A gene importance ranking method splits large MILP problems for metabolic network design into parallel subproblems, recovering most original solutions while raising success rates 37-186% under fixed time limits.
Predicting metabolic engineering knockout strategies for chemical production: accounting for competing pathways.Bioinfor- matics, 26(4):536–543
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A Gene Ranking Framework Enhances the Design Efficiency of Genome-Scale Constraint-Based Metabolic Networks under Time Limits
A gene importance ranking method splits large MILP problems for metabolic network design into parallel subproblems, recovering most original solutions while raising success rates 37-186% under fixed time limits.