Identification of gene regulatory networks by strategic gene disruptions and gene overexpressions
Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms
Identifying gene regulatory networks from experimental data
Parallel Computing - new trends in high performance computing
Non-approximability results for optimization problems on bounded degree instances
STOC '01 Proceedings of the thirty-third annual ACM symposium on Theory of computing
Some optimal inapproximability results
Journal of the ACM (JACM)
Clustering causal relationships in genes expression data
WIRN'05 Proceedings of the 16th Italian conference on Neural Nets
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One of the combinatorial models for the biological problem of inferring gene regulation networks is the Maximum Gene Regulatory Network Problem, shortly MGRN, proposed in [2]. The problem is NP-hard [2], consequently the attention has shifted towards approximation algorithms, leading to a polynomial-time 1/2-approximation algorithm [2], while no upper bound on the possible approximation ratio was previously known. In this paper we make a first step towards closing the gap between the best known and the best possible approximation factors, by showing that no polynomial-time approximation algorithm can have a factor better than 1 – (1/8) / (1+e2) unless RP=NP.