Bibliographic Details
| Title: |
Multivariate Bayesian structured variable selection for pharmacogenomic studies. |
| Authors: |
Zhao, Zhi1,2 (AUTHOR) zhi.zhao@medisin.uio.no, Banterle, Marco3 (AUTHOR), Lewin, Alex3 (AUTHOR), Zucknick, Manuela1 (AUTHOR) |
| Source: |
Journal of the Royal Statistical Society: Series C (Applied Statistics). Mar2024, Vol. 73 Issue 2, p420-443. 24p. |
| Subject Terms: |
Markov random fields, Pharmacogenomics, Antineoplastic agents, Multiomics, Cancer cells, Medical screening |
| Abstract: |
Cancer drug sensitivity screens combined with multi-omics characterisation of the cancer cells have become an important tool to determine the optimal treatment for each patient. We propose a multivariate Bayesian structured variable selection model for sparse identification of multi-omics features associated with multiple correlated drug responses. Our model uses known structure between drugs and their targeted genes via a Markov random field (MRF) prior in sparse seemingly unrelated regression. The use of MRF prior can improve the model performance compared to other common priors. The proposed model is applied to the Genomics of Drug Sensitivity in Cancer data. [ABSTRACT FROM AUTHOR] |
|
Copyright of Journal of the Royal Statistical Society: Series C (Applied Statistics) is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| Database: |
Business Source Complete |
|
Full text is not displayed to guests. |
Login for full access.
|
