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Journal of Biomedicine and Biotechnology
Volume 2006, Article ID 91908, 7 pages
http://dx.doi.org/10.1155/JBB/2006/91908
Review Article

Fuzzy Logic in Medicine and Bioinformatics

1Departamento de Psiquiatría, Radiología y Salud Pública, Facultad de Medicina, Universidad de Santiago de Compostela, Santiago de Compostela 15782, Spain
2Departamento de Análisis Matemático, Facultad de Matemáticas, Universidad de Santiago de Compostela, Santiago de Compostela 15782, Spain

Received 29 August 2005; Revised 9 December 2005; Accepted 13 December 2005

Copyright © 2006 Angela Torres and Juan J. Nieto. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. T H Jobe and C M Helgason, “The fuzzy cube and causal efficacy: representation of concomitant mechanisms in stroke,” Neural Networks, vol. 11, no. 3, pp. 549–555, 1998. View at Publisher · View at Google Scholar
  2. C M Helgason and T H Jobe, “Perception-based reasoning and fuzzy cardinality provide direct measures of causality sensitive to initial conditions in the individual patient (Invited paper),” International Journal of Computational Cognition, vol. 1, no. 2, pp. 70–104, 2003. View at Google Scholar
  3. C M Helgason, D S Malik, S-C Cheng, T H Jobe, and J N Mordeson, “Statistical versus fuzzy measures of variable interaction in patients with stroke,” Neuroepidemiology, vol. 20, no. 2, pp. 77–84, 2001. View at Publisher · View at Google Scholar
  4. B Kosko, Neural Networks and Fuzzy Systems, Prentice-Hall, Englewood Cliffs, NJ, 1992.
  5. B Kosko, Fuzzy Thinking: The New Science of Fuzzy Logic, Hyperion Press, New York, NY, 1993.
  6. K Sadegh-Zadeh, “Fundamentals of clinical methodology: 3. Nosology,” Artificial Intelligence in Medicine, vol. 17, no. 1, pp. 87–108, 1999. View at Publisher · View at Google Scholar
  7. M F Abbod, D G von Keyserlingk, D A Linkens, and M Mahfouf, “Survey of utilisation of fuzzy technology in Medicine and Healthcare,” Fuzzy Sets and Systems, vol. 120, no. 2, pp. 331–349, 2001. View at Publisher · View at Google Scholar
  8. P Marchais, “De l'esprit et des modes de classification en psychiatrie [Classification in psychiatry: principles, modes and ways of thinking],” Annales Medico-Psychologiques, vol. 160, no. 3, pp. 247–252, 2002. View at Publisher · View at Google Scholar
  9. S Barro and R Marín, Fuzzy Logic in Medicine, Physica, Heidelberg, Germany, 2002.
  10. K Boegl, K P Adlassnig, Y Hayashi, T E Rothenfluh, and H Leitich, “Knowledge acquisition in the fuzzy knowledge representation framework of a medical consultation system,” Artificial Intelligence in Medicine, vol. 30, no. 1, pp. 1–26, 2004. View at Publisher · View at Google Scholar
  11. M Mahfouf, M F Abbod, and D A Linkens, “A survey of fuzzy logic monitoring and control utilisation in medicine,” Artificial Intelligence in Medicine, vol. 21, no. 1–3, pp. 27–42, 2001. View at Publisher · View at Google Scholar
  12. J N Mordeson, D S Malik, and S-C Cheng, Fuzzy Mathematics in Medicine, Physica, Heidelberg, Germany, 2000.
  13. F Steimann, “On the use and usefulness of fuzzy sets in medical AI,” Artificial Intelligence in Medicine, vol. 21, no. 1–3, pp. 131–137, 2001. View at Publisher · View at Google Scholar
  14. P S Szczepaniak, P JG Lisoba, and J Kacprzyk, Fuzzy Systems in Medicine, Physica, Heidelberg, Germany, 2000.
  15. C A Naranjo, K E Bremner, M Bazoon, and I B Turksen, “Using fuzzy logic to predict response to citalopram in alcohol dependence,” Clinical Pharmacology and Therapeutics, vol. 62, no. 2, pp. 209–224, 1997. View at Publisher · View at Google Scholar
  16. L D Lascio, A Gisolfi, A Albunia, G Galardi, and F Meschi, “A fuzzy-based methodology for the analysis of diabetic neuropathy,” Fuzzy Sets and Systems, vol. 129, no. 2, pp. 203–228, 2002. View at Publisher · View at Google Scholar
  17. G Zahlmann, B Kochner, I Ugi et al., “Hybrid fuzzy image processing for situation assessment,” IEEE Engineering in Medicine and Biology Magazine, vol. 19, no. 1, pp. 76–83, 2000. View at Publisher · View at Google Scholar
  18. B A Sproule, M Bazoon, K I Shulman, I B Turksen, and C A Naranjo, “Fuzzy logic pharmacokinetic modeling: application to lithium concentration prediction,” Clinical Pharmacology and Therapeutics, vol. 62, no. 1, pp. 29–40, 1997. View at Publisher · View at Google Scholar
  19. E Stip, J Dufresne, B Boulerice, and R Elie, “Accuracy of the Pepin method to determine appropriate lithium dosages in healthy volunteers,” Journal of Psychiatry & Neuroscience, vol. 26, no. 4, pp. 330–335, 2001. View at Google Scholar
  20. M E Brandt, T P Bohan, L A Kramer, and J M Fletcher, “Estimation of CSF, white and gray matter volumes in hydrocephalic children using fuzzy clustering of MR images,” Computerized Medical Imaging and Graphics, vol. 18, no. 1, pp. 25–34, 1994. View at Publisher · View at Google Scholar
  21. Y Lu, T Jiang, and Y Zang, “Region growing method for the analysis of functional MRI data,” NeuroImage, vol. 20, no. 1, pp. 455–465, 2003. View at Publisher · View at Google Scholar
  22. J A Dickerson and C M Helgason, “The characterization of stroke subtype and science of evidence-based medicine using fuzzy logic,” Journal of Neurovascular Disease, vol. 2, no. 4, pp. 138–144, 1997. View at Google Scholar
  23. C M Helgason and T H Jobe, “Causal interactions, fuzzy sets and cerebrovascular “accident”: the limits of evidence-based medicine and the advent of complexity-based medicine,” Neuroepidemiology, vol. 18, no. 2, pp. 64–74, 1999. View at Publisher · View at Google Scholar
  24. E I Papageorgiou, C D Stylios, and P P Groumpos, “An integrated two-level hierarchical system for decision making in radiation therapy based on fuzzy cognitive maps,” IEEE Transactions on Biomedical Engineering, vol. 50, no. 12, pp. 1326–1339, 2003. View at Publisher · View at Google Scholar
  25. S Oshita, K Nakakimura, and T Sakabe, “Hypertension control during anesthesia. Fuzzy logic regulation of nicardipine infusion,” IEEE Engineering in Medicine and Biology Magazine, vol. 13, no. 5, pp. 667–670, 1994. View at Publisher · View at Google Scholar
  26. M Johnson, K Firoozbakhsh, M Moniem, and M Jamshidi, “Determining flexor-tendon repair techniques via soft computing,” IEEE Engineering in Medicine and Biology Magazine, vol. 20, no. 6, pp. 176–183, 2001. View at Publisher · View at Google Scholar
  27. A E Hassanien, “Intelligent data analysis of breast cancer based on rough set theory,” International Journal on Artificial Intelligence Tools, vol. 12, no. 4, pp. 465–479, 2003. View at Publisher · View at Google Scholar
  28. H Seker, M O Odetayo, D Petrovic, and R N Naguib, “A fuzzy logic based-method for prognostic decision making in breast and prostate cancers,” IEEE Transactions on Information Technology in Biomedicine, vol. 7, no. 2, pp. 114–122, 2003. View at Publisher · View at Google Scholar
  29. J Schneider, G Peltri, N Bitterlich et al., “Fuzzy logic-based tumor marker profiles including a new marker tumor M2-PK improved sensitivity to the detection of progression in lung cancer patients,” Anticancer Research, vol. 23, no. 2A, pp. 899–906, 2003. View at Google Scholar
  30. N Belacel and M R Boulassel, “Multicriteria fuzzy classification procedure PROCFTN: methodology and medical application,” Fuzzy Sets and Systems, vol. 141, no. 2, pp. 203–217, 2004. View at Publisher · View at Google Scholar
  31. R J Stanley, R H Moss, W Van Stoecker, and C Aggarwal, “A fuzzy-based histogram analysis technique for skin lesion discrimination in dermatology clinical images,” Computerized Medical Imaging and Graphics, vol. 27, no. 5, pp. 387–396, 2003. View at Publisher · View at Google Scholar
  32. H Axer, J Jantzen, D G Keyserlingk, and G Berks, “The application of fuzzy-based methods to central nerve fiber imaging,” Artificial Intelligence in Medicine, vol. 29, no. 3, pp. 225–239, 2003. View at Publisher · View at Google Scholar
  33. G E Matt, M R Turingan, Q T Dinh, J A Felsch, M F Hovell, and C Gehrman, “Improving self-reports of drug-use: numeric estimates as fuzzy sets,” Addiction, vol. 98, no. 9, pp. 1239–1247, 2003. View at Publisher · View at Google Scholar
  34. G Zouridakis, N N Boutros, and B H Jansen, “A fuzzy clustering approach to study the auditory P50 component in schizophrenia,” Psychiatry Research, vol. 69, no. 2-3, pp. 169–181, 1997. View at Publisher · View at Google Scholar
  35. E Massad, N R Ortega, C J Struchiner, and M N Burattini, “Fuzzy epidemics,” Artificial Intelligence in Medicine, vol. 29, no. 3, pp. 241–259, 2003. View at Publisher · View at Google Scholar
  36. E O Im and W Chee, “Fuzzy logic and nursing,” Nursing Philosophy, vol. 4, no. 1, pp. 53–60, 2003. View at Publisher · View at Google Scholar
  37. Q M Zhu, X W Sun, and A G Pipe, “A fuzzy controller to overcome EA accommodation,” in Proceedings of IFAC conference on new technologies for computer control, pp. 493–498, Hong Kong, China, 2001.
  38. A Torres and J J Nieto, “Fuzzy logic and technology in medicine and psychiatry,” preprint, 2004.
  39. N G Bourbakis, “Bio-imaging and bio-informatics,” IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics, vol. 33, no. 5, pp. 726–727, 2003. View at Publisher · View at Google Scholar
  40. R Fuchs, “From sequence to biology: the impact on bioinformatics,” Bioinformatics, vol. 18, no. 4, pp. 505–506, 2002. View at Publisher · View at Google Scholar
  41. A Valencia, “Bioinformatics: biology by other means,” Bioinformatics, vol. 18, no. 12, pp. 1551–1552, 2002. View at Publisher · View at Google Scholar
  42. T Kulikova, P Aldebert, N Althorpe et al., “The EMBL nucleotide sequence database,” Nucleic Acids Research, vol. 32, no. database issue, pp. D27–D30, 2004. View at Publisher · View at Google Scholar
  43. B C Chang and S K Halgamuge, “Protein motif extraction with neuro-fuzzy optimization,” Bioinformatics, vol. 18, no. 8, pp. 1084–1090, 2002. View at Publisher · View at Google Scholar
  44. A Torres and J J Nieto, “The fuzzy polynucleotide space: basic properties,” Bioinformatics, vol. 19, no. 5, pp. 587–592, 2003. View at Publisher · View at Google Scholar
  45. S Tomida, T Hanai, H Honda, and T Kobayashi, “Analysis of expression profile using fuzzy adaptive resonance theory,” Bioinformatics, vol. 18, no. 8, pp. 1073–1083, 2002. View at Publisher · View at Google Scholar
  46. M Schlosshauer and M Ohlsson, “A novel approach to local reliability of sequence alignments,” Bioinformatics, vol. 18, no. 6, pp. 847–854, 2002. View at Publisher · View at Google Scholar
  47. O Cordón, F Gomide, F Herrera, F Hoffmann, and L Magdalena, “Ten years of genetic fuzzy systems: current framework and new trends,” Fuzzy Sets and Systems, vol. 141, no. 1, pp. 5–31, 2004. View at Google Scholar
  48. N Belacel, M Čuperlović-Culf, M Laflamme, and R Ouellette, “Fuzzy J-Means and VNS methods for clustering genes from microarray data,” Bioinformatics, vol. 20, no. 11, pp. 1690–1701, 2004. View at Publisher · View at Google Scholar
  49. Y Huang and Y Li, “Prediction of protein subcellular locations using fuzzy k-NN method,” Bioinformatics, vol. 20, no. 1, pp. 21–28, 2004. View at Publisher · View at Google Scholar
  50. C Carleos, F Rodriguez, H Lamelas, and J A Baro, “Simulating complex traits influenced by genes with fuzzy-valued effects in pedigreed populations,” Bioinformatics, vol. 19, no. 1, pp. 144–148, 2003. View at Publisher · View at Google Scholar
  51. D Dembélé and P Kastner, “Fuzzy C-means method for clustering microarray data,” Bioinformatics, vol. 19, no. 8, pp. 973–980, 2003. View at Google Scholar
  52. A Heger and L Holm, “Sensitive pattern discovery with ‘fuzzy’ alignments of distantly related proteins,” Bioinformatics, vol. 19, no. suppl 1, pp. i130–i137, 2003. View at Publisher · View at Google Scholar
  53. P J Woolf and Y Wang, “A fuzzy logic approach to analyzing gene expression data,” Physiological Genomics, vol. 3, no. 1, pp. 9–15, 2000. View at Google Scholar
  54. R Blankenbecler, M Ohlsson, C Peterson, and M Ringnér, “Matching protein structures with fuzzy alignments,” Proceedings of the National Academy of Sciences of the United States of America, vol. 100, no. 21, pp. 11936–11940, 2003. View at Publisher · View at Google Scholar
  55. D H Wang, N K Lee, and T S Dillon, “Extraction and optimization of fuzzy protein sequence classification rules using GRBF neural networks,” Neural Information Processing—Letters and Reviews, vol. 1, no. 1, pp. 53–59, 2003. View at Google Scholar
  56. H Ressom, R Reynolds, and R S Varghese, “Increasing the efficiency of fuzzy logic-based gene expression data analysis,” Physiological Genomics, vol. 13, no. 2, pp. 107–117, 2003. View at Google Scholar
  57. R Lukac, K N Plataniotis, B Smolka, and A N Venetsanopoulos, “cDNA microarray image processing using fuzzy vector filtering framework,” Fuzzy Sets and Systems, vol. 152, no. 1, pp. 17–35, 2005. View at Publisher · View at Google Scholar
  58. S Bandyopadhyay, “An efficient technique for superfamily classification of amino acid sequences: feature extraction, fuzzy clustering and prototype selection,” Fuzzy Sets and Systems, vol. 152, no. 1, pp. 5–16, 2005. View at Publisher · View at Google Scholar
  59. J J Nieto and A Torres, “Midpoints for fuzzy sets and their application in medicine,” Artificial Intelligence in Medicine, vol. 27, no. 1, pp. 81–101, 2003. View at Publisher · View at Google Scholar
  60. J J Nieto, A Torres, and M M Vázquez-Trasande, “A metric space to study differences between polynucleotides,” Applied Mathematics Letters, vol. 16, no. 8, pp. 1289–1294, 2003. View at Publisher · View at Google Scholar
  61. M Zaus, Crisp and Soft Computing With Hypercubical Calculus, Physica, Heidelberg, Germany, 1999.
  62. J J Nieto, A Torres, D N Georgiou, and T Karakasidis, “Fuzzy polynucleotide spaces and metrics,” to appear in Bulletin of Mathematical Biology.
  63. N K Kasabov, Foundations of Neural Networks, Fuzzy Systems and Knowledge Engineering, MIT Press, Cambridge, Mass, 1996.
  64. P M Lewinsohn, P Rohde, and R A Brown, “Level of current and past adolescent cigarette smoking as predictors of future substance use disorders in young adulthood,” Addiction, vol. 94, no. 6, pp. 913–921, 1999. View at Publisher · View at Google Scholar
  65. C B Nelson and H U Wittchen, “DSM-IV alcohol disorders in a general population sample of adolescents and young adults,” Addiction, vol. 93, no. 7, pp. 1065–1077, 1998. View at Publisher · View at Google Scholar
  66. J Casasnovas and F Rosselló, “Averaging fuzzy biopolymers,” Fuzzy Sets and Systems, vol. 152, no. 1, pp. 139–158, 2005. View at Publisher · View at Google Scholar
  67. V Castelli, J-M Aury, O Jaillon et al., “Whole genome sequence comparisons and “full-length” cDNA Sequences: a combined approach to evaluate and improve Arabidopsis genome annotation,” Genome Research, vol. 14, no. 3, pp. 406–413, 2004. View at Publisher · View at Google Scholar
  68. S T Cole, R Brosch, J Parkhill et al., “Deciphering the biology of Mycobacterium tuberculosis from the complete genome sequence,” Nature, vol. 393, no. 6685, pp. 537–544, 1998. View at Publisher · View at Google Scholar
  69. G Deckert, P V Warren, T {Gaasterland} et al., “The complete genome of the hyperthermophilic bacterium Aquifex aeolicus,” Nature, vol. 392, no. 6674, pp. 353–358, 1998. View at Publisher · View at Google Scholar