Bayesian networks (BN) are probabilistic graphical models which are widely utilized in modeling complex biological interactions in the cell. Learning the structure of a BN is an NP-hard problem and existing exact and heuristic solutions do not scale to large enough domains to allow for meaningful modeling of many biological processes. In this work, we present efficient parallel algorithms which push the scale of both exact and heuristic BN structure learning. We demonstrate the applicability of our methods by implementations on an IBM Blue Gene/L and an AMD Opteron cluster, and discuss their significance for future applications to systems biology.
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机译：贝叶斯网络（BN）是概率图形模型，广泛用于对细胞中复杂的生物相互作用进行建模。学习BN的结构是一个NP难题，现有的精确和启发式解决方案无法扩展到足够大的域，无法对许多生物过程进行有意义的建模。在这项工作中，我们提出了有效的并行算法，可推动精确和启发式BN结构学习的规模。我们通过在IBM Blue Gene / L和AMD Opteron集群上的实现来证明我们的方法的适用性，并讨论它们对系统生物学的未来应用的重要性。
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