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Open AccessJournal ArticleDOI

Systems Biology and Multi-Omics Integration: Viewpoints from the Metabolomics Research Community.

TLDR
This document envisage that this document will serve as a guide to metabolomics researchers and other members of the community wishing to perform multi-omics studies and believe that these ideas may allow the full promise of integratedmulti-omics research and, ultimately, of systems biology to be realized.
Abstract
The use of multiple omics techniques (i.e., genomics, transcriptomics, proteomics, and metabolomics) is becoming increasingly popular in all facets of life science. Omics techniques provide a more holistic molecular perspective of studied biological systems compared to traditional approaches. However, due to their inherent data differences, integrating multiple omics platforms remains an ongoing challenge for many researchers. As metabolites represent the downstream products of multiple interactions between genes, transcripts, and proteins, metabolomics, the tools and approaches routinely used in this field could assist with the integration of these complex multi-omics data sets. The question is, how? Here we provide some answers (in terms of methods, software tools and databases) along with a variety of recommendations and a list of continuing challenges as identified during a peer session on multi-omics integration that was held at the recent 'Australian and New Zealand Metabolomics Conference' (ANZMET 2018) in Auckland, New Zealand (Sept. 2018). We envisage that this document will serve as a guide to metabolomics researchers and other members of the community wishing to perform multi-omics studies. We also believe that these ideas may allow the full promise of integrated multi-omics research and, ultimately, of systems biology to be realized.

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Journal ArticleDOI

Using machine learning approaches for multi-omics data analysis: A review

TL;DR: In this article, the authors explore different integrative machine learning methods which have been used to provide an in-depth understanding of biological systems during normal physiological functioning and in the presence of a disease.
Journal ArticleDOI

Recent advances in the application of metabolomics for food safety control and food quality analyses

TL;DR: Based on developments in analytical chemistry tools, cheminformatics, and bioinformatics methods, the current applications of metabolomics in food safety, food authenticity and quality, and food traceability are highlighted.
Journal ArticleDOI

State of the Field in Multi-Omics Research: From Computational Needs to Data Mining and Sharing.

TL;DR: This review provides guidance to interested users of the multi-omics domain by addressing challenges of the underlying biology, giving an overview of the available toolset, addressing common pitfalls, and acknowledging current methods’ limitations.
Journal ArticleDOI

Translational Metabolomics: Current Challenges and Future Opportunities.

TL;DR: This position paper is the summary of discussion on translational metabolomics undertaken during a peer session of the Australian and New Zealand Metabolomics Conference (ANZMET 2018) held in Auckland, New Zealand.
Journal ArticleDOI

Artificial Intelligence and Machine Learning in Computational Nanotoxicology: Unlocking and Empowering Nanomedicine.

TL;DR: In vitro and in vivo datasets are used in in silico models to unlock and empower nanomedicine and the challenges and opportunities facing the blind spots in nanotoxicology in this computationally dominated era are highlighted.
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TL;DR: The RNA-Seq approach to transcriptome profiling that uses deep-sequencing technologies provides a far more precise measurement of levels of transcripts and their isoforms than other methods.
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TL;DR: The FAIR Data Principles as mentioned in this paper are a set of data reuse principles that focus on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals.
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Network biology: understanding the cell's functional organization

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UniProt: the Universal Protein knowledgebase

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