Active learning of digenic functions with boolean matrix logic programming

Lun Ai, Stephen H. Muggleton, Shi-shun Liang, Geoff S. Baldwin
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Abstract

We apply logic-based machine learning techniques to facilitate cellular engineering and drive biological discovery, based on comprehensive databases of metabolic processes called genome-scale metabolic network models (GEMs). Predicted host behaviours are not always correctly described by GEMs. Learning the intricate genetic interactions within GEMs presents computational and empirical challenges. To address these, we describe a novel approach called Boolean Matrix Logic Programming (BMLP) by leveraging boolean matrices to evaluate large logic programs. We introduce a new system, $BMLP_{active}$, which efficiently explores the genomic hypothesis space by guiding informative experimentation through active learning. In contrast to sub-symbolic methods, $BMLP_{active}$ encodes a state-of-the-art GEM of a widely accepted bacterial host in an interpretable and logical representation using datalog logic programs. Notably, $BMLP_{active}$ can successfully learn the interaction between a gene pair with fewer training examples than random experimentation, overcoming the increase in experimental design space. $BMLP_{active}$ enables rapid optimisation of metabolic models and offers a realistic approach to a self-driving lab for microbial engineering.
利用布尔矩阵逻辑编程主动学习数字函数
我们基于称为基因组规模代谢网络模型(GEMs)的代谢过程综合数据库,应用基于逻辑的机器学习技术来促进细胞工程和推动生物发现。学习 GEMs 中错综复杂的基因相互作用给计算和实证带来了挑战。为了解决这些问题,我们介绍了一种名为布尔矩阵逻辑编程(Boolean Matrix Logic Programming,BMLP)的新方法,利用布尔矩阵来评估大型逻辑程序。我们引入了一个新系统 $BMLP_{active}$,它通过主动学习引导信息实验,从而高效地探索基因组假设空间。与亚符号方法相比,$BMLP_{active}$使用datalog逻辑程序,以可解释的逻辑表示编码了一个被广泛接受的细菌主机的最先进的GEM。值得注意的是,与随机实验相比,$BMLP_{active}$ 可以用更少的训练实例成功地学习一对基因之间的相互作用,克服了实验设计空间增大的问题。BMLP_{active}$能够快速优化代谢模型,为微生物工程的自我驱动实验室提供了一种现实的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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