Lilikoi V2.0: a deep learning–enabled, personalized pathway-based R package for diagnosis and prognosis predictions using metabolomics data
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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.
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Now published in GigaScience doi: 10.1093/gigascience/giaa162
Xinying Fang 1Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, USAFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteORCID record for Xinying FangYu Liu 2Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI USAFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteZhijie Ren 3Department of Electric Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USAFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteYuheng Du 1Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, USAFind this author on Google …
Now published in GigaScience doi: 10.1093/gigascience/giaa162
Xinying Fang 1Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, USAFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteORCID record for Xinying FangYu Liu 2Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI USAFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteZhijie Ren 3Department of Electric Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USAFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteYuheng Du 1Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, USAFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteQianhui Huang 1Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, USAFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteLana X. Garmire 2Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI USAFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteORCID record for Lana X. GarmireFor correspondence: lgarmire@med.umich.edu
A version of this preprint has been published in the Open Access journal GigaScience (see paper https://doi.org/10.1093/gigascience/giaa162 ), where the paper and peer reviews are published openly under a CC-BY 4.0 license.
These peer reviews were as follows:
Reviewer 1: http://dx.doi.org/10.5524/REVIEW.102617 Reviewer 2: http://dx.doi.org/10.5524/REVIEW.102618
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