| 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"} |
background-distribution-significance-thresholding
When to use
Use when the workflow requires background-distribution-significance-thresholding.
