Lilikoi V2.0: a deep learning-enabled, personalized pathway-based R package for diagnosis and prognosis predictions using metabolomics data

Gigascience. 2021 Jan 23;10(1):giaa162. doi: 10.1093/gigascience/giaa162.


Background: previously we developed Lilikoi, a personalized pathway-based method to classify diseases using metabolomics data. Given the new trends of computation in the metabolomics field, it is important to update Lilikoi software.

Results: here we report the next version of Lilikoi as a significant upgrade. The new Lilikoi v2.0 R package has implemented a deep learning method for classification, in addition to popular machine learning methods. It also has several new modules, including the most significant addition of prognosis prediction, implemented by Cox-proportional hazards model and the deep learning-based Cox-nnet model. Additionally, Lilikoi v2.0 supports data preprocessing, exploratory analysis, pathway visualization, and metabolite pathway regression.

Conculsion: Lilikoi v2.0 is a modern, comprehensive package to enable metabolomics analysis in R programming environment.

Keywords: classification; deep learning; metabolomics; neural network; pathway; prognosis; survival analysis; visualization.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Deep Learning*
  • Machine Learning
  • Metabolomics
  • Proportional Hazards Models
  • Software