Editorial: Integrative Multi-Modal and Multi-Omics Analytics for the Better Understanding of Metabolic Diseases

Animesh Acharjee; Prasoon Agarwal; Georgios Gkoutos

doi:10.3389/fendo.2023.1266557

Editorial: Integrative Multi-Modal and Multi-Omics Analytics for the Better Understanding of Metabolic Diseases

Animesh Acharjee^*, Prasoon Agarwal, Georgios Gkoutos

^*Corresponding author for this work

Cancer and Genomic Sciences

Research output: Contribution to journal › Editorial › peer-review

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Abstract

In the past few years, large-scale, high-throughput multi-omics experiments and improved clinical measurements have led to the generation of a plethora of multi-modal data sets related to many metabolic diseases (MetS), for example type 1 diabetes (T1D), obesity, non-alcoholic fatty liver disease (NAFLD), etc. In literature there are many integration strategies discussed for example early, intermediate and late integration [1]. The process of early integration involves merging multiple omics information into a unified matrix whereas intermediate integration involves transforming the source datasets into representations that are both common and specific to omics. The late integration involves the individual analysis of each omics dataset, followed by the combination of their respective predictions to get a result [1]. In the Figure 1 an example of late integration described in the context of MetS.This editorial summarizes the contribution to the special issue of Frontiers in Endocrinology, "Integrative Multi-Modal, Multi-Omics Analytics for the Better Understanding of Metabolic Diseases," between November 2021 and July 2023.

Original language	English
Article number	1266557
Number of pages	3
Journal	Frontiers in Endocrinology
Volume	14
DOIs	https://doi.org/10.3389/fendo.2023.1266557
Publication status	Published - 8 Sept 2023

Bibliographical note

Funding:
The authors acknowledge support from the NIHR Birmingham ECMC, NIHR Birmingham SRMRC, Nanocommons H2020-EU (731032), MAESTRIA (Grant agreement ID 965286), HYPERMARKER (Grant agreement ID 101095480), PARC (Grant Agreement No. 101057014), and the MRC Heath Data Research UK (HDRUK/CFC/01), an initiative funded by UK Research and Innovation, the Department of Health and Social Care (England) and the devolved administrations, and leading medical research charities. The views expressed in this publication are those of the authors and do not necessarily represent those of the NHS, the National Institute for Health Research, the Medical Research Council, or the Department of Health.

Keywords

biomarker
Diagnostic
Metabolic Health
NAFLD
Type 2 Diabetes
obesity

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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1 Finished
2 Active

Hypermarker: Personalised pharmacometabolomic optimisation of treatment for hypertension
Acharjee, A., Gkoutos, G., Mobley, A. & Kotecha, D.
UKRI Horizon Europe Underwriting â€“ Innovate UK
1/01/23 → 31/12/26
Project: Research
MAESTRIA: Machine Learning Artificial Intelligence Early Detection Stroke Atrial Fibrillation
Kirchhof, P., Gkoutos, G. & Fabritz, L.
European Commission
1/03/21 → 28/02/26
Project: EU
H2020_RIA_NANOCOMMONS_CO-ORDINATOR
Valsami-Jones, E., Lynch, I. & Gkoutos, G.
European Commission
1/01/18 → 30/06/22
Project: EU

Cite this

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title = "Editorial: Integrative Multi-Modal and Multi-Omics Analytics for the Better Understanding of Metabolic Diseases",

abstract = "In the past few years, large-scale, high-throughput multi-omics experiments and improved clinical measurements have led to the generation of a plethora of multi-modal data sets related to many metabolic diseases (MetS), for example type 1 diabetes (T1D), obesity, non-alcoholic fatty liver disease (NAFLD), etc. In literature there are many integration strategies discussed for example early, intermediate and late integration [1]. The process of early integration involves merging multiple omics information into a unified matrix whereas intermediate integration involves transforming the source datasets into representations that are both common and specific to omics. The late integration involves the individual analysis of each omics dataset, followed by the combination of their respective predictions to get a result [1]. In the Figure 1 an example of late integration described in the context of MetS.This editorial summarizes the contribution to the special issue of Frontiers in Endocrinology, {"}Integrative Multi-Modal, Multi-Omics Analytics for the Better Understanding of Metabolic Diseases,{"} between November 2021 and July 2023. ",

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note = "Funding: The authors acknowledge support from the NIHR Birmingham ECMC, NIHR Birmingham SRMRC, Nanocommons H2020-EU (731032), MAESTRIA (Grant agreement ID 965286), HYPERMARKER (Grant agreement ID 101095480), PARC (Grant Agreement No. 101057014), and the MRC Heath Data Research UK (HDRUK/CFC/01), an initiative funded by UK Research and Innovation, the Department of Health and Social Care (England) and the devolved administrations, and leading medical research charities. The views expressed in this publication are those of the authors and do not necessarily represent those of the NHS, the National Institute for Health Research, the Medical Research Council, or the Department of Health.",

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N1 - Funding: The authors acknowledge support from the NIHR Birmingham ECMC, NIHR Birmingham SRMRC, Nanocommons H2020-EU (731032), MAESTRIA (Grant agreement ID 965286), HYPERMARKER (Grant agreement ID 101095480), PARC (Grant Agreement No. 101057014), and the MRC Heath Data Research UK (HDRUK/CFC/01), an initiative funded by UK Research and Innovation, the Department of Health and Social Care (England) and the devolved administrations, and leading medical research charities. The views expressed in this publication are those of the authors and do not necessarily represent those of the NHS, the National Institute for Health Research, the Medical Research Council, or the Department of Health.

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N2 - In the past few years, large-scale, high-throughput multi-omics experiments and improved clinical measurements have led to the generation of a plethora of multi-modal data sets related to many metabolic diseases (MetS), for example type 1 diabetes (T1D), obesity, non-alcoholic fatty liver disease (NAFLD), etc. In literature there are many integration strategies discussed for example early, intermediate and late integration [1]. The process of early integration involves merging multiple omics information into a unified matrix whereas intermediate integration involves transforming the source datasets into representations that are both common and specific to omics. The late integration involves the individual analysis of each omics dataset, followed by the combination of their respective predictions to get a result [1]. In the Figure 1 an example of late integration described in the context of MetS.This editorial summarizes the contribution to the special issue of Frontiers in Endocrinology, "Integrative Multi-Modal, Multi-Omics Analytics for the Better Understanding of Metabolic Diseases," between November 2021 and July 2023.

AB - In the past few years, large-scale, high-throughput multi-omics experiments and improved clinical measurements have led to the generation of a plethora of multi-modal data sets related to many metabolic diseases (MetS), for example type 1 diabetes (T1D), obesity, non-alcoholic fatty liver disease (NAFLD), etc. In literature there are many integration strategies discussed for example early, intermediate and late integration [1]. The process of early integration involves merging multiple omics information into a unified matrix whereas intermediate integration involves transforming the source datasets into representations that are both common and specific to omics. The late integration involves the individual analysis of each omics dataset, followed by the combination of their respective predictions to get a result [1]. In the Figure 1 an example of late integration described in the context of MetS.This editorial summarizes the contribution to the special issue of Frontiers in Endocrinology, "Integrative Multi-Modal, Multi-Omics Analytics for the Better Understanding of Metabolic Diseases," between November 2021 and July 2023.

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KW - Diagnostic

KW - Metabolic Health

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Editorial: Integrative Multi-Modal and Multi-Omics Analytics for the Better Understanding of Metabolic Diseases

Abstract

Bibliographical note

Keywords

UN SDGs

Access to Document

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Projects

Hypermarker: Personalised pharmacometabolomic optimisation of treatment for hypertension

MAESTRIA: Machine Learning Artificial Intelligence Early Detection Stroke Atrial Fibrillation

H2020_RIA_NANOCOMMONS_CO-ORDINATOR

Cite this