Probabilistic Modeling of Imaging, Genetics and Diagnosis
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Probabilistic modeling.pdf
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Author(s)
Quon, Gerald
Sabuncu, Mert
Batmanghelich, Nematollah
Dalca, Adrian Vasile
Golland, Polina
Date Issued
February 2016
Journal
IEEE Transactions on Medical Imaging
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Batmanghelich, Nematollah K., et al. “Probabilistic Modeling of Imaging, Genetics and Diagnosis.” IEEE Transactions on Medical Imaging, vol. 35, no. 7, July 2016, pp. 1765–79.
Version
Author's final manuscript
Abstract
We propose a unified Bayesian framework for detecting genetic variants associated with disease by exploiting image-based features as an intermediate phenotype. The use of imaging data for examining genetic associations promises new directions of analysis, but currently the most widely used methods make sub-optimal use of the richness that these data types can offer. Currently, image features are most commonly selected based on their relevance to the disease phenotype. Then, in a separate step, a set of genetic variants is identified to explain the selected features. In contrast, our method performs these tasks simultaneously in order to jointly exploit information in both data types. The analysis yields probabilistic measures of clinical relevance for both imaging and genetic markers. We derive an efficient approximate inference algorithm that handles the high dimensionality of image and genetic data. We evaluate the algorithm on synthetic data and demonstrate that it outperforms traditional models. We also illustrate our method on Alzheimer's Disease Neuroimaging Initiative data.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1109/tmi.2016.2527784
