Department of Biostatistics, School of Public Health, University of Michigan, 1415 Washington Heights, Ann Arbor, MI 49109, USA.
Department of Computational Medicine and Bioinformatics, University of Michigan, 1600 Huron Parkway, Ann Arbor, MI 48105, USA.
Gigascience. 2021 Jan 23;10(1). doi: 10.1093/gigascience/giaa162.
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.
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.
Lilikoi v2.0 is a modern, comprehensive package to enable metabolomics analysis in R programming environment.
此前，我们开发了 Lilikoi，这是一种个性化的基于途径的方法，可使用代谢组学数据对疾病进行分类。鉴于代谢组学领域计算的新趋势，更新 Lilikoi 软件非常重要。
在这里，我们报告了 Lilikoi 的下一个版本，这是一个重大升级。新的 Lilikoi v2.0 R 包已经实现了一种分类的深度学习方法，除了流行的机器学习方法之外。它还有几个新模块，包括最重要的预后预测的添加，通过 Cox 比例风险模型和基于深度学习的 Cox-nnet 模型实现。此外，Lilikoi v2.0 还支持数据预处理、探索性分析、途径可视化和代谢物途径回归。
Lilikoi v2.0 是一个现代化的、全面的软件包，可在 R 编程环境中进行代谢组学分析。
