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Efficient Ad-Hoc Graph Inference and Matching in Biological Databases

Summary: Formalizes ad-hoc online inference and subgraph matching of gene regulatory networks from feature databases without full materialization, via probabilistic graphs. Introduces a probabilistic edge-score and, with reduction, pruning, embedding, and index-guided query processing, to efficiently answer IM-GRN queries. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
5357
Venue
SIGMOD
Year
2017
Pagerank
5.093636e-05
Overall Rank
11,974 | 17.85%
DOI
10.1145/3035918.3035929

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BibTeX Citation

@inproceedings{lian_sigmod17,
        title = {{Efficient Ad-Hoc Graph Inference and Matching in Biological Databases}},
        author = {Lian, Xiang and Kim, Dongchul},
        series = {{SIGMOD} '17},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3035918.3035929},
        url = {https://dl.acm.org/doi/10.1145/3035918.3035929},
        year = {2017}
}

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