AI & ML

holobiomicslab Skill

Use when after instantiating a transformer encoder module for mass spectrometry data processing (e.g., in IDSL_MINT), before training on large MS/MS datasets or running inference on test spectra.

holobiomicslab Skill

Use when after running inference on a trained structure prediction model with one or more input modalities (1H NMR, 13C NMR, or combined), you have generated predicted molecular formulas and connectivity graphs that need to be compared against known ground truth structures.

holobiomicslab Skill

Use when you need to measure how much a specific model capability or architectural feature contributes to prediction performance, especially when that capability is non-obvious or orthogonal to baseline methods.

holobiomicslab Skill

Use when after executing a molecular networking workflow on GC-MS data that has been processed through auto-deconvolution, and a published reference network exists from a prior analysis of the same or analogous dataset.

holobiomicslab Skill

Use when you have loaded an MsmsSpectrum object from a proteomics or metabolomics dataset and need to focus the analysis window on a specific m/z range relevant to your experiment (e.g., 100–1400 m/z for typical tryptic peptides).

holobiomicslab Skill

Use when you have a preprocessed bag-of-fragments corpus derived from tandem mass spectrometry spectra and need to discover recurring fragmentation motifs without prior compound identification.

holobiomicslab Skill

Use when you have LC-MS feature tables (m/z and retention time columns) and corresponding .mzXML or .mzML files, and you need to automatically classify whether extracted ion chromatograms represent genuine metabolomic features or false positives.

holobiomicslab Skill

Use when after embedding MS/MS spectra into a 32-dimensional vector space using GLEAMS, when you need to identify and group all spectra originating from the same peptide sequence.

holobiomicslab Skill

Use when after implementing a transformer encoder backbone with masking mechanisms in PyTorch, before training on mass spectra data.

holobiomicslab Skill

Use when when you have a set of small molecules (as SMILES or structures) that need to be identified or ranked by their elution order in RPLC systems with acidic pH (~2.7), and you want to assess model confidence in retention predictions.

holobiomicslab Skill

Use when you have preprocessed mass spectrometry fragmentation data (neutral losses and fragment masses extracted and noise-filtered) and want to discover hidden structural motifs across a spectral dataset in an unsupervised manner.

holobiomicslab Skill

Use when you have a trained multitask machine learning model for structure prediction, test set molecules with paired ¹H and ¹³C NMR spectra, and need to understand the marginal contribution of each NMR modality or justify multimodal input design.

holobiomicslab Skill

Use when after training or loading a NeatMS neural network model, before applying it to filter false positive MS1 peaks in a new dataset.

holobiomicslab Skill

Use when after running retention-order prediction experiments on a test or held-out evaluation dataset.

holobiomicslab Skill

Use when after loading raw LC-MS data from multiple disease groups when you need to compute correlations between metabolite signals and disease classes, or before training a deep learning model on metabolomics profiles.

holobiomicslab Skill

Use when when you have raw LC-MS metabolomics data in .mzML or .npy format from multiple disease groups with varying ionization efficiencies or detector sensitivities, and you need to train a deep learning model for disease classification.

holobiomicslab Skill

Use when you have prediction arrays (model outputs) and ground-truth label arrays from a classification task and need to compute confusion matrices, accuracy scores, or other performance metrics for visualization or quantitative evaluation.

holobiomicslab Skill

Use when you are preparing to run LipoCLEAN on MS-DIAL output and need to create or update a configuration file, or you have switched between MS-DIAL 4 and MS-DIAL 5 data and need to verify that your options file and trained model are compatible with the current version's column naming and scaling.

holobiomicslab Skill

Use when when you have a raw list of SMILES strings but lack sufficient real mass spectrometry reference data (typically <300 spectra) to train a generative model for unknown chemical identification.

holobiomicslab Skill

Use when after AutoTuner has completed EICparams extraction and parameter estimation on raw untargeted metabolomics data (mzML, mzXML, or CDF format), and you need to pass those estimates into XCMS or MZmine2 for full dataset processing.

holobiomicslab Skill

Use when when you have a preprocessed bag-of-fragments corpus from tandem mass spectrometry spectra and need to train an MS2LDA model to discover Mass2Motifs.

holobiomicslab Skill

Use when when you have access to annotated MS/MS spectra from a specific ionization mode (e.g., negative ESI) or adduct class (e.

holobiomicslab Skill

Use when when preparing an input spectral peak list for NMRformer or similar Transformer-based peak assignment models, and you have observed peaks whose chemical shifts do not align with known metabolite signatures.

holobiomicslab Skill

Use when you have LC-MS feature tables (with m/z and retention time columns) paired with raw .mzXML or .mzML files, and you need to automatically validate which detected features correspond to genuine chromatographic peaks versus false positives or noise.

holobiomicslab Skill

Use when you have cleaned MS/MS spectra (in formats like .mgf, .msp, .

holobiomicslab Skill

Use when before submitting peak data or other inputs to a machine learning classification API for the first time, after a model update, or if you encounter unexpected prediction errors. It is essential when the underlying model's input names or structure may change and require code updates.

holobiomicslab Skill

Use when you have raw LC-MS data in mzML format with regions of interest (ROI) already identified, and a trained detection model (e.g., checkpoint0029.pth) has produced bounding box predictions with confidence scores.

holobiomicslab Skill

Use when when you have pre-computed embeddings (from MSBERT, Spec2Vec, or other deep learning models) for a query spectrum dataset and a reference library, and need to measure how well the embedding space ranks correct library matches.

holobiomicslab Skill

Use when you have a set of unidentified tandem mass spectra (queries) and need to identify them by matching against a curated reference library (e.g., GNPS Orbitrap dataset).

holobiomicslab Skill

Use when when you have TCN-predicted candidate formulas with ranked scores and need to train a Siamese rescore model to re-rank those candidates.

holobiomicslab Skill

Use when you have generated feature tables from LC-MS data using different parameter combinations (e.g., varying Centwave, FeatureFinderMetabo, or ADAP peak picking settings) and need to objectively compare their outputs to select the -performing configuration for your dataset.

holobiomicslab Skill

Use when you have molecular descriptors or fingerprints for a set of compounds (e.g., from LC-MS metabolomics) and need to predict a continuous property—such as HPLC retention time—to support compound identification or filter out false positive annotations.

holobiomicslab Skill

Use when when you have annotated chemical structures (SMILES or InChI strings) from a curated MS/MS dataset and need to compute pairwise structural similarity scores (Tanimoto or other metrics) as training labels, or when preparing molecular representations for comparison against mass spectral data.

holobiomicslab Skill

Use when after fitting a Multi-Block PLS (MB-PLS) discriminant or regression model on multi-assay LC-MS intensity data (e.g., HPOS, LPOS, LNEG blocks), and you need to identify which features drive model performance and warrant further statistical validation or biological interpretation.

holobiomicslab Skill

Use when when you need to verify whether a specific mass spectrometry instrument platform (vendor and model), acquisition mode (e.g., targeted, ddMS2-topN, AIF, direct infusion, imaging), or file format is compatible with a lipidomics or proteomics software tool;

holobiomicslab Skill

Use when when you have deployed a trained embedding or similarity model on a test set of tandem mass spectra and need to measure its ability to rank correct library compounds near the top of retrieved candidates.