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Application of Rough Set Theory to Prediction of Antimicrobial Activity of Bis-Quaternary Imidazolium Chlorides

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Języki publikacji
EN
Abstrakty
EN
The paper investigates relationships between chemical structure, surface active properties and antibacterial activity of 70 bis-quaternary imidazolium chlorides. Chemical structure and properties of imidazolium chlorides were described by 7 condition attributes and antimicrobial properties were mapped by a decision attribute. Dominance-based Rough Set Approach (DRSA) was applied to discover a priori unknown rules exhibiting monotonicity relationships in the data, which hold in some parts of the evaluation space. Strong decision rules discovered in this way may enable creating prognostic models of new compounds with favorable antimicrobial properties. Moreover, relevance of the attributes estimated from the discovered rules allows to distinguish which of the structure and surface active properties describe compounds that have the most preferable and the least preferable antimicrobial properties.
Wydawca
Rocznik
Strony
315--330
Opis fizyczny
Bibliogr. 23 poz., tab., rys., wykr.
Twórcy
  • Nicolaus Copernicus University, Collegium Medicum, Department of Pharmaceutical Technology, Jurasza 2, 85-089 Bydgoszcz, Poland
  • Nicolaus Copernicus University, Collegium Medicum, Department of Pharmaceutical Technology, Jurasza 2, 85-089 Bydgoszcz, Poland
  • Poznań University of Technology, Institute of Computing Science, Piotrowo 2, 60-965 Poznań, Poland
  • Poznań University of Technology, Institute of Computing Science, Piotrowo 2, 60-965 Poznań, Poland
  • Poznań University of Technology, Institute of Chemical Technology, Skłodowskiej-Curie 2, 60-965 Poznań, Poland
  • Poznań University of Technology, Institute of Chemical Technology, Skłodowskiej-Curie 2, 60-965 Poznań Poland
  • Nicolaus Copernicus University, Collegium Medicum, Department of Microbiology, Skłodowskiej-Curie 9, 85-094 Bydgoszcz, Poland
  • Nicolaus Copernicus University, Collegium Medicum, Department of Microbiology, Skłodowskiej-Curie 9, 85-094 Bydgoszcz, Poland
Bibliografia
  • [1] Błaszczyński, J., Słowiński, R., Stefanowski, J.: Feature set-based consistency sampling in bagging ensembles. In: From Local Patterns To Global Models (LEGO), ECML/PKDD Workshop, pages 19–35 (2009)
  • [2] Błaszczyński, J., Słowiński, R., Stefanowski, J.: Variable Consistency Bagging Ensembles. Transactions on Rough Sets, LNCS 11, pp. 40–52, Springer (2010)
  • [3] Błaszczyński, J., Słowiński, R., Szeląg, M.: Sequential Covering Rule Induction Algorithm for Variable Consistency Rough Set Approaches. Information Sciences, 181(5), 987–1002 (2011)
  • [4] Błaszczyński, J., Słowiński, R., Susmaga, R.: Rule-based Estimation of Attribute Relevance. In: Yao, J.T. et al. (eds.), RSKT, LNCS 4481, 36–44 (2011)
  • [5] Błaszczyński, J., Greco, S., Słowiński, R.: Inductive Discovery of Laws Using Monotonic Rules. Engineering Applications of Artificial Intelligence, 25, pp. 284-294, (2012)
  • [6] Breiman, L.: Bagging predictors. Machine Learning, 24(2):123–140 (1996)
  • [7] Breiman, L.: Random forests. Machine Learning, 45(1):5–32 (2001)
  • [8] Chlebicki, J., Wegrzynska, J.: Surface-active, Micellar, and Antielectrostatic Properties of Bis-ammoniumSalts J. Colloid Interf.Sci. 323, 372–378 (2008)
  • [9] Greco S., Matarazzo B., Słowiński R., Rough Sets Theory for Multicriteria Decision Analysis, EuropeanJournal of Operational Research, 129, 2001, pp. 1–47
  • [10] Greco, S., Słowiński, R., Szczęch, I.: Properties of Rule Interestingness Measures and Alternative Approachesto Normalization of Measures. Information Sciences, 216, 1–16 (2012)
  • [11] Hansch, C., Leo, A.: Exploring QSAR. Fundamentals and Applications in Chemistry and Biology, ACSProfessional Reference Book, American Chemical Society, Washington (1995)
  • [12] Katritzky, A .R., Pacureanu, L. M., Slavov, S. H., Dobchev, D. M., Shah, D. O., Karelson, M.: QSPR Study of the First and Second Critical Micelle Concentrations of Cationic Surfactants, Comput. Chem. Eng. 33,321–332 (2009)
  • [13] Kolmogorov, A.: Foundations of Probability. AMS Chelsea publishing, Providence, Rhode Island (1956)
  • [14] Krysiński, J., Płaczek, J., Skrzypczak, A., Błaszczak, J., Prędki, B.: Analysis of Relationships Between Structure, Surface Properties and Antimicrobial Activity of Quaternary Ammonium Chlorides, QSAR Comb.Sci., 28, 995–1002 (2009)
  • [15] Kuang, M., Zhou, S., Lei, J., Li, Q.: Low Environmental Sensitive Antistatic Material Based on Poly (vinylchloride)/quaternary Ammonium Salt by Blending with Poly (ethylene oxide), Appl. Polym. Sci. 109, 3887–3891 (2008)
  • [16] Lacrama, A. M., Putz, M. V., Ostafe, V.: A Spectral-SAR Model for the Anionic- Cationic Interaction in IonicLiquids: Application to Vibrio fischeri Ecotoxicity, Int. J. Mol. Sci., 8, 842–863 (2007)
  • [17] Latinne, P., Debeir, O., Decaestecker, Ch.: Different ways of weakening decision trees and their impact on classification accuracy of DT combination. In: Multiple Classifier Systems, LNCS 1857, pp. 200–209.Springer (2000)
  • [18] McBain, A. J., Ledder R. G., Moore L. E., Catrenich C. E.: Effects of Quaternary-Ammonium-Based Formulationson Bacterial Community Dynamics and Antimicrobial Susceptibility, Appl. Environ. Microbiol.70, 3449 – 3456 (2004)
  • [19] Pałkowski, Ł., Błaszczynski, J., Skrzypczak, A., Błaszczak, J., Kozakowska, K., Wróblewska, J., Kozuszko,S., Gospodarek, E., Krysinski, J., Słowinski, R.: Antimicrobial activity and SAR study of new geminiimidazolium-based chlorides. Chem. Biol. Drug Des., 83, 3, 278-288 (2014)
  • [20] Słowiński, R. Greco, S., Matarazzo, B.: Rough Sets in Decision Making. In: Meyers, R.A. (ed.), Encyclopediaof Complexity and Systems Science, Springer, New York, pp. 7753–7786 (2009)
  • [21] Saydel, J. K., Schaper, K. J.: Chemische Struktur und Biologische Aktivitat von Wirkstoffen. Methoden derQuantitativen Struktur-Wirkung-Analyse, Verlag Chemie, Weinheim (1979)
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  • [23] Zupon, J. et al.: Neural Networks for Chemists, Verlag Chemie, Weinheim (1993)
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-578a4796-bd59-4bc7-ae32-54a685a783f0
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