Formal Concept Analysis Applications in Chemistry: From Radionuclides and Molecular Structure to Toxicity and Diagnosis

Chapter

Abstract

Recent chemical applications of Formal Concept Analysis are reviewed, showing that molecular structure and activity of substances may be related through association rules, which is exemplified for mutagenicity and hepatotoxicity cases. Nuclear chemistry and nuclear medicine cases are explored, where attributes of radionuclides are related. A study of biotechnology application to uranium bioremediation is conducted and some Gram-positive bacteria are found as better uranium uptakers.

References

  1. Bartel H-G, Bruggemann R (1998) Application of formal concept analysis to structure-activity relationships. Fresenius J Anal Chem 361:23–38CrossRefGoogle Scholar
  2. Bäuerlein E (2003) Biomineralization of unicellular organisms: an unusual membrane biochemistry for the production of inorganic nano- and microstructures. Angew Chem Int Ed Engl 42:614–641CrossRefGoogle Scholar
  3. Bemis GW, Murcko MA (1996) The properties of known drugs. 1. Molecular Frameworks. J Med Chem 39:2887–2893CrossRefGoogle Scholar
  4. Berardi M, Appice A, Loglisci C, Leo P (2006) Supporting visual exploration of discovered association rules through multi-dimensional scaling. In: Esposito F, Râs ZW, Malerba D, Semeraro G (eds) Foundations of intelligent systems, 16th International Symposium, ISMIS 2006, vol 4203, Lecture notes in computer science. Springer, BerlinGoogle Scholar
  5. Carpineto C, Romano G (2004) Concept data analysis: theory and applications. Wiley, Hoboken, NJ, 07030, USA, pp 1–220CrossRefGoogle Scholar
  6. Carrió I, González P, Estorch M, Canessa J, Mitjavila M, Massardo T (2003) Medicina nuclear. Aplicaciones clínicas. Masson. S.A, BarcelonaGoogle Scholar
  7. Chen M, Vijay V, Shi Q, Liu Z, Jang H, Tong W (2011) FDA-approved drug labeling for the study of drug-induced liver injury. Drug Discov Today 16:697–703CrossRefGoogle Scholar
  8. Clark RD, Wolohan PR, Hodgkin EE, Kelly JH, Sussman NL (2004) Modelling in vitro hepatotoxicity using molecular interaction fields and SIMCA. J Mol Graph Model 22:487–497CrossRefGoogle Scholar
  9. Dambach D, Andrews B, Moulin F (2005) New technologies and screening strategies for hepatotoxicity: use of in vitro models. Toxicol Pathol 33:17–26CrossRefGoogle Scholar
  10. Ekins S, Williams AJ, Xu JJ (2010) A predictive ligand-based Bayesian model for human drug-induced liver injury. Drug Metab Dispos 38(12):2302–2308CrossRefGoogle Scholar
  11. Franke R, Gruska A, Giuliani A, Benigni R (2001) Prediction of rodent carcinogenicity of aromatic amines: a quantitative structure-activity relationships model. Carcinogenesis 22(9):1561–1571CrossRefGoogle Scholar
  12. Ganter B, Wille R (1998) Formal concept analysis: mathematical foundations. Springer, BerlinGoogle Scholar
  13. Gardiner EJ, Gillet VJ (2015) Perspectives on knowledge discovery algorithms recently introduced in chemoinformatics: rough set theory, association rule mining, emerging patterns, and formal concept analysis. J Chem Inf Model 55(9):1781–1803Google Scholar
  14. Greene N, Fisk L, Naven RT, Note RR, Patel ML, Pelletier DJ (2010) Developing structure-activity relationships for the prediction of hepatotoxicity. Chem Res Toxicol 19(23):1215–1222CrossRefGoogle Scholar
  15. Gupta R, Saxena RK, Mohapatra H, Ahuja P (2002) Microbial variables for bioremediation of heavy metals from industrial effluents. In: Singh VP, Stapleton JR (eds) Biotransformations: bioremediation and technology for health and environmental protection, vol 36, Progress in industrial microbiology. Elsevier, Amsterdam, pp 189–230CrossRefGoogle Scholar
  16. Hammaini A, González F, Ballester A, Blázques ML, Muñoz JA (2007) Biosorption of heavy metals by activated sludge and their desorption characteristics. J Environ Manage 84:419–426CrossRefGoogle Scholar
  17. Herzog H, Tellmann L, Scholten B, Cohenen HH, Qaim SM (2008) PET imaging problems with the non-standard positron emitters Yttrium-86 and Iodine-124. Q J Nucl Med Mol Imaging 52(2):159–165Google Scholar
  18. Horikoshi T, Nakajima A, Sakaguchi T (1981) Studies on the accumulation of heavy metal elements in biological systems. Eur J Appl Microbiol 12(2):90–96CrossRefGoogle Scholar
  19. Kazy SK, D’Souza SFD, Pinaki S (2009) Uranium and thorium sequestration by a Pseudomonas sp.: mechanism and chemical characterization. J Hazard Mater 163:65–72CrossRefGoogle Scholar
  20. Lattice Miner (2015) http://sourceforge.net/projects/lattice-miner/. Accessed 25 Apr 2015
  21. Leach AR, Guillet V (2007) An introduction to chemoinformatics. Kluwer, Dordrecht, The Netherlands. doi:10.1007/978-1-4020-6291-9 CrossRefGoogle Scholar
  22. Lenca P, Meyer P, Vaillant B, Lallich S (2008) On selecting interestingness measures for association rules: user oriented description and multiple criteria decision aid. Eur J Oper Res 184(2):610–628CrossRefGoogle Scholar
  23. Lounkine E, Auer J, Bajorath J (2008) Formal concept analysis for the identification of molecular fragment combinations specific for active and highly potent compounds. J Med Chem 51(17):5342–5348CrossRefGoogle Scholar
  24. Low Y, Uehara T, Minowa Y, Yamada H, Ohno Y, Urushidani T, Sedykh A, Muratov E, Kuz’min V, Fourches D, Zhu D, Rusyn I, Tropsha A (2011) Predicting drug-induced hepatotoxicity using QSAR and toxicogenomics approaches. Chem Res Toxicol 24(8):1251–1262CrossRefGoogle Scholar
  25. McNicholas PD, Murphy TB, O’Regan M (2008) Standardising the lift of an association rule. Comput Stat Data Anal 52:4712–4721CrossRefGoogle Scholar
  26. Nakajima A, Sakaguchi T (1986) Selective accumulation of heavy metals by microorganisms. Appl Microbiol Biot 24:59–64Google Scholar
  27. Nakajima A, Tsuruta T (2002) Competitive biosorption of thorium and uranium by actinomycetes. J Nucl Sci Technol 39(Suppl 3):528–531CrossRefGoogle Scholar
  28. Nakajima A, Tsuruta T (2004) Competitive biosorption of thorium and uranium by Micrococcus luteus. J Radioanal Nucl Chem 260(1):13–18CrossRefGoogle Scholar
  29. Njoku DB (2014) Drug-induced hepatotoxicity: metabolic, genetic and immunological basis. Int J Mol Sci 15:6990–7003CrossRefGoogle Scholar
  30. Noyes R (1995) Nuclear wastes cleanup technology and opportunities. Noyes, Park Ridge, NJGoogle Scholar
  31. Osborne JW (2010) Improving your data transformations: applying the Box-Cox transformation. Practical assessment. Res Eval 15(12):1–9Google Scholar
  32. Ozer JS, Chetty R, Kenna G, Palandra J, Zhang Y, Lanevschi A, Koppiker N, Souberbielle BE, Ramaiah SK (2010) Enhancing the utility of alanine aminotransferase as a reference standard biomarker for drug-induced liver injury. Regul Toxicol Pharmacol 56:237–246CrossRefGoogle Scholar
  33. Pagani M, Stone-Elander S, Larsson SA (1997) Alternative positron emission tomography with non-conventional positron emitters: effects of their physical properties on image quality and potential clinical applications. Eur J Nucl Med 24(10):1301–1327CrossRefGoogle Scholar
  34. Plasse M, Niang N, Saporta G, Villeminot A, Leblond L (2007) Combined use of association rules mining and clustering methods to find relevant links between binary rare attributes in a large data set. Comput Stat Data Anal 52(1):596–613CrossRefGoogle Scholar
  35. Priss U (2008) FcaStone-FCA file format and interoperability software. In: Croitoru M, Jaschkë R, Rudolph S (eds). Conceptual structures and the web. Proceedings of the third conceptual structures and tool interoperability workshop, pp 33–43Google Scholar
  36. Quintero NY, Restrepo G, Cohen IM (2013a) Chemotopological study of positron emitters ra-dionuclides used in PET diagnostic imaging: physical, physico-chemical, dosimetric, quantum and nuclear properties. J Radioanal Nucl Chem 295(2):823–833CrossRefGoogle Scholar
  37. Quintero NY, Restrepo G, Cohen IM (2013b) Relating β + radionuclides’ properties by order theory. J Radioanal Nucl Chem 298(3):1937–1946CrossRefGoogle Scholar
  38. Raaphorst RM, Windhorst AD, Elsinga PH, Colabufo NA, Lammertsma AA, Luurtsema G (2015) Radiopharmaceuticals for assessing ABC transporters at the blood-brain barrier. Clin Pharmacol Ther 97(4):362–371CrossRefGoogle Scholar
  39. Reitz T, Merroun ML, Rossberg A, Selenska-Pobell S (2010) Interactions of Sulfolobus acidocaldarius with uranium. Radiochim Acta 98:249–257CrossRefGoogle Scholar
  40. Reitz T, Rossberg A, Barkleit A, Selenska-Pobell S, Merroun ML (2014) Decrease of U (VI) immobilization capability of the facultative anaerobic strain Paenibacillus sp. JG-TB8 under anoxic conditions due to strongly reduced phosphatase activity. PLoS One 9(8):1–13CrossRefGoogle Scholar
  41. Restrepo G, Mesa H, Llanos EJ, Villaveces JL (2004) Topological study of the periodic system. JChem Inf Comput Sci 44(1):68–75CrossRefGoogle Scholar
  42. Restrepo G, Mesa H (2011) Chemotopology: beyond neighbourhoods. Curr Comput Aided Drug Des 7(2):90–97CrossRefGoogle Scholar
  43. Restrepo G, Basak S, Mills D (2011) Comparison of QSARs and characterization of structural basis of bioactivity using partial order theory and formal concept analysis: a case study with mutagenicity. Curr Comput Aided Drug Des 7(2):109–121CrossRefGoogle Scholar
  44. Restrepo G, Harré R (2015) Mereology of quantitative structure-activity relationships models. HYLE Int J Phil Chem 21:19–38Google Scholar
  45. Rincón-Villamizar E, Restrepo G (2014) Rules relating hepatotoxicity with structural attributes of drugs. Toxicol Environ Chem 96(4):594–613CrossRefGoogle Scholar
  46. Sanche R, Lonergan KF (2006) Variable reduction for predictive modeling with modeling clustering. Casualty Actuarial Society Forum, pp 89–100Google Scholar
  47. Singh N, Gadi R (2012) Bioremediation of Ni (II) and Cu (II) from wastewater by the nonliving biomass of Brevundimonas vesicularis. J Environ Chem Ecotoxicol 4(8):137–142Google Scholar
  48. Smith JT (2011) Nuclear accidents. In: Hester RE, Harrison RM (eds) Nuclear power and environment, vol 32, Issues in environmental science and technology. Royal Society of Chemistry, LondonGoogle Scholar
  49. Stephens LJ (1998) Theory and problems of beginning statistics, Schaum’s outline series. McGraw-Hill, MexicoGoogle Scholar
  50. Suk KT, Kim DJ (2012) Drug-induced liver injury: present and future. Clin Mol Hepatol 18(3):249–257CrossRefGoogle Scholar
  51. Todeschini R, Consonni V (2009) Molecular descriptors for chemoinformatics. Wiley-VCH, WeinheimCrossRefGoogle Scholar
  52. Tsuruta T (2007) Removal and recovery of uranium using microorganisms isolated from North American uranium deposits. Am J Environ Sci 3(2):60–66CrossRefGoogle Scholar
  53. van Hullebusch E, Lens PNL, Tabak HH (2005) Developments in bioremediation of soils and sediments polluted with metals and radionuclides. 3. Influence of chemical speciation and bioavailability on contaminants immobilization/mobilization bio-processes. Rev Environ Sci Biotechnol 4:185–212CrossRefGoogle Scholar
  54. Yevtushenko SA (2000) System of data analysis “Concept Explorer”. Proceedings of the 7th national conference on Artificial Intelligence KII, Russia, pp 127–134Google Scholar
  55. Yi Z, Lian B (2012) Adsorption of U (VI) by Bacillus mucilaginosus. J Radioanal Nucl Chem 293(1):321–329CrossRefGoogle Scholar
  56. Yi Z, Yao J (2012) Kinetic and equilibrium study of uranium (VI) adsorption by Bacillus licheniformis. J Radioanal Nucl Chem 293(3):907–914CrossRefGoogle Scholar
  57. Ziessmann H, O’Malley J, Thrall JH (2007) Medicina nuclear: Los requisitos en radiología. Elsevier, EspañaGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Laboratorio de Química TeóricaUniversidad de PamplonaPamplonaColombia
  2. 2.Universidad de AntioquiaAntioquiaColombia
  3. 3.Universidad Pontificia BolivarianaAntioquiaColombia
  4. 4.Universidad Católica de OrienteAntioquiaColombia
  5. 5.Bioinformatics Group, Department of Computer ScienceUniversität LeipzigLeipzigGermany

Personalised recommendations