Data augmentation and multimodal learning for predicting drug response in patient-derived xenografts from gene expressions and histology images
Patient-derived xenografts (PDXs) are an appealing platform for preclinical drug studies. A primary challenge in modeling drug response prediction (DRP) with PDXs and neural networks (NNs) is the limited number of drug response samples. We investigate multimodal neural network (MM-Net) and data augm...
Main Authors: | Alexander Partin, Thomas Brettin, Yitan Zhu, James M. Dolezal, Sara Kochanny, Alexander T. Pearson, Maulik Shukla, Yvonne A. Evrard, James H. Doroshow, Rick L. Stevens |
---|---|
Format: | Article |
Language: | English |
Published: |
Frontiers Media S.A.
2023-03-01
|
Series: | Frontiers in Medicine |
Subjects: | |
Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2023.1058919/full |
Similar Items
-
Enhanced Co-Expression Extrapolation (COXEN) Gene Selection Method for Building Anti-Cancer Drug Response Prediction Models
by: Yitan Zhu, et al.
Published: (2020-09-01) -
Integration of Computational Docking into Anti-Cancer Drug Response Prediction Models
by: Oleksandr Narykov, et al.
Published: (2023-12-01) -
A Comprehensive Investigation of Active Learning Strategies for Conducting Anti-Cancer Drug Screening
by: Priyanka Vasanthakumari, et al.
Published: (2024-01-01) -
Deep learning methods for drug response prediction in cancer: Predominant and emerging trends
by: Alexander Partin, et al.
Published: (2023-02-01) -
Patient-Derived Xenograft Models of Pancreatic Cancer: Overview and Comparison with Other Types of Models
by: Patrick L. Garcia, et al.
Published: (2020-05-01)