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.
An in-silico approach to predict and exploit synthetic lethality in cancer metabolism.Nature communications, 8(1):459
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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.