MAGE: an efficient algorithm for identifying Maximal Age-indexed GEne sets.
5 affiliations
- 1Electronics Technology Programme, Electronics and Automation Department, Technical Sciences Vocational School, Bursa Uludağ University, Görükle Campus, Görükle Mahallesi, Üniversite 1. Cadde, No. 711G, Nilüfer, Bursa 16059, Türkiye.
- 2Department of Computer Engineering, Bursa Technical University, Bursa 16310, Türkiye.
- 3Department of Computer Science, University of Florida, Gainesville, FL 32611, United States.
- 4Department of Medical and Surgical Sciences, University of Catanzaro, Viale Europa, I-88100 Catanzaro, Italy.
- 5Department of Computer Engineering, Modelling, Electronic and Systems, University of Calabria, Via P Bucci, I-87036 Rende (CS), Italy.
Abstract
Aging alters transcriptional regulation in a tissue- and time-specific manner, reshaping gene regulatory networks and shifting regulatory influence dynamics. Traditional in silico influence maximization methods often rely on static gene expression snapshots and fail to capture gene-to-gene influence variations over time. To address this, we introduce the Age Index, a network-based metric that quantifies time-varying gene influence. We define the Maximal and Minimal Age Index Problems (MAP\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} +\end{document} and MAP\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} −\end{document}) as the tasks of identifying gene sets with the highest and lowest Age Index, and prove these problems are NP-hard. To solve them, we develop MAGE (Maximal Age-Index Algorithm), a stochastic method that iteratively constructs gene sets by prioritizing genes likely to optimize the Age Index. MAGE identifies statistically significant gene sets at the genome scale, with strong literature support, highlighting its potential to uncover age-sensitive regulatory mechanisms.
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BibTeX
@article{calıs2026mage,
title = {{MAGE: an efficient algorithm for identifying Maximal Age-indexed GEne sets.}},
author = {Murat Çalış and Volkan Altuntas and Omer Kahveci and Pietro Hiram Guzzi and Pierangelo Veltri and Tamer Kahveci},
journal = {Briefings in bioinformatics},
year = {2026},
month = nov,
doi = {10.1093/bib/bbag613},
url = {https://europepmc.org/article/MED/42969857},
note = {CC BY},
}
RIS
TY - JOUR
TI - MAGE: an efficient algorithm for identifying Maximal Age-indexed GEne sets.
AU - Murat Çalış
AU - Volkan Altuntas
AU - Omer Kahveci
AU - Pietro Hiram Guzzi
AU - Pierangelo Veltri
AU - Tamer Kahveci
PY - 2026
DA - 2026/11/01
JO - Briefings in bioinformatics
DO - 10.1093/bib/bbag613
UR - https://europepmc.org/article/MED/42969857
AB - Aging alters transcriptional regulation in a tissue- and time-specific manner, reshaping gene regulatory networks and shifting regulatory influence dynamics. Traditional in silico influence maximization methods often rely on static gene expression snapshots and fail to capture gene-to-gene influence variations over time. To address this, we introduce the Age Index, a network-based metric that quantifies time-varying gene influence. We define the Maximal and Minimal Age Index Problems (MAP\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} ^+\end{document} and MAP\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} ^−\end{document}) as the tasks of identifying gene sets with the highest and lowest Age Index, and prove these problems are NP-hard. To solve them, we develop MAGE (Maximal Age-Index Algorithm), a stochastic method that iteratively constructs gene sets by prioritizing genes likely to optimize the Age Index. MAGE identifies statistically significant gene sets at the genome scale, with strong literature support, highlighting its potential to uncover age-sensitive regulatory mechanisms.
SN - 1467-5463
SN - 1477-4054
ER -