Lilikoi: an R package for personalized pathway-based classification modeling using metabolomics data
Fadhl AlakwaaiD, Breck YunitsiD, Sijia HuangiD, Hassam Alhajaji, Lana Xia GarmireiD
Abstract
Lilikoi (Hawaiian word for passion fruit) is a new and comprehensive R package for personalized pathway based classification modelling, using metabolomics data. Four basic modules are presented as the backbone of the package: 1) Feature mapping module, which standardizes the metabolite names provided by users, and map them to pathways. 2) Dimension transformation module, which transforms the metabolomic profiles to personalized pathway-based profiles using pathway deregulation scores (PDS). 3) Feature selection module which helps to select the significant pathway features related to the disease phenotypes, and 4) Classification and prediction module which offers various machine-learning classification algorithms. The package is freely available under the GPLv3 license through the github repository at: https://github.com/lanagarmire/lilikoi
02 /Specification & Classification
Click any tag to browse matching papers- Citations
- 33
- FWCI
- 1.60
- Field percentile
- top 16%
- Assay
- Research Focus
- Approach
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Lilikoi V2.0: a deep-learning enabled, personalized pathway-based R package for diagnosis and prognosis predictions using metabolomics data
Xinying Fang, Yu Liu, … Lana Xia Garmire·GigaSciencePeer-reviewed·Jan 1, 2021
- Predicting The Pathway Involvement Of Metabolites Based on Combined Metabolite and Pathway Features
Erik D. Huckvale, Hunter Moseley·MetabolitesPeer-reviewed·May 7, 2024
- Predicting the Pathway Involvement of Metabolites in Both Pathway Categories and Individual Pathways
Erik D. Huckvale, Hunter Moseley·bioRxiv·Aug 7, 2024
- Decomposing metabolite set activity levels with PALS
Karen McLuskey, Joe Wandy, … Rónán Daly·bioRxiv·Jun 7, 2020
- Common data models to streamline metabolomics processing and annotation, and implementation in a Python pipeline
Joshua M. Mitchell, Yuanye Chi, … Shuzhao Li·PLoS Computational BiologyPeer-reviewed·Jun 6, 2024
- Benchmark dataset for training machine learning models to predict the pathway involvement of metabolites
Erik D. Huckvale, Christian D. Powell, … Hunter Moseley·MetabolitesPeer-reviewed·Nov 1, 2023