MKAN-MMI: empowering traditional medicine-microbe interaction prediction with masked graph autoencoders and KANs

Abstract

The growing microbial resistance to traditional medicines necessitates in-depth analysis of medicine-microbe interactions (MMIs) to develop new therapeutic strategies. Widely used artificial intelligence models are limited by sparse observational data and prevalent noise, leading to over-reliance on specific data for feature extraction and reduced generalization ability. To address these limitations, we integrate Kolmogorov-Arnold Networks (KANs), independent subspaces, and collaborative decoding techniques into the masked graph autoencoder (Mask GAE) framework, creating an innovative MMI prediction model with enhanced accuracy, generalization, and interpretability. First, we apply Bernoulli distribution to randomly mask parts of the medicine-microbe graph, advancing self-supervised training and reducing noise impact. Additionally, the independent subspace technique enables graph neural networks (GNNs) to learn weights independently across different feature subspaces, enhancing feature expression. Fusing the multi-layer outputs of GNNs effectively reduces information loss caused by masking. Moreover, using KANs for advanced nonlinear mapping enhances the learnability and interpretability of weights, deepening the understanding of complex MMIs. These measures significantly enhanced the accuracy, generalization, and interpretability of our model in MMI prediction tasks. We validated our model on three public datasets with results showing that our model outperformed existing leading models. The relevant data and code are publicly accessible at: https://github.com/zhuoninnin1992/MKAN-MMI.

Keywords: artificial intelligence models; kolmogorov-arnold networks (KANs); masked graph autoencoder (mask GAE); medicine-microbe interactions (MMIs); traditional medicine (TM).

FIGURE 1

The MKAN-MMI model’s architecture comprises: (A) constructing and masking the medicine-microbe graph, (B) extracting microbe and medicine representations using independent subspace technology, (C) reconstructing the masked graph with collaborative decoding, and (D) employing KAN technology.

FIGURE 2

Results of MKAN-MMI model using different node sampling rates.

FIGURE 3

Results of MKAN-MMI model using different walk lengths.

FIGURE 4

Results of MKAN-MMI model using different GNN encoders.

FIGURE 5

Results of MKAN-MMI model using different subsapce numbers.

FIGURE 6

AUC-based statistical significance analysis on aBiofilm dataset.

FIGURE 7

AUC-based statistical significance analysis on DrugVirus dataset.

FIGURE 8

AUC-based statistical significance analysis on MDAD dataset.