Matthew Bracher-Smith
Timau a rolau for Matthew Bracher-Smith
Cydymaith Ymchwil
Cyhoeddiad
2026
- Bracher‐Smith, M. and Escott‐Price, V. 2026. Applications of machine learning in the genetics of brain disorders. In: Su, L. ed. Artificial Intelligence in Neuroscience. Wiley. , pp.205-230. (10.1002/9781394278886.ch08)
2025
- Watt, J. K. et al., 2025. Thiazides as a potential drug class for dementia repurposing trials. Evidence from clinical, research cohort, and genetic data [Conference Abstract]. Cerebral Circulation - Cognition & Behavior 9 100507. (10.1016/j.cccb.2025.100507)
- Bracher-Smith, M. and Escott-Price, V. 2025. Decoding the genomic symphony: unravelling brain disorders through data integration and machine learning. Molecular Psychiatry 30 , pp.5914-5925. (10.1038/s41380-025-03330-4)
- Harrison, J. R. et al. 2025. White matter microstructure in mid- to late adulthood is influenced by pathway-stratified polygenic risk for Alzheimer’s disease. Frontiers in Neuroscience 19 1638503. (10.3389/fnins.2025.1638503)
- Raykov, P. P. et al., 2025. Complementary MR measures of white matter and their relation to cardiovascular health and cognition. Scientific Reports 15 (1) 28890. (10.1038/s41598-025-13610-2)
- Bracher-Smith, M. et al. 2025. Machine learning in Alzheimer’s disease genetics. Nature Communications 16 (1) 6726. (10.1038/s41467-025-61650-z)
2024
- Zhao, Y. et al., 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) 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 106393. (10.1016/j.psyneuen.2023.106393)
- Caseras, X. et al. 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) 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) 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
- 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)
- Smith, M. 2021. Machine learning for genetic prediction of schizophrenia. PhD Thesis , Cardiff University.
Adrannau llyfrau
- Bracher‐Smith, M. and Escott‐Price, V. 2026. Applications of machine learning in the genetics of brain disorders. In: Su, L. ed. Artificial Intelligence in Neuroscience. Wiley. , pp.205-230. (10.1002/9781394278886.ch08)
Erthyglau
- Watt, J. K. et al., 2025. Thiazides as a potential drug class for dementia repurposing trials. Evidence from clinical, research cohort, and genetic data [Conference Abstract]. Cerebral Circulation - Cognition & Behavior 9 100507. (10.1016/j.cccb.2025.100507)
- Bracher-Smith, M. and Escott-Price, V. 2025. Decoding the genomic symphony: unravelling brain disorders through data integration and machine learning. Molecular Psychiatry 30 , pp.5914-5925. (10.1038/s41380-025-03330-4)
- Harrison, J. R. et al. 2025. White matter microstructure in mid- to late adulthood is influenced by pathway-stratified polygenic risk for Alzheimer’s disease. Frontiers in Neuroscience 19 1638503. (10.3389/fnins.2025.1638503)
- Raykov, P. P. et al., 2025. Complementary MR measures of white matter and their relation to cardiovascular health and cognition. Scientific Reports 15 (1) 28890. (10.1038/s41598-025-13610-2)
- Bracher-Smith, M. et al. 2025. Machine learning in Alzheimer’s disease genetics. Nature Communications 16 (1) 6726. (10.1038/s41467-025-61650-z)
- Zhao, Y. et al., 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) 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 106393. (10.1016/j.psyneuen.2023.106393)
- Caseras, X. et al. 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) 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) 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.