Dobson, Richard

Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls

The Wellcome Trust Case Control Consortium, Management Committee, Burton, P. R., Clayton, D. G., Cardon, L. R., Craddock, N., Deloukas, P., Duncanson, A., Kwiatkowski, D. P., McCarthy, M. I., et al. (2007) · Nature

Genetic Associations and EpidemiologyGenetic Mapping and Diversity in Plants and Animals

Cited by 9,442

Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer’s disease risk

Jansen, I. E., Savage, J. E., Watanabe, K., Bryois, J., Williams, D. M., Steinberg, S., Sealock, J., Karlsson, I. K., Hägg, S., Athanasiu, L., et al. (2019) · Nature Genetics

Alzheimer's disease research and treatmentsDementia and Cognitive Impairment Research

Cited by 2,277

A genome-wide association study with 1,126,563 individuals identifies new risk loci for Alzheimer’s disease

Wightman, D. P., Jansen, I. E., Savage, J. E., Shadrin, A. A., Bahrami, S., Holland, D., Rongve, A., Børte, S., Winsvold, B. S., Drange, O. K., et al. (2021) · Nature Genetics

Alzheimer's disease research and treatmentsDementia and Cognitive Impairment Research

Cited by 1,011

Functional annotation of the human brain methylome identifies tissue-specific epigenetic variation across brain and blood

Davies, M. N., Volta, M., Pidsley, R., Lunnon, K., Dixit, A., Lovestone, S., Coarfa, C., Harris, R. A., Milosavljevic, A., Troakes, C., et al. (2012) · Genome Biology

Epigenetics and DNA MethylationGenomics and Chromatin Dynamics

Cited by 630

Using Smartphones and Wearable Devices to Monitor Behavioral Changes During COVID-19

Sun, S., Folarin, A. A., Ranjan, Y., Rashid, Z., Conde, P., Stewart, C., Cummins, N., Matcham, F., Dalla Costa, G., Simblett, S., et al. (2020) · Journal of Medical Internet Research

Human Mobility and Location-Based AnalysisCOVID-19 Digital Contact Tracing

Cited by 256

Digital Health Tools for the Passive Monitoring of Depression: A Systematic Review of Methods

de Angel, V., Lewis, S., White, K., Oetzmann, C., Leightley, D., Oprea, E., Lavelle, G., Matcham, F., Pace, A., Mohr, D. C., et al. (2021) · npj Digital Medicine

Digital Mental Health InterventionsMental Health Research Topics

Cited by 227

Alzheimer's disease biomarker discovery using SOMAscan multiplexed protein technology

Sattlecker, M., Kiddle, S. J., Newhouse, S., Proitsi, P., Nelson, S., Williams, S., Johnston, C., Killick, R., Simmons, A., Westman, E., et al. (2014) · Alzheimer's & Dementia

Dementia and Cognitive Impairment ResearchAlzheimer's disease research and treatments

Cited by 223

Clusterin regulates β-amyloid toxicity via Dickkopf-1-driven induction of the wnt–PCP–JNK pathway

Killick, R., Ribe, E. M., Al-Shawi, R., Malik, B., Hooper, C., Fernandes, C., Dobson, R., Nolan, P. M., Lourdusamy, A., Furney, S., et al. (2014) · Molecular Psychiatry

Alzheimer's disease research and treatmentsClusterin in disease pathology

Cited by 222

Mitochondrial genes are altered in blood early in Alzheimer's disease

Lunnon, K., Keohane, A., Pidsley, R., Newhouse, S., Riddoch-Contreras, J., Thubron, E. B., Devall, M., Soininen, H., Kłoszewska, I., Mecocci, P., et al. (2017) · Neurobiology of Aging

Mitochondrial Function and PathologyAlzheimer's disease research and treatments

Cited by 190

AI chatbots not yet ready for clinical use.

Au Yeung, J., Kraljevic, Z., Luintel, A., Balston, A., Idowu, E., Dobson, R. J., & Teo, J. T. (2023) · Frontiers in Digital Health

Artificial Intelligence in Healthcare and EducationMachine Learning in Healthcare

Cited by 176

AI chatbots not yet ready for clinical use

Yeung, J. A., Kraljevic, Z., Luintel, A., Balston, A., Idowu, E., Dobson, R. J., & Teo, J. T. (2023) · Frontiers in Digital Health

Artificial Intelligence in Healthcare and EducationMachine Learning in Healthcare

Cited by 176

Foresight-a generative pretrained transformer for modelling of patient timelines using electronic health records: a retrospective modelling study

Kraljevic, Z., Bean, D., Shek, A., Bendayan, R., Hemingway, H., Yeung, J. A., Deng, A., Balston, A., Ross, J., Idowu, E., et al. (2024) · The Lancet Digital Health

Machine Learning in HealthcareMedical Coding and Health Information

Cited by 169

Male-biased autosomal effect of 16p13.11 copy number variation in neurodevelopmental disorders

Tropeano, M., Ahn, J. W., Dobson, R. J. B., Breen, G., Rucker, J., Dixit, A., Pal, D. K., McGuffin, P., Farmer, A., White, P. S., et al. (2013) · PLoS ONE

Genomic variations and chromosomal abnormalitiesGenetics and Neurodevelopmental Disorders

Cited by 154

Circulating Proteomic Signatures of Chronological Age

Menni, C., Kiddle, S. J., Mangino, M., Viñuela, A., Psatha, M., Steves, C., Sattlecker, M., Buil, A., Newhouse, S., Nelson, S., et al. (2015) · The Journals of Gerontology: Series A

Genetics, Aging, and Longevity in Model OrganismsAging and Gerontology Research

Cited by 144

CogStack - experiences of deploying integrated information retrieval and extraction services in a large National Health Service Foundation Trust hospital

Jackson, R., Kartoglu, I., Stringer, C., Gorrell, G., Roberts, A., Song, X., Wu, H., Agrawal, A., Lui, K., Groza, T., et al. (2018) · BMC Medical Informatics and Decision Making

Electronic Health Records SystemsBiomedical Text Mining and Ontologies

Cited by 134

Knowledge graph prediction of unknown adverse drug reactions and validation in electronic health records

Bean, D. M., Wu, H., Iqbal, E., Dzahini, O., Ibrahim, Z. M., Broadbent, M., Stewart, R., & Dobson, R. J. B. (2017) · Scientific Reports

Pharmacovigilance and Adverse Drug ReactionsComputational Drug Discovery Methods

Cited by 127

The EMIF-AD Multimodal Biomarker Discovery study: design, methods and cohort characteristics

Bos, I., Vos, S., Vandenberghe, R., Scheltens, P., Engelborghs, S., Frisoni, G., Molinuevo, J. L., Wallin, A., Lleó, A., Popp, J., et al. (2018) · Alzheimer's Research & Therapy

Dementia and Cognitive Impairment ResearchAlzheimer's disease research and treatments

Cited by 112

Remote Assessment of Disease and Relapse in Major Depressive Disorder (RADAR-MDD): recruitment, retention, and data availability in a longitudinal remote measurement study

Matcham, F., Leightley, D., Siddi, S., Lamers, F., White, K. M., Annas, P., de Girolamo, G., Difrancesco, S., Haro, J. M., Horsfall, M., et al. (2022) · BMC Psychiatry

Digital Mental Health InterventionsMental Health Research Topics

Cited by 110

C9orf72 intermediate expansions of 24–30 repeats are associated with ALS

Initiative, A. D. N., Iacoangeli, A., Al Khleifat, A., Jones, A. R., Sproviero, W., Shatunov, A., Opie-Martin, S., Morrison, K. E., Shaw, P. J., Shaw, C. E., et al. (2019) · Acta Neuropathologica Communications

Amyotrophic Lateral Sclerosis ResearchGenetic Neurodegenerative Diseases

Cited by 102

A survey on clinical natural language processing in the United Kingdom from 2007 to 2022

Wu, H., Wang, M., Wu, J., Francis, F., Chang, Y.-H., Shavick, A., Dong, H., Poon, M. T. C., Fitzpatrick, N., Levine, A. P., et al. (2022) · npj Digital Medicine

Biomedical Text Mining and OntologiesMachine Learning in Healthcare

Cited by 101

Plasma Based Markers of [11C] PiB-PET Brain Amyloid Burden

Kiddle, S. J., Thambisetty, M., Simmons, A., Riddoch-Contreras, J., Hye, A., Westman, E., Pike, I., Ward, M., Johnston, C., Lupton, M. K., et al. (2012) · PLoS ONE

Dementia and Cognitive Impairment ResearchAlzheimer's disease research and treatments

Cited by 100

An epigenome-wide association study of Alzheimer's disease blood highlights robust DNA hypermethylation in the HOXB6 gene

Roubroeks, J. A. Y., Smith, A. R., Smith, R. G., Pishva, E., Ibrahim, Z., Sattlecker, M., Hannon, E. J., Kłoszewska, I., Mecocci, P., Soininen, H., et al. (2020) · Neurobiology of Aging

Epigenetics and DNA MethylationDementia and Cognitive Impairment Research

Cited by 82

Predicting depressive symptom severity through individuals’ nearby Bluetooth device count data collected by mobile phones: preliminary longitudinal study

Zhang, Y., Folarin, A. A., Sun, S., Cummins, N., Ranjan, Y., Rashid, Z., Conde, P., Stewart, C., Laiou, P., Matcham, F., et al. (2021) · JMIR mHealth and uHealth

Mental Health Research TopicsDigital Mental Health Interventions

Cited by 68

Natural Language Processing for Mimicking Clinical Trial Recruitment in Critical Care: A Semi-Automated Simulation Based on the LeoPARDS Trial

Tissot, H. C., Shah, A. D., Brealey, D., Harris, S., Agbakoba, R., Folarin, A., Romao, L., Roguski, L., Dobson, R., & Asselbergs, F. W. (2020) · IEEE Journal of Biomedical and Health Informatics

Machine Learning in HealthcareElectronic Health Records Systems

Cited by 61