Lilikoi V2.0: a deep learning–enabled, personalized pathway-based R package for diagnosis and prognosis predictions using metabolomics data. Issue 1 (23rd January 2021)

• Record Type: Journal Article
• Authors: Fang, Xinying; Liu, Yu; Ren, Zhijie; Du, Yuheng; Huang, Qianhui; Garmire, Lana X
• Is Part Of: GigaScience. Volume 10:Issue 1(2021)
• Publication Date: 2021-01-23
• Subjects: classification -- prognosis -- survival analysis -- neural network -- deep learning -- metabolomics -- pathway -- visualization; Information storage and retrieval systems -- Research -- Periodicals; Biology -- Research -- Periodicals; Medical sciences -- Research -- Periodicals; Database management -- Periodicals; 570.285
• DOI: 10.1093/gigascience/giaa162
• Languages: English
• ISSNs: 2047-217X

Abstract

Abstract: 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.