ExPath: Targeted Pathway Inference for Biological Knowledge Bases via Graph Learning and Explanation
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908958491410432 |
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| author | Kotoge, Rikuto Yang, Ziwei Chen, Zheng Dong, Yushun Matsubara, Yasuko Sun, Jimeng Sakurai, Yasushi |
| author_facet | Kotoge, Rikuto Yang, Ziwei Chen, Zheng Dong, Yushun Matsubara, Yasuko Sun, Jimeng Sakurai, Yasushi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_18026 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ExPath: Targeted Pathway Inference for Biological Knowledge Bases via Graph Learning and Explanation Kotoge, Rikuto Yang, Ziwei Chen, Zheng Dong, Yushun Matsubara, Yasuko Sun, Jimeng Sakurai, Yasushi Machine Learning Artificial Intelligence 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. |
| title | ExPath: Targeted Pathway Inference for Biological Knowledge Bases via Graph Learning and Explanation |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2502.18026 |