Lilikoi V2.0: a deep-learning enabled, personalized pathway-based R package for diagnosis and prognosis predictions using metabolomics data
Lilikoi V2.0: a deep-learning enabled, personalized pathway-based R package for diagnosis and prognosis predictions using metabolomics data
ABSTRACT 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, 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.
Du Yuheng、Liu Yu、Garmire Lana X.、Huang Qianhui、Ren Zhijie、Fang Xinying
Department of Biostatistics, School of Public Health, University of MichiganDepartment of Computational Medicine and Bioinformatics, University of MichiganDepartment of Computational Medicine and Bioinformatics, University of MichiganDepartment of Biostatistics, School of Public Health, University of MichiganDepartment of Electric Engineering and Computer Science, University of MichiganDepartment of Biostatistics, School of Public Health, University of Michigan
生物科学研究方法、生物科学研究技术计算技术、计算机技术生物科学现状、生物科学发展
classificationprognosissurvivalpredictionneural networkdeep learningmodellingCox proportional hazardsmetabolomicspathwayvisualization
Du Yuheng,Liu Yu,Garmire Lana X.,Huang Qianhui,Ren Zhijie,Fang Xinying.Lilikoi V2.0: a deep-learning enabled, personalized pathway-based R package for diagnosis and prognosis predictions using metabolomics data[EB/OL].(2025-03-28)[2025-04-29].https://www.biorxiv.org/content/10.1101/2020.07.09.195677.点此复制
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