Matches in Nanopublications for { ?s ?p <https://doi.org/10.1093/bioadv/vbae208> ?g. }
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- Quantum%20approximate%20optimization%20algorithms%20outperform%20classical%20methods%20for%20disjoint%20network%20motif%20identification%20in%20biological%20networks. obtainsSupportFrom vbae208 assertion.
- The%20QOMIC%20algorithm%20achieves%20up%20to%2063.6%25%20F1%20score%20improvement%20over%20classical%20methods%20when%20identifying%20network%20motifs%20in%20gene%20regulatory%20networks. obtainsSupportFrom vbae208 assertion.
- Quantum%20annealing%20provides%20computational%20advantages%20for%20NP-hard%20network%20motif%20identification%20problems%20in%20systems%20biology. obtainsSupportFrom vbae208 assertion.
- Network%20motif%20analysis%20reveals%20conserved%20regulatory%20patterns%20in%20disease-associated%20gene%20networks%20through%20quantum%20optimization. obtainsSupportFrom vbae208 assertion.
- qomic-quantum-optimization-for-motif-identification supports vbae208 assertion.
- study source vbae208 assertion.
- study source vbae208 assertion.
- 0000-0002-1784-2920 quotes vbae208 assertion.
- 0000-0002-1784-2920 quotes vbae208 assertion.
- 0000-0002-1784-2920 quotes vbae208 assertion.