ExPath: Targeted Pathway Inference for Biological Knowledge Bases via Graph Learning and Explanation
This addresses the problem of pathway inference for biologists by providing a more accurate and interpretable method, though it is incremental as it builds on existing graph learning and explanation techniques.
The paper tackles the challenge of retrieving targeted pathways in biological knowledge bases by proposing ExPath, a graph learning and explanation framework that integrates experimental data to classify bio-networks, achieving up to 4.5x higher Fidelity+ and 14x lower Fidelity- than baselines while preserving signaling chains up to 4x longer.
Retrieving targeted pathways in biological knowledge bases, particularly when incorporating wet-lab experimental data, remains a challenging task and often requires downstream analyses and specialized expertise. In this paper, we frame this challenge as a solvable graph learning and explaining task and propose a novel subgraph inference framework, ExPAth, that explicitly integrates experimental data to classify various graphs (bio-networks) in biological databases. The links (representing pathways) that contribute more to classification can be considered as targeted pathways. Our framework can seamlessly integrate biological foundation models to encode the experimental molecular data. We propose ML-oriented biological evaluations and a new metric. The experiments involving 301 bio-networks evaluations demonstrate that pathways inferred by ExPath are biologically meaningful, achieving up to 4.5x higher Fidelity+ (necessity) and 14x lower Fidelity- (sufficiency) than explainer baselines, while preserving signaling chains up to 4x longer.