Systems Biology and Multi-Omics Integration: Viewpoints from the Metabolomics Research Community.
Farhana R. Pinu,David J. Beale,Paten Am,Konstantinos A. Kouremenos,Sanjay Swarup,Horst Joachim Schirra,David S. Wishart +6 more
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.read more
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Using machine learning approaches for multi-omics data analysis: A review
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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.
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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.
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Artificial Intelligence and Machine Learning in Computational Nanotoxicology: Unlocking and Empowering Nanomedicine.
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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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