Journal
GIGASCIENCE
Volume 10, Issue 1, Pages -Publisher
OXFORD UNIV PRESS
DOI: 10.1093/gigascience/giaa162
Keywords
classification; prognosis; survival analysis; neural network; deep learning; metabolomics; pathway; visualization
Categories
Funding
- NIEHS through trans-NIH Big Data to Knowledge (BD2K) initiative [K01ES025434]
- NLM [R01 LM012373, R01 LM012907]
- NICHD [R01 HD084633]
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Lilikoi v2.0 is a significant upgrade with the implementation of deep learning method, new modules, and support for various functions to enable metabolomics analysis in R programming environment.
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.
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