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Lilikoi: an R package for personalized pathway-based classification modeling using metabolomics data.

Fadhl M Al-Akwaa1, Breck Yunits2, Sijia Huang2,3

  • 1Department of Computational Medicine and Bioinformatics, Building 520, 1600 Huron Parkway, Ann Arbor, MI 48109, USA.

Gigascience
|December 12, 2018
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Summary
This summary is machine-generated.

Lilikoi is a new R package for personalized pathway-based classification using metabolomics data. It offers modules for metabolite mapping, dimension transformation, feature selection, and machine learning classification to aid disease phenotype analysis.

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Area of Science:

  • Computational Biology and Bioinformatics
  • Metabolomics and Systems Biology
  • Machine Learning in Healthcare

Background:

  • Metabolomics data offers a powerful lens for understanding biological systems and disease mechanisms.
  • Personalized pathway-based approaches are crucial for interpreting complex metabolomic profiles in relation to disease phenotypes.
  • Existing tools may lack comprehensive functionality for integrated pathway analysis and machine learning in metabolomics.

Purpose of the Study:

  • To introduce Lilikoi, a novel R package designed for comprehensive, personalized pathway-based classification using metabolomics data.
  • To provide a robust framework integrating metabolite standardization, pathway mapping, dimension reduction, feature selection, and machine learning classification.
  • To facilitate the identification of significant pathway features associated with disease phenotypes from metabolomic data.

Main Methods:

  • Development of an R package, Lilikoi, comprising four core modules: feature mapping, dimension transformation, feature selection, and classification/prediction.
  • Feature mapping standardizes metabolite names and maps them to biological pathways.
  • Dimension transformation calculates pathway deregulation scores, creating personalized pathway-based profiles.
  • Feature selection identifies significant pathway features linked to disease phenotypes.
  • Classification module implements various machine learning algorithms for predictive modeling.

Main Results:

  • Lilikoi provides a unified and extensible platform for advanced metabolomic data analysis.
  • The package enables the transformation of raw metabolomic data into biologically relevant, personalized pathway scores.
  • It facilitates robust feature selection and the application of diverse machine learning models for disease classification.

Conclusions:

  • Lilikoi offers a comprehensive solution for personalized pathway-based classification in metabolomics research.
  • The package enhances the interpretability of metabolomic data by focusing on pathway-level deregulation.
  • Lilikoi is freely available, promoting its adoption and advancement in the scientific community for disease phenotype analysis.