M
Mingon Kang
Researcher at University of Nevada, Las Vegas
Publications - 68
Citations - 642
Mingon Kang is an academic researcher from University of Nevada, Las Vegas. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 9, co-authored 57 publications receiving 317 citations. Previous affiliations of Mingon Kang include Texas A&M University–Commerce & University of Illinois at Chicago.
Papers
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Journal ArticleDOI
PASNet: pathway-associated sparse deep neural network for prognosis prediction from high-throughput data.
TL;DR: PASNet is the first pathway-based deep neural network that represents hierarchical representations of genes and pathways and their nonlinear effects, to the best of the authors' knowledge and would be promising due to its flexible model representation and interpretability, embodying the strengths of deep learning.
Journal ArticleDOI
A roadmap for multi-omics data integration using deep learning.
TL;DR: In this paper, the authors outline a roadmap of multi-omics integration using DL and offer a practical perspective into the advantages, challenges and barriers to the implementation of DL in multomics data.
Proceedings ArticleDOI
PAGE-Net: Interpretable and Integrative Deep Learning for Survival Analysis Using Histopathological Images and Genomic Data.
TL;DR: A biologically interpretable deep learning model (PAGE-Net) that integrates histopathological images and genomic data, not only to improve survival prediction, but also to identify genetic and Histopathological patterns that cause different survival rates in patients.
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Deep-Hipo: Multi-scale receptive field deep learning for histopathological image analysis.
TL;DR: A novel multi-task based deep learning model for HIstoPatholOgy (named Deep-Hipo) that takes multi-scale patches simultaneously for accurate histopathological image analysis that has outperformed the current state-of-the-art deep learning methods.
Journal ArticleDOI
Interpretable Deep Neural Network for Cancer Survival Analysis by Integrating Genomic and Clinical Data
TL;DR: A novel biologically interpretable pathway-based sparse deep neural network, named Cox-PASNet, which integrates high-dimensional gene expression data and clinical data on a simple neural network architecture for survival analysis, and shows out-performance, compared to the benchmarking methods.