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Professor Graham Ball

Director of the Medical Technology Research Centre

Medical Technology Research Centre

Faculty of Health, Medicine and Social Care
Areas of Expertise:
Artificial Intelligence
Research Supervision:

Graham utilises computer based Machine Learning to search for genes and proteins that relate to disease and that can be used for the development of new drugs.

Email: [email protected]


Graham has over 25 years of experience in the development and application of Bioinformatic algorithms using Artificial Neural Networks (ANNs) and other machine learning techniques, with application to environmental modelling, medical diagnostics and business risk analysis. Also development of knowledge management and e-learning solutions incorporating aspects of the above technologies. Educated to PhD level and with post-doctoral experience in the subject areas described above, Graham seeks to apply this knowledge to develop simplified solutions to complex real-world problems. One of his current goals is to apply this knowledge to business solutions.

Research interests

Current research interests have focused on the development and application of machine learning algorithms using Artificial Neural Networks (ANNs) to medical diagnostics, systems biology and drug target discovery including:

  • Cancer systems biology and bioinformatics
  • Drug discovery using machine learning
  • Development of molecular digital twins for disease states
  • Identification of prognostic, diagnostic and predictive biomarkers for disease and therapy
  • Computational drug target discovery
  • Derivation and development of prognostic indices for cancer
  • Characterisation of tumours from immuno-histochemical markers in breast cancer
  • Molecular diagnostic modelling of cancer disease characteristics from mass spectrometry data of tissue and serum
  • Strain and species characterisation of bacterial pathogens from MS and sequence data
  • Modelling immune response in prostate cancer.

Areas of expertise

Use of Artificial intelligence:

  • to identify biological drug targets
  • to identify biomarkers
  • to model biological and biomedical systems.
Areas of research supervision

Application of Machine Learning and Artificial Intelligence to:

  • computational and systems biology
  • drug discovery
  • drug repurposing
  • diagnostics
  • microbial pathogen characterisation
  • species distribution mapping.
  • BSc (Hons) Applied Biology. Trent Polytechnic, Oct 1988 to June 1992, Upper Second Class, Awarded July 1992
  • PhD Application of Artificial Neural Networks to Plant Environment Interactions. Nottingham Trent University, Oct 1992 to June 1996, Pass, Awarded July 1996
Memberships, editorial boards
  • Fellow of the Royal Society of Biology
  • Editorial Board Member of Proteomics.
Research grants, consultancy, knowledge exchange
  • 2020-, Brain Research UK, Novel Targeted Approach to Inhibit Ferroptosis in a Rodent Model of Intracerebral Haemorrhage
  • 2021, British Council, Newton Bhabha visiting studentship programme
  • 2021, Innovate UK, Evaluation of in silico drug discovery processes from Omics data
  • 2020-2022, UCB pharma contract research, Using SWATH proteomics to identify biomarkers of novel anti-fibrotic molecule target engagement and provide clues to the mechanism of action
  • 2019-2021, NIHR, Evaluation of the SPAG5 biomarker in the clinical setting
  • 2018-2019, Innovate UK Tuberculosis 2, Translation of Bovine Tuberculosis biomarkers to the point of care setting
  • 2016-2018, Technology Strategy Board Sepsis 2, Validation of prognostic biomarkers for Sepsis. Lead by Karen Kempsall, Health Protection Agency
  • 2012-2015, MRC DPFS award, Nottingham Prognostic Index Plus (NPI+)
  • Chief Scientific Officer of Intelligent Omics Ltd.
Selected recent publications

315 abstracts, patents and journal publications, including 220 Journal publications h-index 47

  • A Ibrahim, AG Lashen, A Katayama, R Mihai, G Ball, MS Toss, EA Rakha. 2021. Defining the area of mitoses counting in invasive breast cancer using whole slide image Modern Pathology, 1-10
  • S Shiino, G Ball, et al, Prognostic significance of receptor expression discordance between primary and recurrent breast cancers: a meta-analysis. M Breast Cancer Research and Treatment, 1-14
  • A Aljohani, G Ball, AR Green, EA Rakha. Cyclin B2 Is a Key Gene in LymphoVascular Invasion in Breast Cancer. J.Path 255, S14-S14
  • Shuvolina Mukherjee, et al Comprehending meningioma signalling cascades using multipronged proteomics approaches & targeted validation of potential markers. Frontiers in Oncology, 10,1600, 2021
  • Tarek MA Abdel-Fatah, Graham R Ballet al,. 2021. Association of SpermAssociated Antigen 5 and Treatment Response in Patients With Estrogen Receptor–Positive Breast Cancer. JAMA network open. 3:7 e209486-e209486.
  • Mansour A Alsaleem, Graham Ball, et al. A novel prognostic two-gene signature for triple negative breast cancer. Modern Pathology. 1-13
  • Elsharawy, Khloud A, et al 2020. Prognostic significance of nucleolar assessment in invasive breast cancer. Histopathology, 76:5, 671-684.
  • Tong, Dong et al, 2020. Development of a Bioinformatics Framework for Identification and Validation of Genomic Biomarkers and Key Immunopathology Processes and Controllers in Infectious and Non-infectious Severe Inflammatory Response Syndrome, Frontiers in Immunology. 11. 380
  • Wagner, Sarah et al, A parsimonious 3-gene signature predicts clinical outcomes in an acute myeloid leukemia multicohort study, Blood Advances,3,8,1330-1346,2019
  • Wagner, Sarah; Ball, Graham R; Pockley, A Graham; Miles, Amanda K; Application of omic technologies in cancer research, Translational Medicine Reports,2,1,,2018
  • Martin, Stewart G. et al, Low expression of G protein-coupled oestrogen receptor 1 GPER) is associated with adverse survival of breast cancer patients, Oncotarget,9,40,25946,2018
  • Rakha, Emad Aet al, ,Breast cancer histologic grading using digital microscopy: concordance and outcome association, Journal of Clinical Pathology,71,8,680-686,2018
  • Thompson, Alastair M; et al, Impact of radiotherapy and endocrine therapy on further events: Final multivariate analysis of a prospective, national cohort study of screen detected ductal carcinoma in situ (DCIS) of the breast, CANCER RESEARCH,78,4,,2018
  • Jiang, Lu; Ball, Graham; Hodgman, Charlie; Coules, Anne; Zhao, Han; Lu, Chungui; ,Analysis of gene regulatory networks of maize in response to nitrogen, Genes,9,3,151,2018
  • Vadakekolathu, et al, MTSS1 and SCAMP1 cooperate to prevent invasion in breast cancer, Cell death & disease,9,3,344,2018
  • Furini, Giulia; et al, Proteomic profiling reveals the transglutaminase-2 externalization pathway in kidneys after unilateral ureteric obstruction, Journal of the American Society of Nephrology,29,3,880-905,2018
  • Bagnati, Marta et al, Glucolipotoxicity initiates pancreatic β-cell death through TNFR5/CD40-mediated STAT1 and NF-κB activation, Cell death & disease,7,8,e2329,2016
  • Tarek M A Abdel-Fatah, et al, SPAG5 as a prognostic biomarker and chemotherapy sensitivity predictor in breast cancer: a retrospective, integrated genomic, transcriptomic, and protein analysis. The Lancet Oncology 06/2016.
Recent presentations and conferences

Session chair and plenary presentation at Biomarkers UK, 3 and 4 May, London.

Media experience

Media Training Received in 2015. Presentations made on Radio and Television.