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---
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
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/v1
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
---
# background-distribution-significance-thresholding
## When to use
Use when the workflow requires background-distribution-significance-thresholding.
