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Decision Diagrams for the Representation and Analysis of Logical Models of Genetic Networks [chapter]

Aurélien Naldi, Denis Thieffry, Claudine Chaouiya
Lecture Notes in Computer Science  
We show that the use of this representation enables the development of efficient algorithms for the analysis of specific dynamical properties of the regulatory graphs.  ...  The complexity of biological regulatory networks calls for the development of proper mathematical methods to model their structures and to obtain insight in their dynamical behaviours.  ...  Acknowledgements We acknowledge financial support from the European Commission (contract LSHG-CT-2004-512143), the French Research Ministry through the ANR project JC05-53969 and A. Naldi PhD grant.  ... 
doi:10.1007/978-3-540-75140-3_16 fatcat:xqds246vgrhjtb2qd74ut7a3qy

A Modular, Qualitative Modeling of Regulatory Networks Using Petri Nets [chapter]

Claudine Chaouiya, Hanna Klaudel, Franck Pommereau
2011 Computational Biology  
In a first step, we briefly show how logical models of regulatory networks can be transposed into standard (place/transition) Petri nets, and discuss the capabilities of such a representation.  ...  In this context, we present two representations of logical models in terms of Petri nets.  ...  Thieffry for fruitful discussions. This work was supported by the French Research Agency (project ANR-08-SYSC-003).  ... 
doi:10.1007/978-1-84996-474-6_12 dblp:series/cb/ChaouiyaKP16 fatcat:bwkhgtz6uzdupomf2uz4vmn6de

Evolutionary Fuzzy Neural Inference System for Decision Making in Geotechnical Engineering

Min-Yuan Cheng, Hsing-Chih Tsai, Chien-Ho Ko, Wen-Te Chang
2008 Journal of computing in civil engineering  
First, an evolutionary fuzzy neural inference model ͑EFNIM͒ was constructed by combining the genetic algorithm ͑GA͒, fuzzy logic ͑FL͒, and neural network ͑NN͒.  ...  In the proposed model, GA is primarily concerned with optimizing parameters required in the fuzzy neural network; FL with imprecision and approximate reasoning; and NN with learning and curve fitting.  ...  Geotechnical decision making improves in terms of both efficiency and accuracy through the aid of artificial intelligence technology ͑Cheng and Genetic algorithm, fuzzy logic, and neural network are the  ... 
doi:10.1061/(asce)0887-3801(2008)22:4(272) fatcat:esr2h6wjjraxrete4v2fjy5i2u

Soft Computing Methods in Business Optimization

Petr Dostal
2013 Global Journal of Technology and Optimization  
The use of soft computing methods can lead to higher optimum of business decision-making, but generally in many other areas such engineering, technology, public services etc.  ...  In effect, the role model for fuzzy logic is the human mind.  ...  systems and decision processes; and the 1979 report on possibility theory and soft data analysis.  ... 
doi:10.4172/2229-8711.1000e103 fatcat:ckyp6klm2jbwvmbqtarl5phfsy

Soft Computing Methods in Business Optimization

Petr Dostal
2013 Global Journal of Technology and Optimization  
The use of soft computing methods can lead to higher optimum of business decision-making, but generally in many other areas such engineering, technology, public services etc.  ...  In effect, the role model for fuzzy logic is the human mind.  ...  systems and decision processes; and the 1979 report on possibility theory and soft data analysis.  ... 
doi:10.4172/1985-9406.1000e103 fatcat:elz7joekkfebpmu2v3xagf6wwy


Mukhamedieva D. T, Safarova L.U
2017 International Journal of Research in Engineering and Technology  
In these cases, it is advisable to apply such systems as modelling, decision-making and regulation that use technological tools of soft computing (Soft Computing).  ...  There is usually no opportunity to form simple sufficient symbolic-form models for complex processes defined as indeterminacy (inaccuracy, unstochasticity, incompleteness, fuzziness) in the background  ...  diagram supported by neural and fuzzy network has become sufficiently close to the reference model.  ... 
doi:10.15623/ijret.2017.0609005 fatcat:j7lusinurrgjbaiicbphnvohmi

A Problog Model for Analyzing Gene Regulatory Networks

António Gonçalves, Irene M. Ong, Jeffrey A. Lewis, Vítor Santos Costa
2012 International Conference on Inductive Logic Programming  
We introduce logic-based regulation models based on state-of-the-art work on statistical relational learning, to show that network hypotheses can be generated from existing gene expression data for use  ...  Gaining an understanding of the dynamics that govern how a cell will respond to diverse environmental cues is difficult using intuition alone.  ...  Funds through the FCT Fundacão para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) within project HORUS (PTDC/EIA-EIA/100897/2008) and by the US 760 Department of Energy (  ... 
dblp:conf/ilp/GoncalvesOLC12 fatcat:sun6cyk425duniedhuxoxlm67e

The Application Of Learning Systems To Support Decision For Stakeholder And Infrastructures Managers Based On Crowdsourcing

Alfonso Bastías, Álvaro González
2012 Zenodo  
To adequately support their decisions and decrease any negative impact and collateral effect, they need to use computational tools called decision support systems (DSS), but now the main source of information  ...  The actual grow of the infrastructure in develop country require sophisticate ways manage the operation and control the quality served.  ...  Fuzzy logic captures the imperfect information; neural networks have the learning engine in native form, and the genetic algorithms have the component for optimization in its model.  ... 
doi:10.5281/zenodo.1078946 fatcat:3lqhpcql6nf7veexctb46kgwfy

Decision Support and Expert Systems: Management Support Systems (4th Edition)

John S. Edwards
1996 Journal of the Operational Research Society  
The second is a chapter on applications of neural networks, genetic algorithms and fuzzy logic, which follows the existing more theoretical chapter on neural networks.  ...  Intriguingly, the new chapter in which non-USA examples are in the majority is the new one on neural networks, genetic algorithms and fuzzy logic.  ... 
doi:10.1057/jors.1996.87 fatcat:z7l3na6xljasbjnhyq6gtichuu

Creating Shareable Clinical Decision Support Rules for a Pharmacogenomics Clinical Guideline Using Structured Knowledge Representation

Margaret K Linan, Davide Sottara, Robert R Freimuth
2015 AMIA Annual Symposium Proceedings  
We utilized the Unified Modeling Language (UML), the Health Level 7 virtual medical record (HL7 vMR) model, and standard terminologies to represent the semantics and decision logic derived from a PGx guideline  ...  The modeling and extraction processes developed here demonstrate how structured knowledge representations can be used to support the creation of shareable CDS rules from PGx guidelines.  ...  Acknowledgments The authors would like to thank Pooja Raghani for reviewing the modeling protocol.  ... 
pmid:26958298 pmcid:PMC4765632 fatcat:6ofsrkxll5fjvpibq3t3zkmvpe

Hyperspectral Image Classification using Softcomputing Techniques: A Review

A. Rajitha, P. Bhargavi, S. Jyothi
2018 International Journal of Computer Applications  
Softcomputing is an emerging field consisting of Fuzzy Logic, Neural Network and Genetic Algorithms.  ...  Hyperspectral images provide both spatial details of airborne imagery and spectral resolution for spectroscopic analysis and narrow band analysis techniques.  ...  In effect, the human mind is the role model for soft computing. Some of the important approaches in softcomptuing are Fuzzy Logic, Genetic Algorithm and Artificial Neural Networks [6] .  ... 
doi:10.5120/ijca2018917731 fatcat:ehwqqsyzxnhszaqoyaigjpr5he

How Cell Decides Between Life and Death: Mathematical Modeling of Epigenetic Landscapes of Cellular Fates [chapter]

Andrei Zinovyev, Laurence Calzone, Simon Fourquet, Emmanuel Barillot
2012 Springer Proceedings in Mathematics  
The genetic network underlying cell fate decisions is reconstructed in the form of an influence diagram together with logical rules defining possible system state changes, while the epigenetic landscape  ...  We describe the principles of the model construction and in silico experiments performed on it.  ...  We would like to acknowledge support by the APO-SYS EU FP7 project. All the authors are members of the team Systems Biology of  ... 
doi:10.1007/978-3-642-20164-6_16 fatcat:2ghtifjrovdgfik32w2dczqmdi

Learning hardware using multiple-valued logic - Part 1: introduction and approach

M. Perkowski, D. Foote, Qihong Chen, A. Al-Rabadi, L. Jozwiak
2002 IEEE Micro  
Observe recent rapid progress in soft computing, that is, artificial neural networks (ANNs), fuzzy logic, rough sets, genetic algorithms, and genetic and evolutionary programming.  ...  These methods teach the computer system by examples and evaluations of the system's behavior rather than completely programming the system.  ...  For instance, recursion and decision diagrams are difficult to parallelize or pipeline and, in general, to implement in hardware structures.  ... 
doi:10.1109/mm.2002.1013303 fatcat:fjeo52iks5hafg5gcuz53h7sxy

Soft computing in engineering design – A review

K.M. Saridakis, A.J. Dentsoras
2008 Advanced Engineering Informatics  
offered by fuzzy logic, artificial neural networks and genetic algorithms for further improvement of both the design outcome and the design process itself.  ...  Within this context, fuzzy logic (FL), genetic algorithms (GA) and artificial neural networks (ANN), as well as their fusion are reviewed in order to examine the capability of soft computing methods and  ...  Acknowledgements The present research work has been done within the framework of the project Pythagoras II (EPEAEK). University of Patras is a member of the EU-funded I*PROMS Network of Excellence.  ... 
doi:10.1016/j.aei.2007.10.001 fatcat:tbyo2vuoifccte2xrmoxzx2ori

Development of teaching programs of artificial intelligence methods in aerospace education

Vladimir Kalugin, Alexander Lutsenko, Irina Romanova, Ding Ye, D.A. Kozorez, V.T. Kalugin, S. Yong, N. Severina
2022 SHS Web of Conferences  
The analysis of Federal state educational standards (FSES) and professional standards for enlarged groups of specialties and directions (EGSD) 24.00.00 AEROSPACE ENGINEERING was conducted and the perspective  ...  programs on the foundations of artificial intelligence and intelligent data analysis were proposed.  ...  Computer-aided decision support systems. 4. Expert systems. 5. Neural networks. 6. Cognitive modeling. 7. Genetic algorithms and evolutionary modeling. 8.  ... 
doi:10.1051/shsconf/202213701006 fatcat:fk3ixxt65bcf7gxyxtzzhjegve
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