Dennis Wang

Dennis is an interdisciplinary computational researcher at the intersection of molecular biology, medicine and data science. He obtained his Bachelor of Science in Computer Science, Microbiology and Immunology from The University of British Columbia, and both his MPhil in Computational Biology and PhD in Biostatistics from the University of Cambridge. He joined the National Heart and Lung Institute of Imperial College London in 2022. His research focuses on organising high-dimensional datasets collected from patients in order to develop better ways of diagnosing and predicting outcomes of complex diseases (cardiovascular, neurological, infectious diseases and cancer).

Cardiovascular conditions such as hypertension are observed in ~44% of dementia cases. And although specific diseases occur in different areas of the body, and may seem unrelated, common genetic and molecular markers have been found in the patients of each disease when studied separately. Dennis leads an international team of computational scientists who train artificial intelligence (AI) to find common molecular and phenotypic features in patient data in order to predict multiple long-term health conditions or multimorbidities in individuals of the general population and at hospitals.

Research interests

Dennis holds an Academy of Medical Sciences Professorship which enables him to develop AI techniques that learn from multiple different patient datasets across UK regions and internationally in other continents. By bringing his experience of multi-ethnic patient studies from Singapore, he will be embedding equality, diversity and inclusion in the way data is collected and used to train AI. This will limit bias from influencing predictions, and therefore allow all groups of people to benefit equally from big data approaches in the health service.

Selected Publications

Journal Articles

• Rajab MD, Jammeh E, Taketa T, et al., 2022, Assessment of Alzheimer-related Pathologies of Dementia Using Machine Learning Feature Selection
• Kariotis S, Jammeh E, Swietlik EM, et al., 2021, Biological heterogeneity in idiopathic pulmonary arterial hypertension identified through unsupervised transcriptomic profiling of whole blood, Nature Communications, Vol:12, ISSN:2041-1723, Pages:1-14
• Errington N, Iremonger J, Pickworth JA, et al., 2021, A diagnostic miRNA signature for pulmonary arterial hypertension using a consensus machine learning approach, Ebiomedicine, Vol:69, ISSN:2352-3964
• Shepheard SR, Parker MD, Cooper-Knock J, et al., 2021, Value of systematic genetic screening of patients with amyotrophic lateral sclerosis, Journal of Neurology Neurosurgery and Psychiatry, Vol:92, ISSN:0022-3050, Pages:510-518
• Parker MD, Lindsey BB, Leary S, et al., 2021, Subgenomic RNA identification in SARS-CoV-2 genomic sequencing data, Genome Research, Vol:31, ISSN:1088-9051, Pages:645-658
• Wang D, Hensman J, Kutkaite G, et al., 2020, A statistical framework for assessing pharmacological responses and biomarkers using uncertainty estimates, Elife, Vol:9, ISSN:2050-084X
• Rhodes C, Otero-Núñez P, Wharton J, et al., 2020, Whole blood RNA profiles associated with pulmonary arterial hypertension and clinical outcome, American Journal of Respiratory and Critical Care Medicine, Vol:202, ISSN:1073-449X, Pages:586-594
• Freeman TM, Wang D, Harris J, et al., 2020, Genomic loci susceptible to systematic sequencing bias in clinical whole genomes, Genome Research, Vol:30, ISSN:1088-9051, Pages:415-426
• Wharton SB, Wang D, Parikh C, et al., 2019, Epidemiological pathology of A beta deposition in the ageing brain in CFAS: addition of multiple A beta-derived measures does not improve dementia assessment using logistic regression and machine learning approaches, Acta Neuropathologica Communications, Vol:7, ISSN:2051-5960
• Keshava N, Toh TS, Yuan H, et al., 2019, Defining subpopulations of differential drug response to reveal novel target populations, Npj Systems Biology and Applications, Vol:5
• Toh TS, Dondelinger F, Wang D, 2019, Looking beyond the hype: Applied AI and machine learning in translational medicine, Ebiomedicine, Vol:47, ISSN:2352-3964, Pages:607-615
• Wang D, Nhu-An P, Freeman TM, et al., 2019, Somatic Alteration Burden Involving Non-Cancer Genes Predicts Prognosis in Early-Stage Non-Small Cell Lung Cancer, Cancers, Vol:11
• Menden MP, Wang D, Mason MJ, et al., 2019, Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen, Nature Communications, Vol:10, ISSN:2041-1723