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Publications

Dysregulated Immune Proteins in Plasma in the UK Biobank Predict Multiple Myeloma 12 years Before Clinical Diagnosis

Fieggen J. et al, (2025)

Aligning, autoencoding and prompting large language models for novel disease reporting

Liu F. et al, (2025), Ieee transactions on pattern analysis and machine intelligence

Exploring the effectiveness of instruction tuning in biomedical language processing

Rohanian O. et al, (2024), Artificial intelligence in medicine, 158

At-admission prediction of mortality and pulmonary embolism in an international cohort of hospitalised patients with COVID-19 using statistical and machine learning methods.

Mesinovic M. et al, (2024), Sci rep, 14

Mitigating machine learning bias between high income and low-middle income countries for enhanced model fairness and generalizability.

Yang J. et al, (2024), Sci rep, 14

Data from Assessing the Value of Incorporating a Polygenic Risk Score with Nongenetic Factors for Predicting Breast Cancer Diagnosis in the UK Biobank

Collister JA. et al, (2024)

Data from Assessing the Value of Incorporating a Polygenic Risk Score with Nongenetic Factors for Predicting Breast Cancer Diagnosis in the UK Biobank

Collister JA. et al, (2024)

Figure 1 from Assessing the Value of Incorporating a Polygenic Risk Score with Nongenetic Factors for Predicting Breast Cancer Diagnosis in the UK Biobank

Collister JA. et al, (2024)

Figure 1 from Assessing the Value of Incorporating a Polygenic Risk Score with Nongenetic Factors for Predicting Breast Cancer Diagnosis in the UK Biobank

Collister JA. et al, (2024)

Figure 2 from Assessing the Value of Incorporating a Polygenic Risk Score with Nongenetic Factors for Predicting Breast Cancer Diagnosis in the UK Biobank

Collister JA. et al, (2024)

Figure 2 from Assessing the Value of Incorporating a Polygenic Risk Score with Nongenetic Factors for Predicting Breast Cancer Diagnosis in the UK Biobank

Collister JA. et al, (2024)

Figure 3 from Assessing the Value of Incorporating a Polygenic Risk Score with Nongenetic Factors for Predicting Breast Cancer Diagnosis in the UK Biobank

Collister JA. et al, (2024)

Figure 3 from Assessing the Value of Incorporating a Polygenic Risk Score with Nongenetic Factors for Predicting Breast Cancer Diagnosis in the UK Biobank

Collister JA. et al, (2024)

Supplementary Figure S1 from Assessing the Value of Incorporating a Polygenic Risk Score with Nongenetic Factors for Predicting Breast Cancer Diagnosis in the UK Biobank

Collister JA. et al, (2024)

Supplementary Figure S1 from Assessing the Value of Incorporating a Polygenic Risk Score with Nongenetic Factors for Predicting Breast Cancer Diagnosis in the UK Biobank

Collister JA. et al, (2024)

Supplementary Figure S2 from Assessing the Value of Incorporating a Polygenic Risk Score with Nongenetic Factors for Predicting Breast Cancer Diagnosis in the UK Biobank

Collister JA. et al, (2024)

Supplementary Figure S2 from Assessing the Value of Incorporating a Polygenic Risk Score with Nongenetic Factors for Predicting Breast Cancer Diagnosis in the UK Biobank

Collister JA. et al, (2024)

Supplementary Figure S3 from Assessing the Value of Incorporating a Polygenic Risk Score with Nongenetic Factors for Predicting Breast Cancer Diagnosis in the UK Biobank

Collister JA. et al, (2024)

Supplementary Figure S3 from Assessing the Value of Incorporating a Polygenic Risk Score with Nongenetic Factors for Predicting Breast Cancer Diagnosis in the UK Biobank

Collister JA. et al, (2024)

Supplementary Figure S4 from Assessing the Value of Incorporating a Polygenic Risk Score with Nongenetic Factors for Predicting Breast Cancer Diagnosis in the UK Biobank

Collister JA. et al, (2024)

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