# Active learning of digenic functions with boolean matrix logic programming

arXiv: https://arxiv.org/abs/2408.14487 · HTML: https://mlsanity.com/paper/2408.14487/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 32 references that are in the map.
- First posted on arXiv 19 Aug 2024.

## Builds on (most cited first, 4 of 4)

- [Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients](https://mlsanity.com/paper/1912.04871/) (2019)
- [A Bayesian machine scientist to aid in the solution of challenging scientific problems](https://mlsanity.com/paper/2004.12157/) (2020)
- [Towards a Formal Distributional Semantics: Simulating Logical Calculi with Tensors](https://mlsanity.com/paper/1304.5823/) (2013)
- [Boolean matrix logic programming for active learning of gene functions in genome-scale metabolic network models](https://mlsanity.com/paper/2405.06724/) (2024)

## Abstract (arXiv)

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, BMLPactive, which efficiently explores the genomic hypothesis space by guiding informative experimentation through active learning. In contrast to sub-symbolic methods, BMLPactive encodes a state-of-the-art GEM of a widely accepted bacterial host in an interpretable and logical representation using datalog logic programs. Notably, BMLPactive can successfully learn the interaction between a gene pair with fewer training examples than random experimentation, overcoming the increase in experimental design space. BMLPactive enables rapid optimisation of metabolic models and offers a realistic approach to a self-driving lab for microbial engineering.