Projects per year
Abstract
With the development of modern high-throughput omic measurement platforms, it has become essential for biomedical studies to undertake an integrative (combined) approach to fully utilise these data to gain insights into biological systems. Data from various omics sources such as genetics, proteomics, and metabolomics can be integrated to unravel the intricate working of systems biology using machine learning-based predictive algorithms. Machine learning methods offer novel techniques to integrate and analyse the various omics data enabling the discovery of new biomarkers. These biomarkers have the potential to help in accurate disease prediction, patient stratification and delivery of precision medicine. This review paper explores 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. It provides insight and recommendations for interdisciplinary professionals who envisage employing machine learning skills in multi-omics studies.
Original language | English |
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Article number | 107739 |
Number of pages | 24 |
Journal | Biotechnology Advances |
Volume | 49 |
Early online date | 29 Mar 2021 |
DOIs | |
Publication status | Published - Jul 2021 |
Keywords
- Machine Learning
- Multi-omics
- Predictive Modelling
- Supervised Learning
- Systems Biology
- Unsupervised Learning
ASJC Scopus subject areas
- Biotechnology
- Bioengineering
- Applied Microbiology and Biotechnology
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MICA: InterdisciPlInary Collaboration for EfficienT and Effective Use of Clinical Images in Big Data Health Care RESearch: PICTURES (Programme Grant) (Joint with Universities of Edinburgh and Abertay)
Doney, A. (Investigator), Jefferson, E. (Investigator), Palmer, C. (Investigator), Steele, D. (Investigator), Trucco, M. (Investigator) & Wang, H. (Investigator)
1/08/19 → 28/02/25
Project: Research
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Scotland India Diabetes Health Informatics Unit (joint with Madras Diabetes Research Foundation)
Doney, A. (Investigator), McCrimmon, R. (Investigator), Palmer, C. (Investigator), Pearson, E. (Investigator) & Trucco, M. (Investigator)
1/06/17 → 30/09/21
Project: Research
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Omics-Based Strategies for Improved Diagnosis and Treatment of Endocrine Hypertension (ENSAT-HT) (Joint with Inserm, University of Torino, University of Padua, University of Glasgow, University of Birmingham, Radboud University Medical Centre, SleekIT Limited and Inserm Transfert)
Connell, J. (Investigator), Doney, A. (Investigator), Jefferson, E. (Investigator) & Zhou, K. (Investigator)
COMMISSION OF THE EUROPEAN COMMUNITIES
1/05/15 → 31/12/21
Project: Research