Learning Multi-valued Biological Models with Delayed Influence from Time-Series Observations

Ribeiro, Tony; Magnin, Morgan; Inoue, Katsumi; Sakama, Chiaki

doi:10.1109/icmla.2015.19

Cited by 13 publications

(9 citation statements)

References 12 publications

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“…Until now, the LF1T algorithm [20,48,50] only tackled the learning of synchronous deterministic programs. Using the formalism introduced in the previous sections, it can now be revised to learn systems from transitions produced from any semantics respecting Theorem 1 like the three semantics defined above.…”

Section: Gulamentioning

confidence: 99%

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Learning any memory-less discrete semantics for dynamical systems represented by logic programs

et al. 2021

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Section: Gulamentioning

confidence: 99%

“…To date, the following systems have been tackled: memory-less deterministic systems [20], systems with memory [49], probabilistic systems [33] and their multi-valued extensions [50,32]. [51] proposes a method that allows to deal with continuous time series data, the abstraction itself being learned by the algorithm.…”

Section: Introductionmentioning

confidence: 99%

Learning any memory-less discrete semantics for dynamical systems represented by logic programs

et al. 2021

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“…Given time-series data of discrete gene expression, it can learn gene interactions, thus allowing to explain and predict states changes over time (Ribeiro et al, 2020). LFIT has been applied to learn biological models, like Boolean Networks, under several semantics: memory-less deterministic systems (Inoue et al, 2014;, and their multi-valued extensions (Ribeiro et al, 2015;Martínez et al, 2016). Martínez et al (2016) combine LFIT with a reinforcement learning algorithm to learn probabilistic models with exogenous effects (effects not related to any action) from scratch.…”

Section: Learning From Trajectoriesmentioning

confidence: 99%

Inductive logic programming at 30

et al. 2021

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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 and background knowledge. As ILP turns 30, we review the last decade of research. We focus on (i) new meta-level search methods, (ii) techniques for learning recursive programs, (iii) new approaches for predicate invention, and (iv) the use of different technologies. We conclude by discussing current limitations of ILP and directions for future research.

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“…In bioinformatics, learning the dynamics of biological systems can correspond to the identification of the influence of genes and can help to understand their interactions. While building a model, the choice of a relevant semantics associated to the studied system represents a major issue with regard to the kind of dynamical properties to and their multi-valued extensions [15,11]. All those methods are dedicated to discrete systems or assume an abstraction of time series data as discrete transitions.…”

Section: Introductionmentioning

confidence: 99%

“…For this purpose, we propose a modeling of discrete multi-valued systems as logic programs in which each rule represents that a variable possibly takes some value at the next state, extending the formalism introduced in [12,15,11]. Research in multi-valued logic programming has proceeded along three different directions [10]: bilattice-based logics [5,7], quantitative rule sets [17] and annotated logics [2,1].…”

Section: Introductionmentioning

confidence: 99%

Learning Dynamics with Synchronous, Asynchronous and General Semantics

Ribeiro

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Magnin

et al. 2018

Inductive Logic Programming

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Abstract.Learning from interpretation transition (LFIT) automatically constructs a model of the dynamics of a system from the observation of its state transitions. So far, the systems that LFIT handles are restricted to synchronous deterministic dynamics, i.e., all variables update their values at the same time and, for each state of the system, there is only one possible next state. However, other dynamics exist in the field of logical modeling, in particular the asynchronous semantics which is widely used to model biological systems. In this paper, we focus on a method that learns the dynamics of the system independently of its semantics. For this purpose, we propose a modeling of multi-valued systems as logic programs in which a rule represents what can occurs rather than what will occurs. This modeling allows us to represent nondeterminism and to propose an extension of LFIT in the form of a semantics free algorithm to learn from discrete multi-valued transitions, regardless of their update schemes. We show through theoretical results that synchronous, asynchronous and general semantics are all captured by this method. Practical evaluation is performed on randomly generated systems and benchmarks from biological literature to study the scalability of this new algorithm regarding the three aforementioned semantics.Keywords: Dynamical semantics, learning from interpretation transition, dynamical systems, Inductive Logic Programming IntroductionLearning the dynamics of systems with many interactive components becomes more and more important due to many applications, e.g., multi-agent systems, robotics and bioinformatics. Knowledge of a system dynamics can be used by agents and robots for planning and scheduling. In bioinformatics, learning the dynamics of biological systems can correspond to the identification of the influence of genes and can help to understand their interactions. While building a model, the choice of a relevant semantics associated to the studied system represents a major issue with regard to the kind of dynamical properties to analyze. The differences and common features of different semantics w.r.t. properties of interest (attractors, oscillators, etc.) constitutes an area of research per itself, especially in the field of Boolean networks. In [8], the author exhibits the translation from Boolean networks into logic programs and discusses the point attractors in both synchronous and asynchronous semantics. In [6], A. Garg et al. address the differences and complementarity of synchronous and asynchronous semantics to model biological networks and identify attractors. The benefits of the synchronous model are to be computationally tractable, while classical state space exploration algorithms fail on asynchronous ones. For some applications, like the biological ones, asynchronous semantics is said to capture more realistic behaviors: at a given time, a single gene can change its expression level. This results in a potential combinatorial explosion of the number of reachable states. To illustr...

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Learning Multi-valued Biological Models with Delayed Influence from Time-Series Observations

Cited by 13 publications

References 12 publications

Learning any memory-less discrete semantics for dynamical systems represented by logic programs

Learning any memory-less discrete semantics for dynamical systems represented by logic programs

Inductive logic programming at 30

Learning Dynamics with Synchronous, Asynchronous and General Semantics

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