用图学习和解释方法，从实验数据中精准挖掘生物通路。
ExPath: Targeted Pathway Inference for Biological Knowledge Bases via Graph Learning and Explanation
- 通过图学习融合实验数据，自动识别关键生物通路。
- 相比基线方法，路径保真度提升4.5倍，冗余路径减少14倍。
- 适合生物信息学研究者与药物靶点发现场景。
在整合湿实验数据的情况下，从生物知识库中检索目标通路仍具挑战性，通常需要下游分析和专业技能。本文将该问题建模为可解的图学习与解释任务，提出一种新框架ExPath，显式融合实验数据以分类生物数据库中的各类图（生物网络）。对分类贡献更大的边（代表通路）可视为目标通路。该框架可无缝集成生物基础模型来编码实验分子数据。我们设计了面向机器学习的生物评估方法和新指标。在301个生物网络上的实验表明，ExPath推断出的通路具有生物学意义，其Fidelity+（必要性）最高提升4.5倍，Fidelity-（充分性）降低14倍，同时保持信号链长度达基线4倍。
原文摘要 · Abstract (English)
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.
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