Predicting Biomedical Interactions with Probabilistic Model Selection for Graph Neural Networks

TL;DR

This paper introduces a Bayesian model selection framework for graph neural networks to automatically determine optimal depth, improving prediction accuracy and calibration in biomedical interaction networks.

Contribution

It presents a novel Bayesian approach that jointly infers GNN depth, applies dropout regularization, and learns parameters, enhancing performance and calibration in biomedical data analysis.

Findings

Outperforms existing methods on four biomedical datasets.

Achieves well-calibrated predictions with adaptive GNN depth.

Demonstrates robustness across various biomedical network types.

Abstract

Heterogeneous molecular entities and their interactions, commonly depicted as a network, are crucial for advancing our systems-level understanding of biology. With recent advancements in high-throughput data generation and a significant improvement in computational power, graph neural networks (GNNs) have demonstrated their effectiveness in predicting biomedical interactions. Since GNNs follow a neighborhood aggregation scheme, the number of graph convolution (GC) layers (i.e., depth) determines the neighborhood orders from which they can aggregate information, thereby significantly impacting the model's performance. However, it often relies on heuristics or extensive experimentation to determine an appropriate GNN depth for a given biomedical network. These methods can be unreliable or result in expensive computational overhead. Moreover, GNNs with more GC layers tend to exhibit poor calibration, leading to high confidence in incorrect predictions. To address these challenges, we propose a Bayesian model selection framework to jointly infer the most plausible number of GC layers supported by the data, apply dropout regularization, and learn network parameters. Experiments on four biomedical interaction datasets demonstrate that our method achieves superior performance over competing methods, providing well-calibrated predictions by allowing GNNs to adapt their depths to accommodate interaction information from various biomedical networks. Source code and data is available at: https://github.com/kckishan/BBGCN-LP/tree/master