通过相位加权图模型，从无节律基因中挖掘潜在的昼夜节律调控因子。
Recovering Candidate Circadian Regulators of Arrhythmic Pituitary Hormone Genes Using Reliability-Weighted Magnetic Laplacian with rwMagLap
- 构建基于周期性基因的图模型，用复数相位表示时间顺序。
- 在垂体数据中，候选基因富集显著（7.95倍）且排序准确率达0.971。
- 适合研究女性健康相关基因调控及节律药物设计者阅读。
我们研究如何从不表现出明显24小时节律的垂体激素基因中，恢复其潜在的昼夜节律调控因子，以支持未来针对女性健康的节律药理与节律治疗策略。提出rwMagLap方法：基于周期性核心基因构建图，每条边结合24小时拟合质量与峰时相位（复平面单位圆表示），生成赫米特邻接矩阵与磁拉普拉斯算子。将无节律激素基因作为锚点，通过可靠性加权最近邻投影插入，并聚合为软跳跃分布，再以复数个性化PageRank按得分大小排序周期性核心基因。在垂体数据中，11个女性健康锚点均无节律，但前50名候选基因在KEGG昼夜节律通路中富集7.95倍（13基因集，8/8在454基因核心中；修正后BH p=4×10⁻⁶），Reactome通路富集4.54倍（111基因集，8/16；p=1.6×10⁻⁴），而无相位信息的基准方法未检出任何富集。结果表明，可靠性加权与相位感知种子机制优于磁传播。磁相位赋予了表征时间顺序的能力：在垂体核心基因中，磁嵌入对实际峰时顺序的恢复准确率达0.971，而无磁荷（q=0）时仅为随机水平。
原文摘要 · Abstract (English)
We study how to recover candidate circadian-clock regulators of pituitary hormone genes that are important for women's health but do not show a clear 24-hour rhythm in bulk tissue, aiming to nominate clock-linked regulatory targets that could inform future chronopharmacologic and chronotherapeutic strategies. We propose \textbf{rwMagLap}, which builds a graph on rhythmic backbone genes. For each edge, we combine 24-hour fit quality with peak-time phase, represented as a complex unit-circle value, yielding a Hermitian adjacency matrix and a magnetic Laplacian. We insert arrhythmic hormone genes, treated as anchors, by a reliability-weighted nearest-neighbor projection. The projected anchor-neighbor weights are pooled into a soft teleport distribution, and complex personalized PageRank then ranks rhythmic backbone genes by the magnitude of their PageRank scores. In pituitary data, we find that all 11 women's-health anchors are arrhythmic. Even so, we find that the top-50 list is $7.95\times$ enriched for the 13-gene KEGG circadian set (7 of the 8 set genes in the 454-gene backbone; corrected Benjamini-Hochberg (BH) $p_{\mathrm{BH}}=4\times10^{-6}$) and $4.54\times$ enriched for the 111-gene Reactome set (8 of 16 genes; $p_{\mathrm{BH}}=1.6\times10^{-4}$), while a phase-blind real-valued baseline recovers none. We recover candidates through reliability weighting and phase-aware seeding rather than through magnetic propagation. The magnetic phase adds a different capability: it represents temporal order. On pituitary backbone, the magnetic embedding recovers measured peak-time order of connected pituitary genes with accuracy $0.971$, while $q{=}0$, i.e., no magnetic charge, is at chance.
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