Claude Skills by HolobiomicsLab

Use when after converting MS/MS spectra to fixed-length vector representations

Use when you have a complete metabolomics data matrix (simulated or real

Use when apply fillPeaks after retention time alignment (whether XCMS

Use when you have a raw abundance matrix (e.g., metabolite or gene features

Use when after loading raw omics expression data (protein, peptide, metabolite

Use when after sample alignment and feature grouping in untargeted LC-MS

Use when when preparing raw HPLC column parameter arrays for featurization

Use when your metabolomic peak table contains missing values (e.g., undetected

Use when your metabolomic dataset contains missing values (common in

Use when after feature extraction and quality control filtering (blank

Use when after feature alignment across multiple LC-MS/MS runs, when

Use when your raw metabolomics dataset contains missing values (NAs)

Use when you have a metabolomics data matrix with missing-not-at-random

Use when after mark_nas() has replaced non-NA missing-value codes (e.g.,

Use when your feature intensity table (samples × compounds) contains

Use when you have log-transformed metabolomics data in SummarizedExperiment

Use when you are implementing a custom MsBackend subclass for the Spectra

Use when after loading a feature table into memory when the table contains

Use when your metabolomics dataset contains hierarchical or repeated

Use when you have an observed NMR mixture spectrum and one or more candidate

Use when you have metabolomics data (targeted LC/MS or untargeted GC/MS)

Use when when you have applied multiple left-censored missing value imputation

Use when working with raw multiplexed IM-MS data (UIMF or Agilent MassHunter

Use when analyzing CE-MS(/MS) data where electroosmotic flow fluctuations

Use when when you have a trained multitask model that accepts multiple

Use when you need to measure how much a specific model capability or

Use when after a deep neural network model has completed training on

Use when training a Transformer or neural network model on a large dataset

Use when after successfully training a spectrum prediction model (FFN

Use when when you have multiple candidate spectrum prediction models

Use when you have a pre-trained Keras model and need to deploy it via

Use when you have retrained or modified a neural network model (e.g.,

Use when you have a pre-trained GNN model for CCS prediction and need

Use when you have a pre-trained or newly retrained graph neural network

Use when a deep learning model for molecular structure prediction (e.g.,

Use when you have a trained baseline GNN model with established hyperparameters

Use when you have MS/MS spectra in .msp format and need to retrieve similar

Use when when deploying a TensorFlow Serving instance for the NP Classifier

Use when after starting a TensorFlow Serving instance (e.g., via docker-compose)

Use when when you need to programmatically interface with a TensorFlow

Use when when preparing to send peak data (1H and 13C NMR measurements)

Use when before submitting peak data or other inputs to a machine learning

Use when after deploying a TensorFlow Serving container (especially within

Use when after instantiating a neural network model (such as TransG-Net)

Use when you have a binary classification task on metabolomics data (e.g.,

Use when after fitting a linear model to normalized metabolomics featuredata

Use when you have trained Keras models that need to be deployed in a

Use when you have raw co-elution profiles (27 fractions × 2+ proteins

Use when when you have predictions from multiple independently trained

Use when after training a MEISTER deep learning reconstruction model