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Vivian-Griffiths, Timothy, Baker, Emily, Schmidt, Karl M. ORCID: https://orcid.org/0000-0002-0227-3024, Bracher-Smith, Matthew, Walters, James ORCID: https://orcid.org/0000-0002-6980-4053, Artemiou, Andreas ORCID: https://orcid.org/0000-0002-7501-4090, Holmans, Peter ORCID: https://orcid.org/0000-0003-0870-9412, O'Donovan, Michael C. ORCID: https://orcid.org/0000-0001-7073-2379, Owen, Michael J. ORCID: https://orcid.org/0000-0003-4798-0862, Pocklington, Andrew ORCID: https://orcid.org/0000-0002-2137-0452 and Escott-Price, Valentina ORCID: https://orcid.org/0000-0003-1784-5483 2019. Predictive modeling of schizophrenia from genomic data: Comparison of polygenic risk score with kernel support vector machines approach. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics 180 (1) , pp. 80-85. 10.1002/ajmg.b.32705
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Vivian-Griffiths, Timothy 2017. Investigating the ability of machine learning techniques to provide insight into the aetiology of complex psychiatric genetic disorders. PhD Thesis, Cardiff University.
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Vivian-Griffiths, Timothy, Escott-Price, Valentina ORCID: https://orcid.org/0000-0003-1784-5483, Walters, James Tynan Rhys ORCID: https://orcid.org/0000-0002-6980-4053, Moran, J., McCarroll, S., O'Donovan, Michael Conlon ORCID: https://orcid.org/0000-0001-7073-2379, Owen, Michael John ORCID: https://orcid.org/0000-0003-4798-0862 and Pocklington, Andrew ORCID: https://orcid.org/0000-0002-2137-0452 2015. Utilising machine-learning algorithms to uncover complex genetic interactions in schizophrenia [Conference Abstract]. Human Heredity 79 (1) , p. 48. 10.1159/000381109

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