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      Lilikoi: an R package for personalized pathway-based classification modeling using metabolomics data


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          Lilikoi (the Hawaiian word for passion fruit) is a new and comprehensive R package for personalized pathway-based classification modeling using metabolomics data. Four basic modules are presented as the backbone of the package: feature mapping module, which standardizes the metabolite names provided by users and maps them to pathways; dimension transformation module, which transforms the metabolomic profiles to personalized pathway-based profiles using pathway deregulation scores; feature selection module, which helps to select the significant pathway features related to the disease phenotypes; and classification and prediction module, which offers various machine learning classification algorithms. The package is freely available under the GPLv3 license through the github repository at: https://github.com/lanagarmire/lilikoi and CRAN: https://cran.r-project.org/web/packages/lilikoi/index.html.

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          Most cited references 25

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            MetPA (Metabolomics Pathway Analysis) is a user-friendly, web-based tool dedicated to the analysis and visualization of metabolomic data within the biological context of metabolic pathways. MetPA combines several advanced pathway enrichment analysis procedures along with the analysis of pathway topological characteristics to help identify the most relevant metabolic pathways involved in a given metabolomic study. The results are presented in a Google-map style network visualization system that supports intuitive and interactive data exploration through point-and-click, dragging and lossless zooming. Additional features include a comprehensive compound library for metabolite name conversion, automatic generation of analysis report, as well as the implementation of various univariate statistical procedures that can be accessed when users click on any metabolite node on a pathway map. MetPA currently enables analysis and visualization of 874 metabolic pathways, covering 11 common model organisms. Freely available at http://metpa.metabolomics.ca.
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              Pathway Tools version 13.0: integrated software for pathway/genome informatics and systems biology.

              Pathway Tools is a production-quality software environment for creating a type of model-organism database called a Pathway/Genome Database (PGDB). A PGDB such as EcoCyc integrates the evolving understanding of the genes, proteins, metabolic network and regulatory network of an organism. This article provides an overview of Pathway Tools capabilities. The software performs multiple computational inferences including prediction of metabolic pathways, prediction of metabolic pathway hole fillers and prediction of operons. It enables interactive editing of PGDBs by DB curators. It supports web publishing of PGDBs, and provides a large number of query and visualization tools. The software also supports comparative analyses of PGDBs, and provides several systems biology analyses of PGDBs including reachability analysis of metabolic networks, and interactive tracing of metabolites through a metabolic network. More than 800 PGDBs have been created using Pathway Tools by scientists around the world, many of which are curated DBs for important model organisms. Those PGDBs can be exchanged using a peer-to-peer DB sharing system called the PGDB Registry.

                Author and article information

                Oxford University Press
                December 2018
                10 December 2018
                10 December 2018
                : 7
                : 12
                [1 ]Department of Computational Medicine and Bioinformatics, Building 520, 1600 Huron Parkway, Ann Arbor, MI 48109, USA
                [2 ]University of Hawaii Cancer Center, Department of Epidemiology, 701 Ilalo Street, Honolulu, HI USA 96813
                [3 ]Molecular Biology and Bioengineering Graduate Program, University of Hawaii at Monoa, Honolulu, HI, USA 96822
                Author notes
                Correspondence address. Lana X. Garmire, Department of Computational Medicine and Bioinformatics, Building 520, 1600 Huron Parkway, Ann Arbor, MI 48109. E-mail: lgarmire@ 123456med.umich.edu
                © The Author(s) 2018. Published by Oxford University Press.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                Page count
                Pages: 9
                Funded by: National Institute of Enviromental Health Sciences
                Award ID: K01ES025434
                Funded by: National Institute of General Medical Sciences 10.13039/100000057
                Award ID: R01 LM012373
                Funded by: National Library of Medicine 10.13039/100000092
                Award ID: R01 HD084633
                Funded by: Eunice Kennedy Shriver National Institue of Health
                Technical Note


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