PATHWAY-CAPSULE NEURAL NETWORKS

  • Phuong-Nam Nguyen

Abstract

The progress made in Next-Generation Sequencing technology has resulted in many genetic databases,
which offer the potential for more effective diagnostic tools. Recent literature highlights Artificial
Intelligence (AI) as one of the most potent methods. Nonetheless, when it comes to analyzing
genetic data, Machine Learning (ML) and Deep Learning (DL) face three significant challenges
due to: (1) multi-modality analysis, (2) model explainability, and (3) extension to patient-specific
biomarker identification. In this paper, we introduce Pathway-Capsule Neural Networks (PCNN)
and its Bayesian version (BayesPCNN) to address the three limitations. We demonstrate the model
using Big Data of 7, 372 samples with 57 protein expressions from the TCGA dataset. In the best
setting, the proposed PCNN outperforms tree-based ML models and KNN with a significant gap
of 10% - 30% in accuracy for Pan-Cancer detection; while only outperformed by kernel methods
with under 2%. Another contribution of BayesPCNN is highlighted as Explainable AI (XAI), which
reveals tumors with potential metastasis and patient-specific biomarkers.

Author Biography

Phuong-Nam Nguyen

Phuong-Nam Nguyen
Faculty of Computer Science
PHENIKAA University, Yen Nghia, Ha Dong
Hanoi, Vietnam 12116
nam.nguyenphuong@phenikaa-uni.edu.vn

Published
2024-05-27