KINDLE: Knowledge-Guided Distillation for Prior-Free Gene Regulatory Network Inference

TL;DR

KINDLE is a novel framework that uses knowledge-guided distillation to improve gene regulatory network inference, achieving state-of-the-art results without relying on prior knowledge and enabling discovery of new biological insights.

Contribution

KINDLE introduces a three-stage knowledge distillation approach that decouples GRN inference from prior knowledge dependence, enhancing accuracy and discovery potential.

Findings

State-of-the-art performance on four benchmark datasets

Successfully identified key transcription factors in mouse development

Accurately predicted cell fate transitions after gene knockouts

Abstract

Gene regulatory network (GRN) inference serves as a cornerstone for deciphering cellular decision-making processes. Early approaches rely exclusively on gene expression data, thus their predictive power remain fundamentally constrained by the vast combinatorial space of potential gene-gene interactions. Subsequent methods integrate prior knowledge to mitigate this challenge by restricting the solution space to biologically plausible interactions. However, we argue that the effectiveness of these approaches is contingent upon the precision of prior information and the reduction in the search space will circumscribe the models' potential for novel biological discoveries. To address these limitations, we introduce KINDLE, a three-stage framework that decouples GRN inference from prior knowledge dependencies. KINDLE trains a teacher model that integrates prior knowledge with temporal gene expression dynamics and subsequently distills this encoded knowledge to a student model, enabling accurate GRN inference solely from expression data without access to any prior. KINDLE achieves state-of-the-art performance across four benchmark datasets. Notably, it successfully identifies key transcription factors governing mouse embryonic development and precisely characterizes their functional roles. In mouse hematopoietic stem cell data, KINDLE accurately predicts fate transition outcomes following knockout of two critical regulators (Gata1 and Spi1). These biological validations demonstrate our framework's dual capability in maintaining topological inference precision while preserving discovery potential for novel biological mechanisms.