القائمة
Use when use when the workflow requires background-distribution-significance-thresholding.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
Use when when you have a neural network or machine learning model with multiple tunable hyperparameters (layer size, regularization strength, dropout) or design choices (e.
Use when when building a visualization library that must support multiple plotting backends (e.g., matplotlib, bokeh, plotly) and multiple data types (e.g., chromatograms, spectra, peak maps) without duplicating core logic or configuration handling across backend–plot-type combinations.
Use when after peaks have been assigned to heteroatom classes (e.g., CHO, CHON, CHOS, CHOP) and you need to compare molecular composition across samples, classes, or time series.
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.
Use when after computing a histogram of all pairwise mass differences from an MSI dataset, use this skill when you have observed mass difference peaks that may correspond to known adducts (e.g., [M+H]+, [M+Na]+, [M−H2O]+).
Use when you have a trained formula ranking model (such as MIST-CF) and want to measure the specific performance gain from incorporating multiple positive-mode adduct types (e.g., [M+H]+, [M+Na]+, [M+K]+, [M+NH4]+) instead of restricting predictions to [M+H]+ only.
| name | background-distribution-significance-thresholding |
| description | Use when use when the workflow requires background-distribution-significance-thresholding. |
| license | CC-BY-4.0 |
| metadata | {"edam_topics":[],"tools":["MiMeNet","ADAM optimizer","MelonnPan","Elastic Net","WGCNA"],"license_tier":"open"} |
| derived_from | [{"doi":"10.1371/journal.pcbi.1009021","title":"MiMeNet"}] |
| evidence_spans | ["An MLPNN model is composed of multiple fully connected hidden layers composed of perceptrons","MiMeNet is an integrative MLPNN, which trains models to accurately predict the metabolome based on a microbiome","MiMeNet was trained using the ADAM optimizer and the mean squared error (MSE) loss function.","MiMeNet was trained using the ADAM optimizer and the mean squared error (MSE) loss function","MelonnPan was downloaded from https://github.com/biobakery/melonnpan and executed using the given instructions","Multivariate Elastic Net models were implemented using ElasticNet and GridSearchCV using 5-fold internal cross-validation"] |
| claims | [] |
| provenance | {"collection":"https://w3id.org/holobiomicslab/asb-skill/collection/metabolomics/v2","assembled_by":"scripts/collect_metabolomics_collection.py","sources":[{"build":"coll_mimenet","doi":"10.1371/journal.pcbi.1009021","title":"MiMeNet"}],"dedup_kept_from":"coll_mimenet"} |
| schema_version | 0.2.0 |
| attribution | {"generator":"AgenticScienceBuilder","original_doi":"10.1371/journal.pcbi.1009021","all_source_dois":["10.1371/journal.pcbi.1009021"],"zenodo_doi":"10.5281/zenodo.20794027","curators":[],"promoter":"Louis-Félix Nothias","sponsor":"CNRS & Université Côte d'Azur"} |
Use when the workflow requires background-distribution-significance-thresholding.
