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bioRxiv · 10.1101/2020.07.09.195677

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

Xinying Fang·Yu Liu·Zhijie Ren·Yuheng Du·Qianhui Huang·Lana X. Garmire
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Abstract

ABSTRACTPreviously we developed Lilikoi, a personalized pathway-based method to classify diseases using metabolomics data. Given the new trends of computation in the metabolomics field, 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-PH model and the deep-learning based Cox-nnet model. Additionally, Lilikoi v2.0 supports data preprocessing, exploratory analysis, pathway visualization and metabolite-pathway regression. In summary, Lilikoi v2.0 is a modern, comprehensive package to enable metabolomics analysis in R programming environment.Competing Interest StatementThe authors have declared no competing interest.View Full Text

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Xinying Fang, Yu Liu, Zhijie Ren, Yuheng Du, Qianhui Huang, Lana X. Garmire. 2020-07-10. Lilikoi V2.0: a deep-learning enabled, personalized pathway-based R package for diagnosis and prognosis predictions using metabolomics data. https://doi.org/10.1101/2020.07.09.195677

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