We present a methodology based on grammatical inference algorithms applied to the linguistic modeling of biological regulation networks. The linguistic approach to the problem of regulation networks was proposed by COLLADO-VIDES, who proved and formalized the need for use of context sensitive languages to represent such networks. The learning of context sensitive languages is a difficult task, our proposed methodology describes this class from language with a simpler nature that can be learned by already consolidated grammars inference algorithms. In addition to the proposed methodology, we suggest promising directions for this research
This article describes how a unifying approach based on machine learning enables inference of gene ...
International audienceRecent sequencing projects and technological progress in the field are giving ...
Computational Systems Biology: Inference and Modelling provides an introduction to, and overview of,...
We present a methodology based on grammatical inference algorithms applied to the linguistic modelin...
International audienceLearning the language of biological sequences is an appealing challenge for th...
Gene regulatory networks are powerful abstractions of biological systems. Since the advent of high-t...
In this work we explore the application of Gene Regulatory Networks (GRN) inference methods to analy...
This volume explores recent techniques for the computational inference of gene regulatory networks (...
The objective of this thesis is twofold. Firstly, we want to study the potential of recurrent neural...
Syntactic pattern recognition-based agents have been proven to be a useful tool for constructing rea...
This paper examines the inductive inference of a complex grammar with neural networks¿specifically, ...
Recent FMRI studies indicate that language related brain regions are engaged in artificial grammar (...
International audienceThis chapter presents how the formal methods can be used to analyze biological...
International audienceSystems Biology focuses on the understanding of complex biological systems as ...
The aim of this book is to advocate and promote network models of linguistic systems that are both b...
This article describes how a unifying approach based on machine learning enables inference of gene ...
International audienceRecent sequencing projects and technological progress in the field are giving ...
Computational Systems Biology: Inference and Modelling provides an introduction to, and overview of,...
We present a methodology based on grammatical inference algorithms applied to the linguistic modelin...
International audienceLearning the language of biological sequences is an appealing challenge for th...
Gene regulatory networks are powerful abstractions of biological systems. Since the advent of high-t...
In this work we explore the application of Gene Regulatory Networks (GRN) inference methods to analy...
This volume explores recent techniques for the computational inference of gene regulatory networks (...
The objective of this thesis is twofold. Firstly, we want to study the potential of recurrent neural...
Syntactic pattern recognition-based agents have been proven to be a useful tool for constructing rea...
This paper examines the inductive inference of a complex grammar with neural networks¿specifically, ...
Recent FMRI studies indicate that language related brain regions are engaged in artificial grammar (...
International audienceThis chapter presents how the formal methods can be used to analyze biological...
International audienceSystems Biology focuses on the understanding of complex biological systems as ...
The aim of this book is to advocate and promote network models of linguistic systems that are both b...
This article describes how a unifying approach based on machine learning enables inference of gene ...
International audienceRecent sequencing projects and technological progress in the field are giving ...
Computational Systems Biology: Inference and Modelling provides an introduction to, and overview of,...