Systems biology and big data in asthma and allergy: recent discoveries and emerging challenges. Tang, HHF, Sly, Peter, Holt, PG, Holt, KE and Inouye, M 2020, Systems biology and big data in asthma and allergy: recent discoveries and emerging challenges., Eur Respir J, vol. 55, no. 1, doi: 10.1183/13993003.00844-2019. Attached Files Name Description MIMEType Size Downloads Title Systems biology and big data in asthma and allergy: recent discoveries and emerging challenges. Author(s) Tang, HHF Sly, Peter Holt, PG Holt, KE Inouye, M Journal name Eur Respir J Volume number 55 Issue number 1 Publisher p Place of publication England Publication date 2020-01 ISSN 1399-3003 Keyword(s) Science & Technology Life Sciences & Biomedicine Respiratory System GENOME-WIDE ASSOCIATION GENE-ENVIRONMENT INTERACTIONS HOUSE-DUST MITE CHILDHOOD ASTHMA GUT MICROBIOTA AIR-POLLUTION RISK-FACTORS T-CELLS CLINICAL PHENOTYPES CYTOKINE EXPRESSION Summary Asthma is a common condition caused by immune and respiratory dysfunction, and it is often linked to allergy. A systems perspective may prove helpful in unravelling the complexity of asthma and allergy. Our aim is to give an overview of systems biology approaches used in allergy and asthma research. Specifically, we describe recent "omic"-level findings, and examine how these findings have been systematically integrated to generate further insight.Current research suggests that allergy is driven by genetic and epigenetic factors, in concert with environmental factors such as microbiome and diet, leading to early-life disturbance in immunological development and disruption of balance within key immuno-inflammatory pathways. Variation in inherited susceptibility and exposures causes heterogeneity in manifestations of asthma and other allergic diseases. Machine learning approaches are being used to explore this heterogeneity, and to probe the pathophysiological patterns or "endotypes" that correlate with subphenotypes of asthma and allergy. Mathematical models are being built based on genomic, transcriptomic and proteomic data to predict or discriminate disease phenotypes, and to describe the biomolecular networks behind asthma.The use of systems biology in allergy and asthma research is rapidly growing, and has so far yielded fruitful results. However, the scale and multidisciplinary nature of this research means that it is accompanied by new challenges. Ultimately, it is hoped that systems medicine, with its integration of omics data into clinical practice, can pave the way to more precise, personalised and effective management of asthma. Language eng DOI 10.1183/13993003.00844-2019 Indigenous content off Field of Research 11 Medical and Health Sciences Free to Read? Yes Persistent URL http://hdl.handle.net/10536/DRO/DU:30164623 Document type: Journal Article Collections: Faculty of Health School of Psychology Open Access Collection Related Links Link Description Link to full-text (open access)   Connect to published version Go to link with your DU access privileges   Author URL Go to link with your DU access privileges   Connect to Elements publication management system Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au. Versions Version Filter Type Fri, 18 Mar 2022, 07:35:16 EST Fri, 18 Mar 2022, 15:59:02 EST Wed, 06 Apr 2022, 08:59:08 EST Thu, 21 Apr 2022, 16:18:20 EST Thu, 21 Apr 2022, 16:31:52 EST Thu, 05 May 2022, 21:36:41 EST Filtered Full Citation counts:   Cited 8 times in TR Web of Science Cited 10 times in Scopus Search Google Scholar Access Statistics: 10 Abstract Views, 0 File Downloads  -  Detailed Statistics Created: Fri, 18 Mar 2022, 07:35:16 EST