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Integer linear programming for phylogenetic tree inference in multi-state mixtures | ||
| Journal of Mathematical Modeling | ||
| مقالات آماده انتشار، اصلاح شده برای چاپ، انتشار آنلاین از تاریخ 23 تیر 1405 اصل مقاله (921.98 K) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22124/jmm.2026.33204.3025 | ||
| نویسندگان | ||
| Raziyeh Enayatsefat1؛ Mehri Bagherian* 1؛ Hamidreza Vaziri2 | ||
| 1Department of Applied Mathematics, Faculty of Mathematical Sciences, University of Guilan, Rasht, Iran | ||
| 2Department of Biology, Faculty of Sciences, University of Guilan, Rasht, Iran | ||
| چکیده | ||
| The evolutionary history of somatic mutations within cells, which plays a key role in the development of cancer, can be represented using a phylogenetic tree. Studying the evolution of mutations is crucial for advancing cancer treatment. In this study, we address the problem of reconstructing phylogenetic trees from mixed populations of cells, modeled as the cladistic multi-state Perfect Phylogeny Mixture Deconvolution Problem (PPMDP). Given the frequency measurements of character states across bulk samples, the objective is to infer a tree that satisfies Infinite-Alleles Assumption (IAA). To this aim, we propose a Binary Linear Programming (BLP) formulation. The method incorporates both Single-Nucleotide Variations (SNVs) and Copy Number Variations (CNVs) as input data. We evaluate the approach using simulated and real data, and demonstrate that the BLP model reconstructs phylogenetic trees efficiently and accurately, showing high coincidence with the ground truth trees. | ||
| کلیدواژهها | ||
| Binary linear programming؛ multi-state perfect phylogeny؛ phylogenetic tree reconstruction؛ optimization؛ combinatorial algorithms | ||
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آمار تعداد مشاهده مقاله: 8 تعداد دریافت فایل اصل مقاله: 4 |
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