McGarry, Kenneth - Sunderland University - Department of Pharmacy, Health and Well-being

个人简介

Dr Ken McGarry is a Senior Lecturer in Statistics in Health Science in the School of Pharmacy. He teaches statistics and research methods to UG and PG students and also advises staff and PhD students on statistical issues related to their research. This includes a wide range of subjects from biostatistics to economic data. His own interests include the data analysis of biostatistical data for constructing bioinformatics models of protein interactions using a variety of computational techniques.

研究领域

Ken's research interests include data mining and knowledge discovery from databases, especially bioinformatics data. Any prospective PhD applicant interested in bioinformatics, data mining, medical statistics or computational statistics should contact me by email or by phone for a discussion on relevant topics. I am particularly interested in constructing bioinformatic models of protein interactions using a variety of computational techniques. Genes and proteins tend to cooperate in various functions within the cell in specific modules, a particular gene may participate in several modules. This leads to the concept of important hub genes, e.g. a failure of one gene may lead to several functional modules being disrupted or impaired. This in turn leads onto the concept of networks of disease, i.e. having a particular disease may involve several other problems. Identification of protein involvement in various biological processes is key to designing effective medicines. Various computational methods from statistics and data mining can be used to construct models to explain cellular processes: techniques such as classification, clustering, Bayesian networks, graph theory, information theory, bootstrapping can all be used. Additionally, biological supporting knowledge is available in the form of several ontologies such as Gene Ontology, Disease Ontology, Cell Ontology and others, these can be integrated to provide insights and guidance on discoveries made by the data mining process. Suitable work for a PhD programme could involve designing novel algorithms, modifications to existing algorithms, combining techniques in a hybrid system, or developing new methods of data and knowledge integration in this exciting new field.

近期论文

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Khan, Nabeel, Naqvi, Atta, Ahmad, Rizwan, Ahmed, Farrukh, McGarry, Kenneth, Fazlani, Raafia and Ahsan, Mahrukh (2016) Perceptions and Attitudes of Medical Sales Representatives (MSRs) and Prescribers Regarding Pharmaceutical Sales Promotion and Prescribing Practices in Pakistan. Journal of Young Pharmacists, 8 (3). pp. 244-250. ISSN 0975-1483 Horridge, Karen, McGarry, Kenneth, Williams, Jane and Whittlingum, Gabriel (2016) Prospective pilots of routine data captures by paediatricians in clinics and validation of the disability complexity scale. Developmental Medicine and Child Neurology, 58 (6). pp. 12-19. ISSN 1469-8749 Horridge, Karen, Harvey, Carl, McGarry, Kenneth, Williams, Jane, Whittlingum, Gabriel, Busk, Mary, Fox, Suzanne, Baird, Gillian and Spencer, Andy (2016) Quantifying multi-facted needs in a district disability clinic population: analysis of data captured at point of care and development of a disabilities terminology set and disabilities complexity scale. Developmental Medicine and Child Neurology, 58 (6). pp. 570-580. ISSN 1469-8749 Steadman, Sarah, Ahmed, Imran, McGarry, Kenneth and Rasia, S (2016) Is screening for urine infection in well infants with prolonged jaundice required? Local review and meta-analysis of existing data. Archives of Disease in Childhood, 101. ISSN 1468 2044 McGarry, Kenneth, Rashid, Anaum and Smith, Hannah (2016) Computational Methods for Drug Re-positioning. Drug Target Review, 3. pp. 31-33. ISSN 2059-1349 Graham, Yitka, Mansour, D., Small, P.K., Hinshaw, Kim, Gatiss, S., Mahawar, K.K., McGarry, Kenneth and Wilkes, Scott (2016) A survey of bariatric surgical and reproductive health professionals' knowledge and provision of contraception to reproductive-aged bariatric surgical patients. Obesity Surgery.