Computing biological model parameters by parallel statistical model checking. Mancini, T.; Tronci, E.; Salvo, I.; Mari, F.; Massini, A.; and Melatti, I. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9044:542-554, Springer Verlag, 2015. cited By 1; Conference of 3rd International Work-Conference on Bioinformatics and Biomedical Engineering, IWBBIO 2015 ; Conference Date: 15 April 2015 Through 17 April 2015; Conference Code:115599
Computing biological model parameters by parallel statistical model checking [link]Paper  abstract   bibtex   
Biological models typically depend on many parameters. Assigning suitable values to such parameters enables model individualisation. In our clinical setting, this means finding a model for a given patient. Parameter values cannot be assigned arbitrarily, since inter-dependency constraints among them are not modelled and ignoring such constraints leads to biologically meaningless model behaviours. Classical parameter identification or estimation techniques are typically not applicable due to scarcity of clinical measurements and the huge size of parameter space. Recently, we have proposed a statistical algorithm that finds (almost) all biologically meaningful parameter values. Unfortunately, such algorithm is computationally extremely intensive, taking up to months of sequential computation. In this paper we propose a parallel algorithm designed as to be effectively executed on an arbitrary large cluster of multi-core heterogenous machines. © Springer International Publishing Switzerland 2015.
@ARTICLE{Mancini2015542,
author={Mancini, T. and Tronci, E. and Salvo, I. and Mari, F. and Massini, A. and Melatti, I.},
title={Computing biological model parameters by parallel statistical model checking},
journal={Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
year={2015},
volume={9044},
pages={542-554},
note={cited By 1; Conference of 3rd International Work-Conference on Bioinformatics and Biomedical Engineering, IWBBIO 2015 ; Conference Date: 15 April 2015 Through 17 April 2015;  Conference Code:115599},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84925275587&partnerID=40&md5=2cb3ff224d168ef55928e7927d8e800b},
affiliation={Computer Science Department, Sapienza University of Rome, Italy},
abstract={Biological models typically depend on many parameters. Assigning suitable values to such parameters enables model individualisation. In our clinical setting, this means finding a model for a given patient. Parameter values cannot be assigned arbitrarily, since inter-dependency constraints among them are not modelled and ignoring such constraints leads to biologically meaningless model behaviours. Classical parameter identification or estimation techniques are typically not applicable due to scarcity of clinical measurements and the huge size of parameter space. Recently, we have proposed a statistical algorithm that finds (almost) all biologically meaningful parameter values. Unfortunately, such algorithm is computationally extremely intensive, taking up to months of sequential computation. In this paper we propose a parallel algorithm designed as to be effectively executed on an arbitrary large cluster of multi-core heterogenous machines. © Springer International Publishing Switzerland 2015.},
keywords={Bioinformatics;  Biomedical engineering;  Clustering algorithms;  Model checking, Biological modeling;  Clinical measurements;  Estimation techniques;  Inter-dependencies;  Modeling behaviour;  Sequential computations;  Statistical algorithm;  Statistical model checking, Parameter estimation},
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editor={Ortuno F., Rojas I},
sponsors={Algorithms for Molecular Biology; BioData Mining; BioMed Central; CITIC-UGR; E-Health Business Development BULL; Faculty of Sciences, Universidad de Granada; GigaScience; Harmonic Pharma; IEEE Computational Intelligence Society; Journal of Biomedical Semantics; Source Code for Biology and Medicine},
publisher={Springer Verlag},
issn={03029743},
isbn={9783319164793},
language={English},
abbrev_source_title={Lect. Notes Comput. Sci.},
document_type={Conference Paper},
source={Scopus},
}
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