The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.
To cite lilikoi in publications use:
Fang X, Liu Y, Ren Z, Du Y, Huang Q, Garmire LX (2020). “Lilikoi V2.0: a deep-learning enabled, personalized pathway-based R package for diagnosis and prognosis predictions using metabolomics data.” bioRxiv. doi:10.1101/2020.07.09.195677, https://doi.org/10.1101/2020.07.09.195677.
AlAkwaa FM, Yunits B, Huang S, Alhajaji H, Garmire LX (2018). “Lilikoi: an R package for personalized pathway-based classification modeling using metabolomics data.” GigaScience, 7(12). https://doi.org/10.1093/gigascience/giy136.
Corresponding BibTeX entries:
@Article{,
title = {Lilikoi V2.0: a deep-learning enabled, personalized
pathway-based R package for diagnosis and prognosis predictions
using metabolomics data},
author = {Xinying Fang and Yu Liu and Zhijie Ren and Yuheng Du and
Qianhui Huang and Lana X. Garmire},
journal = {bioRxiv},
year = {2020},
doi = {10.1101/2020.07.09.195677},
url = {https://doi.org/10.1101/2020.07.09.195677},
}
@Article{,
title = {Lilikoi: an R package for personalized pathway-based
classification modeling using metabolomics data},
author = {Fadhl M. AlAkwaa and Breck Yunits and Sijia Huang and
Hassam Alhajaji and Lana X. Garmire},
journal = {GigaScience},
year = {2018},
volume = {7},
number = {12},
url = {https://doi.org/10.1093/gigascience/giy136},
}
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.