Zobrazeno 1 - 10
of 141
pro vyhledávání: '"MUGGLETON, STEPHEN H."'
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
Externí odkaz:
http://arxiv.org/abs/2408.14487
Autor:
Ai, Lun, Muggleton, Stephen H.
We describe a datalog query evaluation approach based on efficient and composable boolean matrix manipulation modules. We first define an overarching problem, Boolean Matrix Logic Programming (BMLP), which uses boolean matrices as an alternative comp
Externí odkaz:
http://arxiv.org/abs/2408.10369
Recent attention to relational knowledge bases has sparked a demand for understanding how relations change between entities. Petri nets can represent knowledge structure and dynamically simulate interactions between entities, and thus they are well s
Externí odkaz:
http://arxiv.org/abs/2405.11412
Techniques to autonomously drive research have been prominent in Computational Scientific Discovery, while Synthetic Biology is a field of science that focuses on designing and constructing new biological systems for useful purposes. Here we seek to
Externí odkaz:
http://arxiv.org/abs/2405.06724
Autor:
Ai, Lun, Liang, Shi-Shun, Dai, Wang-Zhou, Hallett, Liam, Muggleton, Stephen H., Baldwin, Geoff S.
An important application of Synthetic Biology is the engineering of the host cell system to yield useful products. However, an increase in the scale of the host system leads to huge design space and requires a large number of validation trials with h
Externí odkaz:
http://arxiv.org/abs/2308.12740
Publikováno v:
Machine Learning 2023
The topic of comprehensibility of machine-learned theories has recently drawn increasing attention. Inductive Logic Programming (ILP) uses logic programming to derive logic theories from small data based on abduction and induction techniques. Learned
Externí odkaz:
http://arxiv.org/abs/2205.10250
In Meta-Interpretive Learning (MIL) the metarules, second-order datalog clauses acting as inductive bias, are manually defined by the user. In this work we show that second-order metarules for MIL can be learned by MIL. We define a generality orderin
Externí odkaz:
http://arxiv.org/abs/2106.07464
The application of Artificial Intelligence (AI) to synthetic biology will provide the foundation for the creation of a high throughput automated platform for genetic design, in which a learning machine is used to iteratively optimise the system throu
Externí odkaz:
http://arxiv.org/abs/2105.07758
Inductive logic programming (ILP) is a form of logic-based machine learning. The goal is to induce a hypothesis (a logic program) that generalises given training examples. As ILP turns 30, we review the last decade of research. We focus on (i) new me
Externí odkaz:
http://arxiv.org/abs/2102.10556
Publikováno v:
Mach.Learn. 100, 755-778 (2021)
Meta-Interpretive Learners, like most ILP systems, learn by searching for a correct hypothesis in the hypothesis space, the powerset of all constructible clauses. We show how this exponentially-growing search can be replaced by the construction of a
Externí odkaz:
http://arxiv.org/abs/2101.05050