    Sinusoidal Positional Encoding Design 2 · holobiomicslab

    Use when you have variable-length lists of MS/MS peaks (m/z and intensity pairs) that need to be encoded into a fixed-dimensional representation compatible with transformer architecture.
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71. ▌
    Spectral Fragmentation Motif Analysis 2 · holobiomicslab

    Use when when you have metabolomics intensity data with metabolites grouped by fragmentation spectral similarity (Molecular Families or Mass2Motifs) and need to rank or score these groups by their differential activity across experimental conditions, especially when traditional pathway databases.
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72. ▌
    Spectral Library Matching And Scoring 2 · holobiomicslab

    Use when when you have extracted a mean or ROI spectrum from MSI data (via centroid or profile mode conversion) and need to identify the biochemical composition by comparing against curated reference libraries such as LIPID MAPS, HMDB, or a custom metabolite database.
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73. ▌
    Spectral Peak Detection Composite Map 2 · holobiomicslab

    Use when when you have aligned mass tracks (extracted ion chromatograms) across multiple LC-MS samples consolidated into a composite map and need to detect reproducible elution peaks (features) that will be tracked back to individual samples.
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74. ▌
    Standalone Web Application Deployment 2 · holobiomicslab

    Use when you have cloned the GNPS_MASST codebase and need to instantiate a domain-specific MASST variant (microbeMASST, plantMASST, tissueMASST, microbiomeMASST, or foodMASST) to accept individual MS/MS spectra as input queries and perform searches against the corresponding curated reference.
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75. ▌
    Summarized Experiment Object Handling 2 · holobiomicslab

    Use when you have cross-validated, filtered metabolomic NMR or MS data in a SummarizedExperiment container and need to prepare it for metabolome-wide association studies (MWAS) with epidemiological confounders.
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76. ▌
    Transformer Cnn Hybrid Model Training 2 · holobiomicslab

    Use when you have preprocessed 1H NMR spectral data with compound labels and need to identify multiple compounds in a flavor mixture where both local spectral patterns (handled by CNN) and long-range spectral dependencies (handled by Transformer) are diagnostic.
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77. ▌
    Two Dimensional Spectral Map Analysis 2 · holobiomicslab

    Use when you have GC–MS or LC–MS data represented as a two-dimensional map with m/z values on one axis and retention time on the other, and you need to identify analyte signals and chemo-/biomarker features while minimizing false positive and false negative peak detections.
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78. ▌
    Workflow Definition Schema Validation 2 · holobiomicslab

    Use when you have located a workflow definition file (YAML or JSON) in a versioned release or commit and need to verify that it conforms to the schema specification for that release version (e.g., v1.0.0).
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79. ▌
    Embedding Space Representation 2 · holobiomicslab

    Use when you have pairs or triplets of MS/MS spectra with associated metadata (compound structural information, Tanimoto similarity scores) and want to learn embeddings that simultaneously preserve spectral similarity relationships and reconstruct peak intensities.
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80. ▌
    Conformer Ensemble Energy Ranking 2 · holobiomicslab

    Use when after RDKit has generated a large set of 3D conformers for a molecule in SDF or XYZ format, and before submitting conformers to computationally expensive quantum-chemical methods (e.g., QUICK).
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81. ▌
    Qm Output Parsing And Aggregation 2 · holobiomicslab

    Use when you have completed parallel QUICK quantum calculations on multiple conformers filtered by ASE-ANI and need to extract electronic properties from the output logs and consolidate them into a single structured table for use in CCS calculations or metabolite annotation workflows.
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82. ▌
    Chromatographic Baseline Estimation 2 · holobiomicslab

    Use when you have extracted ion chromatogram (EIC) candidate data from untargeted LC/HRMS files (mzXML, mzML, or netCDF format) and need to identify genuine peaks within each EIC.
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83. ▌
    Duplicate Spectrum Detection Splash 2 · holobiomicslab

    Use when when processing large collections of mass spectra from multiple Open Mass Spectra Libraries (OMSLs) or databases that may contain redundant spectral records with identical m/z–intensity peak patterns but potentially different metadata annotations.
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84. ▌
    Feature Table Export And Validation 2 · holobiomicslab

    Use when after executing an MZmine batch processing workflow on raw metabolomics data (mzML/mzXML format), when you need to convert the in-memory feature detection and alignment results into a shareable, schema-compliant tabular format suitable for downstream statistical analysis, figure.
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85. ▌
    High Performance Metric Computation 2 · holobiomicslab

    Use when you have implemented or are evaluating an algorithmic or system optimization (e.g., MASST+) that claims to reduce execution time, and you need to quantify and statistically validate the speedup against a baseline system (e.g., MASST).
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86. ▌
    Mass Accuracy Tolerance Application 2 · holobiomicslab

    Use when when you have generated in silico annotations (from GNPS ISDB, SIRIUS, or timaR) and need to distinguish true matches from false positives by enforcing a mass accuracy constraint. Apply this skill before computing novelty metrics (e.
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87. ▌
    Mass Spectrometry Adduct Assignment 2 · holobiomicslab

    Use when when processing in-silico or experimental MS spectra records from databases with incomplete metadata, specifically when the adduct field is null or absent but the ionmode field (positive/negative polarity) is present.
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88. ▌
    Metabolomic Peak Quality Assessment 2 · holobiomicslab

    Use when after peak detection in untargeted LC/HRMS workflows, when you have a list of candidate peaks with signal intensity profiles and need to filter them according to data quality thresholds (signal-to-noise ratio, peak width, baseline separation, and isotopic pairing consistency) before.
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89. ▌
    Peak Annotation With Bounding Boxes 2 · holobiomicslab

    Use when you have isolated reference peaks from training chromatograms (ground-truth, single compounds per sample) and need to create a diverse, labelled training set large enough to train a CNN peak detector.
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90. ▌
    Semantic Version Parsing Comparison 2 · holobiomicslab

    Use when before executing a bioinformatics pipeline that depends on multiple R packages with strict version constraints (e.g., DaDIA, which requires R ≥4.0, XCMS ≥3.11.4, and metaMS =1.25.1).
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91. ▌
    Batch File Processing Orchestration 2 · holobiomicslab

    Use when when you have multiple CDF imaging files (e.g., from mass spectrometry imaging scans of biological samples) that need to be read into a single Matlab workspace with consistent structure and metadata (spectral intensity, m/z arrays, spatial coordinates).
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92. ▌
    Contrastive Learning Encoder Design 2 · holobiomicslab

    Use when when you have mass spectrometry ion image data and need to learn meaningful low-dimensional representations through self-supervised contrastive learning.
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93. ▌
    Metabolic Network Edge Construction 2 · holobiomicslab

    Use when when building a comprehensive chemical knowledge base for mass spectrometry formula assignment, particularly when you need to link chemical formulae across heterogeneous repositories (HMDB, ChEMBL, PubChem) and connect them through known metabolic transformations to improve annotation.
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94. ▌
    Root Developmental Tissue Profiling 2 · holobiomicslab

    Use when you have CDF-format mass spectrometry imaging files from plant roots with accompanying MATLAB workspace files (.mat), and your research goal is to reproduce linear-axis intensity profiles, feature distributions, and spatial metrics reported in a prior publication (e.g., Sama et al. 2025).
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95. ▌
    Binary Cross Entropy Loss Optimization 2 · holobiomicslab

    Use when you have a pre-trained TCN spectrum encoder, annotated MS/MS spectra paired with ground-truth molecular formulas, and you want to train only the formula ranking and rescoring components without retraining the spectrum feature extractor.
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96. ▌
    Neutral Loss Annotation Interpretation 2 · holobiomicslab

    Use when when building Word2Vec or embedding-based spectral similarity models where you need to capture fragmentation patterns beyond individual peak positions.
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97. ▌
    Baseline Noise Estimation Chromatogram 2 · holobiomicslab

    Use when after auditing and optionally rescaling a mass track (composite mass chromatogram) when you need to subtract background signal and set dynamic prominence thresholds for peak detection.
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98. ▌
    Cardinal Peak Processing Snr Filtering 2 · holobiomicslab

    Use when when you have loaded an unprocessed Cardinal object from MS imaging data (e.g., from Zenodo or native formats) containing thousands of m/z features across many spectra, and you need to produce a curated peak list with known expected peak count (e.g., 687 cleaned peaks from PIGII_206).
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99. ▌
    Chemical Structure Similarity Matching 2 · holobiomicslab

    Use when you have a set of query chemical compounds (as SMILES, names, or ChemMine objects) and a reference library organized into type groups (e.g., Type A–E chemical sets), and you need to assign each query compound to its -matching type based on structural similarity.
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100. ▌
     Community Dependent Reaction Inference 2 · holobiomicslab

     Use when you have consensus metabolic reconstructions for multiple community members (e.g., plant-associated microbes or plant-microbial consortia) and those individual models contain incomplete or disconnected metabolic pathways.
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