Lilikoi V2.0: a deep-learning enabled, personalized pathway-based R package for diagnosis and prognosis predictions using metabolomics data

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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“…Snapshots of our code and data further supporting this work can be openly found in the GigaScience repository, GigaDB [ 42 ].…”

Section: Data Availabilitymentioning

confidence: 99%

Lilikoi V2.0: a deep learning–enabled, personalized pathway-based R package for diagnosis and prognosis predictions using metabolomics data

et al. 2021

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“…The recently developed Lilikoi v2.0 R software, implements a DL neural network classifier in addition to six ML algorithms including RF and SVM, for the prediction and visualization of metabolic pathways. Lilikoi requires metabolomics data matrix with a column of categorial variables (i.e., treated/control) for classification as input data, which is analyzed prior to the construction of the pathway [ 217 ]. RetroPath Reinforcement Learning-based metabolic space exploration (RetroPath RL) implements the Monte Carlo Tree Search (MCTS) reinforcement learning method ( Figure 3 ) to discover and suggest experimentally relevant pathways.…”

Section: 4ir Technologies and Plant Metabolomicsmentioning

confidence: 99%

Metabolomics-Guided Elucidation of Plant Abiotic Stress Responses in the 4IR Era: An Overview

et al. 2021

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Plants are constantly challenged by changing environmental conditions that include abiotic stresses. These are limiting their development and productivity and are subsequently threatening our food security, especially when considering the pressure of the increasing global population. Thus, there is an urgent need for the next generation of crops with high productivity and resilience to climate change. The dawn of a new era characterized by the emergence of fourth industrial revolution (4IR) technologies has redefined the ideological boundaries of research and applications in plant sciences. Recent technological advances and machine learning (ML)-based computational tools and omics data analysis approaches are allowing scientists to derive comprehensive metabolic descriptions and models for the target plant species under specific conditions. Such accurate metabolic descriptions are imperatively essential for devising a roadmap for the next generation of crops that are resilient to environmental deterioration. By synthesizing the recent literature and collating data on metabolomics studies on plant responses to abiotic stresses, in the context of the 4IR era, we point out the opportunities and challenges offered by omics science, analytical intelligence, computational tools and big data analytics. Specifically, we highlight technological advancements in (plant) metabolomics workflows and the use of machine learning and computational tools to decipher the dynamics in the chemical space that define plant responses to abiotic stress conditions.

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“…Besides metabolite levels, the levels of pathway activity or deregulation may provide more direct insights to function study. Recently, the pathway-based features have been used in some personalized health studies and speci c method has been developed for pathway deregulation matrix generation [22][23][24][25]. A tool which converts raw metabolite-based data matrix to the pathway-based matrix is implemented in 3MCor.…”

Section: Metabolomics Pathway Deregulation Matrix Generationmentioning

confidence: 99%

3MCor: An Integrative Web Server for Metabolome-Microbiome-Metadata Correlation Analysis

Sun

et al. 2021

Preprint

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Background: Mounting evidences have shown that microbiome and metabolome are closely linked to human health and dual-omics studies expanded our knowledge and understanding of health and life. Here, we designed and developed a full-function and easy-to-use platform, 3MCor (http://3mcor.cn/), for metabolome and microbiome correlation analysis under the instruction of phenotype and with the consideration of confounders.Results: Many traditional and newly reported correlation analysis methods were integrated for intra- and inter-correlation analysis. Three inter-correlation pipelines are provided for global, hierarchical, and pairwise analysis. Especially, the incorporated network analysis function is conducive to a rapid identification of network clusters and key nodes from a complicated correlation network. Complete numerical results (csv files) and rich figures (pdf files) will be generated in minutes. To our knowledge, 3MCor is the first platform developed specifically for the correlation analysis of metabolome and microbiome. Its functions were compared with corresponding modules of existing omics data analysis platforms. Results from 2 real-world data sets, one from a public library with a continuous phenotype and one from our lab with a categorical phenotype, were used to demonstrate its simple and flexible operation, comprehensive outputs, and distinctive contribution to dual-omics studies. Conclusions: 3MCor is powerful with complementary pipelines and comprehensive considerations of phenotypes, confounders, and the interactions among omics features. In addition to the web server, the backend R script is available at https://github.com/chentianlu/3MCorServer.

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Lilikoi V2.0: a deep-learning enabled, personalized pathway-based R package for diagnosis and prognosis predictions using metabolomics data

Cited by 6 publications

References 36 publications

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

Metabolomics-Guided Elucidation of Plant Abiotic Stress Responses in the 4IR Era: An Overview

3MCor: An Integrative Web Server for Metabolome-Microbiome-Metadata Correlation Analysis

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