发表机构
Graduate School of Media and Governance, Keio University; Human Biology-Microbiome-Quantum Research Center (WPI-Bio2Q), Keio University; Keio University Sustainable Quantum Artificial Intelligence Center (KSQAIC), Keio University（庆应义塾大学媒体治理研究生院; 庆应义塾大学生物学-微生物组-量子研究中心 (WPI-Bio2Q); 庆应义塾大学可持续量子人工智能中心 (KSQAIC)）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究评估了QUBO递归Ncut系统发育重建中不同图构建表示的影响，发现亲和力表示显著影响分裂恢复，且图构建是决定重建准确性的关键因素。
AI 中文摘要
分子序列数据被用于重建分类群之间的进化关系，但重建的准确性不仅取决于建树方法，还取决于成对序列关系的表示方式。我们在一个递归归一化割（Ncut）框架中评估了序列到亲和力表示，该框架的图划分子问题被表述为二次无约束二元优化（QUBO）模型，并使用模拟分岔（Simulated Bifurcation）求解。利用涵盖多种树生成设置和进化分歧度的模拟氨基酸和核苷酸数据集，我们将归一化位得分亲和力与由变换后的序列相似性和进化距离导出的表示进行比较，检查了交换后细化，并使用邻接法（NJ）作为基于距离的比较器。亲和力表示显著影响内部分裂恢复，尤其是对核苷酸数据。基于JC69的局部亲和力在分歧度增加时保持了相对较高的准确性，而归一化位得分和基于BLAST的核表示下降更为明显。交换后细化通常改善了恢复效果，但在个别重建中并不一致。在所有评估条件下，NJ在WAG和JC69距离上实现了比相应递归Ncut重建更高的平均分裂恢复，而在某些条件下，递归Ncut在基于BLAST的对数距离上优于NJ。这些结果表明，图构建是递归Ncut系统发育重建的重要决定因素。在Ncut内表现良好的表示不一定能最准确地利用底层成对距离。因此，应联合评估成对表示、亲和力变换、优化和递归树构建。
英文摘要
Molecular sequence data are used to reconstruct evolutionary relationships among taxa, but reconstruction accuracy depends not only on the tree-building method but also on how pairwise sequence relationships are represented. We evaluated sequence-to-affinity representations in a recursive normalized-cut (Ncut) framework whose graph-partitioning subproblems were formulated as quadratic unconstrained binary optimization (QUBO) models and solved using Simulated Bifurcation. Using simulated amino-acid and nucleotide datasets spanning multiple tree-generation settings and evolutionary divergence, we compared normalized bit-score affinities with representations derived from transformed sequence similarities and evolutionary distances, examined post-swap refinement, and used neighbor joining (NJ) as a distance-based comparator. Affinity representation substantially affected internal split recovery, particularly for nucleotide data. JC69-based local affinities maintained comparatively high accuracy as divergence increased, whereas normalized bit-score and BLAST-derived kernel representations declined more markedly. Post-swap refinement generally improved recovery, but not consistently across individual reconstructions. NJ achieved higher mean split recovery than corresponding recursive Ncut reconstructions for WAG and JC69 distances across all evaluated conditions, whereas recursive Ncut outperformed NJ for BLAST-derived logarithmic distances under some conditions. These results show that graph construction is an important determinant of recursive Ncut-based phylogenetic reconstruction. A representation that performs well within Ncut does not necessarily provide the most accurate use of the underlying pairwise distances. Pairwise representation, affinity transformation, optimization, and recursive tree construction should therefore be evaluated jointly.
Comments23 pages, 6 figures, plus a graphical abstract; revised manuscript; author list updated
