2016/05/07 by Alexander A. Loboda, Maxim N. Artyomov, Loboda, Alexander A. +3 · 1 citation
Biochemistry, Genetics and Molecular Biology · Computer Science · #Bioinformatics and Genomic Networks #Computational Drug Discovery Methods #Data Structures and Algorithms (cs.DS) #FOS: Computer and information sciences #Microbial Metabolic Engineering and Bioproduction #Plant biochemistry and biosynthesis
paper · pdf · doi:10.48550/arxiv.1605.02168
openalex publication_date 2016/05/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Network enrichment analysis methods allow to identify active modules without\nbeing biased towards a priori defined pathways. One of mathematical\nformulations of such analysis is a reduction to a maximum-weight connected\nsubgraph problem. In particular, in analysis of metabolic networks a\ngeneralized maximum-weight connected subgraph (GMWCS) problem, where both nodes\nand edges are scored, naturally arises. Here we present the first to our\nknowledge practical exact GMWCS solver. We have tested it on real-world\ninstances and compared to similar solvers. First, the results show that on\nnode-weighted instances GMWCS solver has a similar performance to the best\nsolver for that problem. Second, GMWCS solver is faster compared to the closest\nanalogue when run on GMWCS instances with edge weights.\n