时间标签：数据日期 2026-11-05 · 生成时间 2026-11-05 05:30（Asia/Shanghai）｜ 检索窗口 PubMed edat 2026/11/02 – 2026/11/06 ｜ 情报发生日期 2026-11-05
研究问题
At each iteration, a gene is selected at random according to its likelihood of improving the objective function.；Similarly, network sparsity or density primarily affects the scale of influence values rather than their relative ordering across age groups, which underlies the optimization objective of MAGE.；Nonetheless, we acknowledge that extreme variations in GRN construction may impact quantitative results, and systematic evaluation across alternative network inference frameworks constitutes an important direction for future work.Experimental resultsWe tested MAGE under different parameter configurations and also aiming to demonstrate the importance of the Age Index in uncovering biologically meaningful results.We used a GRN obtained from the TRRUST database (version 2) [36], considering 800 human TFs, 8444 TF–target interactions, and 2862 unique genes.
方法
This underscores the importance of modeling the connections between aging, transcriptional deregulation, and systemic functional decline across tissues [10, 11, 14].The tissue-specific effects of aging have been extensively investigated using large-scale transcriptomic and single-cell RNA sequencing technologies.；In this article, we introduce a mathematical framework that explicitly links aging to transcriptional dynamics.；We propose an Age-Index formulation that captures the consistency of monotonic increases or decreases in gene-set influence with aging.
核心结论
Collectively, these molecular perturbations modify regulatory mechanisms governing the cellular life cycle, driving the multifactorial decline observed in aging organisms [10–13].；These studies demonstrate that gene expression profiles evolve with age, modulating a wide range of biological processes, cellular responses, and disease states [17].；We demonstrate that MAP is NP-hard and present a stochastic solution termed the Maximal Age-indexed GEne sets (MAGE) algorithm.
局限
However, a fundamental question remains unresolved: do transcriptional changes act as causal drivers of aging, or are they merely downstream consequences of the aging process [44]?；Aging, however, is inherently directional and progressive, suggesting that influence metrics should explicitly capture monotonic trends in gene activity.；However, the genes in the two bottom figures have little or no publication evidence.
对本项目的价值
中等价值：属「生物标志物 / 建模方法学」方向；方法学 / 生物标志物 / AI 建模栏目可用作本项目文献库与综述素材。 ｜ 价值分 2.6/5（★★★☆☆）
全文来源
PubMed PMC JATS（正文 115,756 字符，题名指纹 0.9，已过 PubMed PMCID 官方关联 + 题名指纹 + 正文长度归属校验）
原文题录
- 中文题名：MAGE：识别最大年龄索引基因集的高效算法
- 英文标题：MAGE: an efficient algorithm for identifying Maximal Age-indexed GEne sets.
- 期刊与标识：Briefings in bioinformatics · PMID 42969857 · DOI 10.1093/bib/bbag613 · PMC PMC13725925