ArXiv Preprint
The integration of multi-modal data, such as pathological images and genomic
data, is essential for understanding cancer heterogeneity and complexity for
personalized treatments, as well as for enhancing survival predictions. Despite
the progress made in integrating pathology and genomic data, most existing
methods cannot mine the complex inter-modality relations thoroughly.
Additionally, identifying explainable features from these models that govern
preclinical discovery and clinical prediction is crucial for cancer diagnosis,
prognosis, and therapeutic response studies. We propose PONET- a novel
biological pathway-informed pathology-genomic deep model that integrates
pathological images and genomic data not only to improve survival prediction
but also to identify genes and pathways that cause different survival rates in
patients. Empirical results on six of The Cancer Genome Atlas (TCGA) datasets
show that our proposed method achieves superior predictive performance and
reveals meaningful biological interpretations. The proposed method establishes
insight into how to train biologically informed deep networks on multimodal
biomedical data which will have general applicability for understanding
diseases and predicting response and resistance to treatment.
Lin Qiu, Aminollah Khormali, Kai Liu
2023-01-06
