Matthew Bracher-Smith
Research Associate, Division of Psychological Medicine and Clinical Neurosciences
Publication
2024
- Zhao, Y., Bracher-Smith, M., Li, Y., Harvey, K., Escott-Price, V., Lewis, P. A. and Manzoni, C. 2024. Transcriptomics and weighted protein network analyses of the LRRK2 protein interactome reveal distinct molecular signatures for sporadic and LRRK2 Parkinson’s Disease. npj Parkinson's Disease 10(1), article number: 144. (10.1038/s41531-024-00761-8)
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)
Articles
- Zhao, Y., Bracher-Smith, M., Li, Y., Harvey, K., Escott-Price, V., Lewis, P. A. and Manzoni, C. 2024. Transcriptomics and weighted protein network analyses of the LRRK2 protein interactome reveal distinct molecular signatures for sporadic and LRRK2 Parkinson’s Disease. npj Parkinson's Disease 10(1), article number: 144. (10.1038/s41531-024-00761-8)
- 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)
Thesis
- Smith, M. 2021. Machine learning for genetic prediction of schizophrenia. PhD Thesis, Cardiff University.