GigaScienceBioinformaticsPeer-reviewed journal article
Lilikoi V2.0: a deep-learning enabled, personalized pathway-based R package for diagnosis and prognosis predictions using metabolomics data
Xinying FangiD, Yu LiuiD, Zhijie ReniD, Yuheng DuiD, Qianhui HuangiD, Lana Xia GarmireiD
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
- assay: metabolomics
- research focus: software / tool
- approach: deep learning
- approach: machine learning (classical)
- Citations
- 18
- FWCI
- 1.02
- Field percentile
- top 27%
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