Professor Michael BarnesProfessor of Bioinformatics and Director of the Centre for Translational BioinformaticsCentre: Clinical Pharmacology and Precision Medicine, Translational BioinformaticsEmail: m.r.barnes@qmul.ac.ukTelephone: +44(0) 20 7882 2059Website: https://www.qmul.ac.uk/c4tb/Twitter: @thebarneslabProfileResearchKey PublicationsSponsorsCollaboratorsProfileMichael co-leads the Centre for Translational Bioinformatics (C4TB), at Queen Mary University of London and is a Fellow of the Digitial Environment Research Institute, QMUL. His team work across diverse research areas, including genomics, drug discovery, stratified medicine, machine learning and health informatics with a unified objective to drive forward translation into the clinic. He brings an industrial perspective to the C4TB, drawn from 16 years of leadership of bioinformatics teams in the pharmaceutical industry. Michael is a Fellow of the Alan Turing Institute and an HDR-UK Investigator, and co-leads the Genomics England Stratified medicine genomic interpretation clinical partnership. He has an active portfolio of stratified medicine projects as a co-investigator and data integration lead on several MRC projects, including MRC PSORT (Psoriasis), MRC RA-Map (RA), MRC MATURA (RA), and CLUSTER (Juvenile Arthritis). He also co-leads the NIHR AI-Multiply Consortium which is using AI to investigate the relationship between multiple long-term conditions and polypharmacy. He has served on the MRC Methodology Research Panel and Stratified Medicine panels and also advises on a number of project boards, including the UnitedHealth Group Pharmacogenetics advisory group, the Dutch Heart Foundation, the MRC-eMedLab HPC Cloud facility, the IMI etriks project, and the F1000 faculty. Research Group members Research staff: Claudia Cabrera, Rafael Henkin, Sandra Ng, Alisha Angdembe PhD Students: Hannah Nicholls, Ruchi Upmanyu, Sara Maserone, Katriona Goldmann, Amaya Syed Summary Dr Barnes has broad computational biology research interests spanning the translational research and drug discovery continuum, from genetic and genomic methods for target identification to clinical informatics, patient stratification and multiple long term conditions. Active research projects in his team fall into the following key areas: Pathway analysis of Genetic and Genomic data: Genome-wide association studies (GWAS) and exome studies of complex trait pathology and drug response, including, Hypertension, Arrhythmia and Cardiovascular disease. Exome and Whole Genome Re-sequencing: NGS pipeline development, Re-sequencing studies, causal variant identification and functional analysis. Multi-Omics and Stratified Medicine: RNA-seq based transcriptomic analysis, proteomics and epigenomics for biomarker identification and stratification of medicine response in Inflammatory diseases. Trauma Injury Research: Transcriptomic and epigenomic analysis of early response to trauma injury as determinant of patient clinical trajectory in critical care. Chemogenomics and Drug Discovery: Target identification and druggability assessment, Chemogenomic analysis and connectivity map analysis. Drug Repositioning. AI for Multiple Long Term Conditions (MLTC): Application of AI methods to MLTC research to explore MLTC pathology and identify intervention points. Clinical Informatics: Development of Clinical datamarts to support translational research and stratified medicine. Currently developing i2b2 and TranSMART based systems. Cloud Computing: Development of virtual machine infrastructure for flexible scientific cloud computing across omics and clinical informatics applications. AI and Machine Learning: Development and application of explainable machine learning and AI methodology to clinical and genomic data. Publications Leong IUS, Cabrera CP, Cipriani V et al. (2024). Large-Scale Pharmacogenomics Analysis of Patients With Cancer Within the 100,000 Genomes Project Combining Whole-Genome Sequencing and Medical Records to Inform Clinical Practice.. nameOfConference DOI: 10.1200/jco.23.02761 QMRO: qmroHref Wenteler A, Cabrera CP, Wei W et al. (2024). AI approaches for the discovery and validation of drug targets. nameOfConference DOI: 10.1017/pcm.2024.4 QMRO: https://qmro.qmul.ac.uk/xmlui/handle/123456789/100334 Henkin R, Goldmann K, Lewis M et al. (2024). shinyExprPortal: a configurable ‘shiny’ portal for sharing analysis of molecular expression data. nameOfConference DOI: 10.1093/bioinformatics/btae172 QMRO: https://qmro.qmul.ac.uk/xmlui/handle/123456789/99464 Shoop-Worrall SJW, Lawson-Tovey S, Wedderburn LR et al. (2024). Towards stratified treatment of JIA: machine learning identifies subtypes in response to methotrexate from four UK cohorts. nameOfConference DOI: 10.1016/j.ebiom.2023.104946 QMRO: https://qmro.qmul.ac.uk/xmlui/handle/123456789/100783 Calero-Díaz H, Hamad RA, Atallah C et al. (2023). Interpretable and robust hospital readmission predictions from Electronic Health Records. 2023 IEEE International Conference on Big Data (BigData) DOI: 10.1109/bigdata59044.2023.10386820 QMRO: qmroHref Goldmann K, Spiliopoulou A, Iakovliev A et al. (2024). Expression quantitative trait loci analysis in rheumatoid arthritis identifies tissue specific variants associated with severity and outcome. nameOfConference DOI: 10.1136/ard-2023-224540 QMRO: https://qmro.qmul.ac.uk/xmlui/handle/123456789/91722 Eto F, Samuel M, Henkin R et al. (publicationYear). Ethnic differences in early onset multimorbidity and associations with health service use, long-term prescribing, years of life lost, and mortality: A cross-sectional study using clustering in the UK Clinical Practice Research Datalink. nameOfConference DOI: 10.1371/journal.pmed.1004300 QMRO: https://qmro.qmul.ac.uk/xmlui/handle/123456789/93337 Beesley R, Feilding FL, Beesley R et al. (2024). Development and implementation of ‘A guide to PPIE – Early Integration into Research Proposals’ in a multi-disciplinary consortium. nameOfConference DOI: 10.1093/rheumatology/kead482 QMRO: qmroHref Ng S, Masarone S, Watson D et al. (2023). The benefits and pitfalls of machine learning for biomarker discovery. nameOfConference DOI: 10.1007/s00441-023-03816-z QMRO: qmroHref Lawson-Tovey S, Smith SL, Geifman N et al. (publicationYear). The successes and challenges of harmonising juvenile idiopathic arthritis (JIA) datasets to create a large-scale JIA data resource. nameOfConference DOI: 10.1186/s12969-023-00839-2 QMRO: qmroHref View Profile Publication Page Sponsors National Institute for Health and Care Research Medical Research Council CollaboratorsInternal Prof Mark Caulfield (WHRI) Prof Costantino Pitzalis (WHRI) Prof Leo Dunkel (WHRI) Prof Patricia Munroe (WHRI) Prof David Kelsell (Blizard) Prof Amrita Ahluwalia (WHRI) Prof Karim Brohi (Blizard) Prof David Van Heel (Blizard) Prof Silvia Marino (Blizard) Prof Sussan Nourshagh (WHRI) Prof Rupert Pearse (WHRI) Prof Steffen Petersen (WHRI) Prof Adam Timmis (WHRI) Dr Michael O'Dwyer (WHRI) Dr Myles Lewis (WHRI) External Prof Nick Reynolds (Newcastle) Prof John Issacs (Newcastle) Prof Christopher Griffiths (Manchester) Prof Ann Morgan (Leeds) Prof Folkert Asselbergs (UCL) Dr Brendan Keating (UPENN) Prof Jo Knight (Lancaster) Dr Michael Weale (KCL) Dr Gerome Breen (Institute of Psychiatry, KCL) Prof Gerhard Ecker (University of Vienna) Back to top