Matthew Bracher-Smith
Cydymaith Ymchwil, Is-adran Meddygaeth Seicolegol a Niwrowyddorau Clinigol
- SmithMR5@caerdydd.ac.uk
- Adeilad Hadyn Ellis, Heol Maendy, Caerdydd, CF24 4HQ
Cyhoeddiad
2023
- Steventon, J. J. et al. 2023. Menopause age, reproductive span and hormone therapy duration predict the volume of medial temporal lobe brain structures in postmenopausal women. Psychoneuroendocrinology 158, article number: 106393. (10.1016/j.psyneuen.2023.106393)
- Caseras, X., Legge, S., Bracher-Smith, M., Anney, R., Owen, M., Escott-Price, V. and Kirov, G. 2023. Copy Number Variants increasing risk for schizophrenia: shared and distinct effects on brain morphometry and cognitive performance. Biological Psychiatry: Global Open Science 3(4), pp. 902-911. (10.1016/j.bpsgos.2022.10.006)
- Harrison, J. R. et al. 2023. Pathway-specific polygenic scores for Alzheimer's disease are associated with changes in brain structure in younger and older adults. Brain Communications 5(5), article number: fcad229. (10.1093/braincomms/fcad229)
- Donnelly, N. et al. 2023. Identifying the neurodevelopmental and psychiatric signatures of genomic disorders associated with intellectual disability: a machine learning approach. Molecular Autism 14(1), article number: 19. (10.1186/s13229-023-00549-2)
2022
- Bracher-Smith, M. et al. 2022. Whole genome analysis in APOE4 homozygotes identifies the DAB1-RELN pathway in Alzheimer’s disease pathogenesis. Neurobiology of Aging 119, pp. 67-76. (10.1016/j.neurobiolaging.2022.07.009)
- Bracher-Smith, M. et al. 2022. Machine learning for prediction of schizophrenia using genetic and demographic factors in the UK Biobank. Schizophrenia Research 246, pp. 156-164. (10.1016/j.schres.2022.06.006)
2021
- Smith, M. 2021. Machine learning for genetic prediction of schizophrenia. PhD Thesis, Cardiff University.
- Bracher-Smith, M., Crawford, K. and Escott-Price, V. 2021. Machine learning for genetic prediction of psychiatric disorders: a systematic review. Molecular Psychiatry 26, pp. 70-79. (10.1038/s41380-020-0825-2)
Erthyglau
- Steventon, J. J. et al. 2023. Menopause age, reproductive span and hormone therapy duration predict the volume of medial temporal lobe brain structures in postmenopausal women. Psychoneuroendocrinology 158, article number: 106393. (10.1016/j.psyneuen.2023.106393)
- Caseras, X., Legge, S., Bracher-Smith, M., Anney, R., Owen, M., Escott-Price, V. and Kirov, G. 2023. Copy Number Variants increasing risk for schizophrenia: shared and distinct effects on brain morphometry and cognitive performance. Biological Psychiatry: Global Open Science 3(4), pp. 902-911. (10.1016/j.bpsgos.2022.10.006)
- Harrison, J. R. et al. 2023. Pathway-specific polygenic scores for Alzheimer's disease are associated with changes in brain structure in younger and older adults. Brain Communications 5(5), article number: fcad229. (10.1093/braincomms/fcad229)
- Donnelly, N. et al. 2023. Identifying the neurodevelopmental and psychiatric signatures of genomic disorders associated with intellectual disability: a machine learning approach. Molecular Autism 14(1), article number: 19. (10.1186/s13229-023-00549-2)
- Bracher-Smith, M. et al. 2022. Whole genome analysis in APOE4 homozygotes identifies the DAB1-RELN pathway in Alzheimer’s disease pathogenesis. Neurobiology of Aging 119, pp. 67-76. (10.1016/j.neurobiolaging.2022.07.009)
- Bracher-Smith, M. et al. 2022. Machine learning for prediction of schizophrenia using genetic and demographic factors in the UK Biobank. Schizophrenia Research 246, pp. 156-164. (10.1016/j.schres.2022.06.006)
- Bracher-Smith, M., Crawford, K. and Escott-Price, V. 2021. Machine learning for genetic prediction of psychiatric disorders: a systematic review. Molecular Psychiatry 26, pp. 70-79. (10.1038/s41380-020-0825-2)
Gosodiad
- Smith, M. 2021. Machine learning for genetic prediction of schizophrenia. PhD Thesis, Cardiff University.