Biologically Interpretable, Integrative Deep Learning for Cancer Survival Analysis

<p>Identifying complex biological processes associated to patients' survival time at the cellular and molecular level is critical not only for developing new treatments for patients but also for accurate survival prediction. However, highly nonlinear and high-dimension, low-sample size (HDLSS) data cause computational challenges in survival analysis. We developed a novel family of pathway-based, sparse deep neural networks (PASNet) for cancer survival analysis. PASNet family is a biologically interpretable neural network model where nodes in the network correspond to specific genes and pathways, while capturing nonlinear and hierarchical effects of biological pathways associated with certain clinical outcomes. Furthermore, integration of heterogeneous types of biological data from biospecimen holds promise of improving survival prediction and personalized therapies in cancer. Specifically, the integration of genomic data and histopathological images enhances survival predictions and personalized treatments in cancer study, while providing an in-depth understanding of genetic mechanisms and phenotypic patterns of cancer. Two proposed models will be introduced for integrating multi-omics data and pathological images, respectively. Each model in PASNet family was evaluated by comparing the performance of current cutting-edge models with The Cancer Genome Atlas (TCGA) cancer data. In the extensive experiments, PASNet family outperformed the benchmarking methods, and the outstanding performance was statistically assessed. More importantly, PASNet family showed the capability to interpret a multi-layered biological system. A number of biological literature in GBM supported the biological interpretation of the proposed models. The open-source software of PASNet family in PyTorch is publicly available at <a href="https://github.com/DataX-JieHao/" target="_blank">https://github.com/DataX-JieHao/</a></p>

Name thesis:degree_name

Doctor of Philosophy in Analytic and Data Science

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Statistics and Analytical Sciences

Year dc:date.available

2019

Author dc:creator

• Hao, Jie

Contributors dc:contributor

• Dr. Mingon Kang
• Dr. Donghyun (David) Kim
• Dr. Xuelei (Sherry) Ni
• Dr. Herman (Gene) Ray
• Dr. Jung Hun Oh

• Survival analysis; data integration; deep learning; gene expression; multi-omics; glioblastoma multiforme
• Bioinformatics

Repository record dc:identifier

https://digitalcommons.kennesaw.edu/dataphd_etd/3

OAI identifier oai:identifier

oai:digitalcommons.kennesaw.edu:dataphd_etd-1002