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Articles by Ulzee An
Accurate prediction of disease-risk factors from volumetric medical scans by a deep vision model pre-trained with 2D scans - Nature Biomedical Engineering
Abstract The application of machine learning to tasks involving volumetric biomedical imaging is constrained by the limited availability of annotated datasets of three-dimensional (3D) scans for model training. Here we report a deep-learning model pre-trained on 2D scans (for which annotated data are relatively abundant) that accurately predicts disease-risk factors from 3D medical-scan modalities.
Deep learning-based phenotype imputation on population-scale biobank data increases genetic discoveries - Nature Genetics
Abstract Biobanks that collect deep phenotypic and genomic data across many individuals have emerged as a key resource in human genetics. However, phenotypes in biobanks are often missing across many individuals, limiting their utility. We propose AutoComplete, a deep learning-based imputation method to impute or ‘fill-in’ missing phenotypes in population-scale biobank datasets.
Phenotype integration improves power and preserves specificity in biobank-based genetic studies of major depressive disorder - Nature Genetics
Abstract Biobanks often contain several phenotypes relevant to diseases such as major depressive disorder (MDD), with partly distinct genetic architectures. Researchers face complex tradeoffs between shallow (large sample size, low specificity/sensitivity) and deep (small sample size, high specificity/sensitivity) phenotypes, and the optimal choices are often unclear. Here we propose to integrate these phenotypes to combine the benefits of each.
Phenotype integration improves power and preserves specificity in biobank-based genetic studies of MDD
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