Kolmogorov-Arnold Network for Gene Regulatory Network Inference

TL;DR

This paper introduces scKAN, a novel Kolmogorov-Arnold network model that infers gene regulatory networks from single-cell RNA sequencing data, accurately distinguishing regulation types and capturing cellular dynamics with explainable AI.

Contribution

The paper presents scKAN, a new model employing differentiable functions and geometric tools to improve gene regulatory network inference from scRNA-seq data, surpassing existing models in accuracy.

Findings

scKAN outperforms existing models in AUROC and AUPRC on the BEELINE benchmark.

It accurately detects activation and inhibition regulations.

The model captures cellular dynamics without prior graph structure knowledge.

Abstract

Gene regulation is central to understanding cellular processes and development, potentially leading to the discovery of new treatments for diseases and personalized medicine. Inferring gene regulatory networks (GRNs) from single-cell RNA sequencing (scRNA-seq) data presents significant challenges due to its high dimensionality and complexity. Existing tree-based models, such as GENIE3 and GRNBOOST2, demonstrated scalability and explainability in GRN inference, but they cannot distinguish regulation types nor effectively capture continuous cellular dynamics. In this paper, we introduce scKAN, a novel model that employs a Kolmogorov-Arnold network (KAN) with explainable AI to infer GRNs from scRNA-seq data. By modeling gene expression as differentiable functions matching the smooth nature of cellular dynamics, scKAN can accurately and precisely detect activation and inhibition regulations through explainable AI and geometric tools. We conducted extensive experiments on the BEELINE benchmark, and scKAN surpasses and improves the leading signed GRN inference models ranging from 5.40\% to 28.37\% in AUROC and from 1.97\% to 40.45\% in AUPRC. These results highlight the potential of scKAN in capturing the underlying biological processes in gene regulation without prior knowledge of the graph structure.