Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Understanding Nature Through the Symbiosis of Information Science, Bioinformatics, and Neuroinformatics Kapitel lesen Erstes Kapitel lesen

2014 | OriginalPaper | Buchkapitel

15. Fuzzy Logic and Rule-Based Methods in Bioinformatics

verfasst von : Lipo Wang, Feng Chu, Wei Xie

Erschienen in: Springer Handbook of Bio-/Neuroinformatics

Verlag: Springer Berlin Heidelberg

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

This chapter reviews some fuzzy logic and rule-based approaches in bioinformatics. Among the fuzzy approaches, we emphasize fuzzy neural networks (FNN), which have advantages from both fuzzy logic (e.g., linguistic rules and reduced computation) and neural networks (e.g., ability to learn from data and universal approximation). After the overview in Sect. 15.1, the structure and algorithm of the FNN are reviewed in Sect. 15.2. In Sect. 15.3, we describe a t-test-based gene importance ranking method followed by a description of how we use the FNN to classify three important microarray datasets, for lymphoma, small round blue cell tumor (SRBCT), and ovarian cancer (Sect. 15.4). Section 15.5 reviews various fuzzy and rule-based approaches to microarray data classification proposed by other authors, while Sect. 15.6 reviews fuzzy and rule-based approaches to clustering and prediction in microarray data. We discuss and draw some conclusions in Sect. 15.7.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel Analysis of Multiple DNA Microarray Datasets
Nächstes Kapitel Phylogenetic Cladograms: Tools for Analyzing Biomedical Data
Literatur
15.1.
15.2.
V. Kecman: Learning and Soft Computing, Support Vector machines, Neural Networks and Fuzzy Logic Models (MIT, Cambridge 2001)MATH
15.3.
L. Wang, X. Fu: Data Mining with Computational Intelligence (Springer, Berlin, Heidelberg 2005)MATH
15.4.
S. Mitra, Y. Hayashi: Bioinformatics with soft computing, IEEE Trans. Syst. Man Cybern. C 36, 616–635 (2006)CrossRef
15.5.
M. Schena, D. Shalon, R.W. Davis, P.O. Brown: Quantitative monitoring of gene expression patterns with a complementary DNA microarray, Science 2, 467–470 (1995)CrossRef
15.6.
T.R. Golub, D.K. Slonim, P. Tamayo, C. Huard, M. Gaasenbeek, J.P. Mesirov, H. Coller, M.L. Loh, J.R. Downing, M.A. Caligiuri, C.D. Bloomfield, E.S. Lander: Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring, Science 286, 531–537 (1999)CrossRef
15.7.
S. Dudoit, J. Fridlyand, T.P. Speed: Comparison of discrimination methods for the classification of tumors using gene expression data, J. Am. Stat. Assoc. 97, 77–87 (2002)MATHMathSciNetCrossRef
15.8.
J.M. Khan, J.S. Wei, M. Ringner, L.H. Saal, M. Ladanyi, F. Westermann, F. Berthold, M. Schwab, C.R. Antonescu, C. Peterson, P.S. Meltzer: Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks, Nat. Med. 7, 673–679 (2001)CrossRef
15.9.
M.P. Brown, W.N. Grundy, D. Lin, N. Cristianini, C.W. Sugnet, T.S. Furey, M. Ares Jr., D. Haussler: Knowledge-based analysis of microarray gene expression data by using support vector machines, Proc. Natl. Acad. Sci. USA 97, 262–267 (2000)CrossRef
15.10.
L. Wang, F. Chu, W. Xie: Accurate cancer classification using expressions of very few genes, IEEE-ACM Trans. Comput. Biol. Bioinform. 4(1), 40–53 (2007)MathSciNetCrossRef
15.11.
R. Tibshirani, T. Hastie, B. Narashiman, G. Chu: Diagnosis of multiple cancer types by shrunken centroids of gene expression, Proc. Natl. Acad. Sci. USA 99, 6567–6572 (2002)CrossRef
15.12.
Y. Frayman, L. Wang: Data mining using dynamically constructed recurrent fuzzy neural networks, Research and Devevelopment in Knowledge Discovery and Data Mining, Vol. 1394 (Springer, Berlin, Heidelberg 1998) pp. 122–131CrossRef
15.13.
Y. Frayman, L. Wang: A Dynamically-constructed fuzzy neural controller for direct model reference adaptive control of multi-input-multi-output nonlinear processes, Soft Comput. 6, 244–253 (2002)MATHCrossRef
15.14.
F. Chu, W. Xie, L. Wang: Gene selection and cancer classification using a fuzzy neural network, Proc. North-Am. Fuzzy Inf. Process. Conf. (NAFIPS 2004), Vol. 2 (2004) pp. 555–559
15.15.
W. Xie, F. Chu, L. Wang, E.T. Lim: A fuzzy neural network for intelligent data processing, SPIE Proc. 5812, 283–290 (2005)CrossRef
15.16.
Y. Frayman, L.P. Wang, C. Wan: Cold rolling mill thickness control using the cascade-correlation neural network, Control Cybern. 31(2), 327–342 (2002)MATH
15.17.
L. Wang, Y. Frayman: A Dynamically generated fuzzy neural network and its application to torsional vibration control of tandem cold rolling mill spindles, Eng. Appl. Artif. Intell. 15(6), 541–550 (2002)CrossRef
15.18.
C.M. Higgins, R.M. Goodman: Fuzzy rule-based networks for control, IEEE Trans. Fuzzy Syst. 2, 82–88 (1994)CrossRef
15.19.
15.20.
L. Wang (Ed.): Support Vector Machines: Theory and Applications (Springer, Berlin, Heidelberg 2005)MATH
15.21.
J. Devore, R. Peck: Statistics: The Exploration and Analysis of Data, 3rd edn. (Duxbury, Pacific Grove 1997)MATH
15.22.
V.G. Tusher, R. Tibshirani, G. Chu: Significance analysis of microarrays applied to the ionizing radiation response, Proc. Natl. Acad. Sci. USA 98, 5116–5121 (2001)MATHCrossRef
15.23.
R. Tibshirani, T. Hastie, B. Narasimhan, G. Chu: Class predicition by nearest shrunken centroids with applications to DNA microarrays, Stat. Sci. 18, 104–117 (2003)MATHMathSciNetCrossRef
15.24.
A.A. Alizadeh, M.B. Eisen, R.E. Davis, C. Ma, I.S. Lossos, A. Rosenwald, J.C. Boldrick, H. Sabet, T. Tran, X. Yu, J.I. Powell, L. Yang, G.E. Marti, T. Moore, J. Hudson Jr., L. Lu, D.B. Lewis, R.T. Tibshirani, G. Sherlock, W.C. Chan, T.C. Greiner, D.D. Weisenburger, J.O. Armitage, R. Warnke, R. Levy, W. Wilson, M.R. Grever, J.C. Byrd, D. Botstein, P.O. Brown, L.M. Staudt: Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling, Nature 403, 503–511 (2000)CrossRef
15.25.
15.26.
O. Troyanskaya, M. Cantor, G. Sherlock, P. Brown, T. Hastie, R. Tibshirani, D. Botstein, R.B. Altman: Missing value estimation methods for DNA microarrays, Bioinformatics 17, 520–525 (2001)CrossRef
15.27.
15.28.
15.29.
G. Schaefer, T. Nakashima: Data mining of gene expression data by fuzzy and hybrid fuzzy methods, IEEE Trans. Inf. Technol. Biomed. 14, 23–29 (2010)CrossRef
15.30.
U. Alon, N. Barkai, D. Notterman, K. Gish, S. Ybarra, D. Mack, A. Levine: Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays, Proc. Natl. Acad. Sci. USA 96, 6745–6750 (1999)CrossRef
15.31.
P. Maji, S.K. Pal: Fuzzy-rough sets for information measures and selection of relevant genes from microarray data, IEEE Trans. Syst. Cybern. B 40, 741–752 (2010)CrossRef
15.32.
M. West, C. Blanchette, H. Dressman, E. Huang, S. Ishida, R. Spang, H. Zuzan, J.A. Olson, J.R. Marks, J.R. Nevins: Predicting the clinical status of human breast cancer by using gene expression profiles, Proc. Natl. Acad. Sci. USA 2(20), 11462–11467 (2001)CrossRef
15.33.
T.C.T.M. van der Pouw Kraan, F.A. van Gaalen, P.V. Kasperkovitz, N.L. Verbeet, T.J.M. Smeets, M.C. Kraan, M. Fero, P.-P. Tak, T.W.J. Huizinga, E. Pieterman, F.C. Breedveld, A.A. Alizadeh, C.L. Verweij: Rheumatoid arthritis is a heterogeneous disease: Evidence for differences in the activation of the STAT-1 pathway between rheumatoid tissues, Arthritis Rheum. 48(8), 2132–2145 (2003)CrossRef
15.34.
T.C.T.M. van der Pouw Kraan, C.A. Wijbrandts, L.G.M. van Baarsen, A.E. Voskuyl, F. Rustenburg, J.M. Baggen, S.M. Ibrahim, M. Fero, B.A.C. Dijkmans, P.P. Tak, C.L. Verweij: Rheumatoid arthritis subtypes identified by genomic profiling of peripheral blood cells: Assignment of a type I interferon signature in a subpopulation of patients, Ann. Rheum. Dis. 66(8), 1008–1014 (2007)CrossRef
15.35.
P. Maji: Fuzzy-rough supervised attribute clustering algorithm and classification of microarray data, IEEE Trans. Syst. Cybern. B 41, 222–233 (2011)CrossRef
15.36.
Y. Tang, Y.-Q. Zhang, Z. Huang, X. Hu, Y. Zhao: Recursive fuzzy granulation for gene subsets extraction and cancer classification, IEEE Trans. Inf. Technol. Biomed. 12, 723–730 (2008)CrossRef
15.37.
D. Singh, P.G. Febbo, K. Ross, D.G. Jackson, J. Manola, C. Ladd, P. Tamayo, A.A. Renshaw, A.V. DʼAmico, J.P. Richie, E.S. Lander, M. Loda, P.W. Kantoff, T.R. Golub, W.R. Sellers: Gene expression correlates of clinical prostate cancer behavior, Cancer Cell 1(2), 203–209 (2002)CrossRef
15.38.
P. Du, J. Gong, E.S. Wurtele, J.A. Dickerson: Modeling gene expression networks using fuzzy logic, IEEE Trans. Syst. Cybern. B 35, 1351–1359 (2005)CrossRef
15.39.
L. Mendoza, E.R. Alvarez-Buylla: Dynamics of the genetic regulatory network for Arabidopsis thaliana flower morphogenesis, J. Theor. Biol. 193, 307–319 (1998)CrossRef
15.40.
L. Mendoza, D. Thieffry, E.R. Alvarez-Buylla: Genetic control of flower morphogenesis in Arabidopsis thaliana: A logical analysis, Bioinformatics 15, 593–606 (1999)CrossRef
15.41.
I.A. Maraziotis, A. Dragomir, A. Bezerianos: Gene networks reconstruction and time-series prediction from microarray data using recurrent neural fuzzy networks, IET Syst. Biol. 1, 41–50 (2007)CrossRef
15.42.
P.T. Spellman, G. Sherlock, M.Q. Zhang, V.R. Iver, K. Anders, M.B. Eisen, P.O. Brown, D. Botstein, B. Futcher: Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization, Mol. Biol. Cell 9, 3273–3297 (1998)CrossRef
15.43.
M. Ronen, R. Rosenberg, B.I. Shraiman, U. Allon: Assigning number to the arrows: parameterizing a gene regulation network by using accurate expression kinetics, Proc. Natl. Acad. Sci. 99, 10555–10560 (2002)CrossRef
15.44.
S.S. Shen-Orr, R. Milo, S. Mangan, U. Alon: Network motif in the transcriptional regulation network of Escherichia coli, Nat. Genet. 31, 64–68 (2002)CrossRef
15.45.
F.C. Neidhardt, M.A. Savageau: Regulation beyond the operon. In: Escherichia coli and Salmonella: Cellular and Molecular Biology, 2nd edn., ed. by F.C. Neidhardt (Am. Soc. of Microbiology, Washington 1996) pp. 1310–1324
15.46.
S. Mitra, R. Das, Y. Hayashi: Genetic Networks and Soft Computing, IEEE-ACM Trans. Comput. Biol. Bioinform. 8, 616–635 (2011)CrossRef
15.47.
X. Hu, M. Ng, F.-X. Wu, B.A. Sokhansanj: Mining, modeling, and evaluation of subnetworks: From large biomolecular networks and its comparison study, IEEE Trans. Inf. Technol. Biomed. 13, 184–194 (2009)CrossRef
15.48.
J. Shaik, M. Yeasin: Fuzzy-adaptive-subspace-iteration-based two-way clustering of microarray data, IEEE-ACM Trans. Comput. Biol. Bioinform. 6, 244–259 (2009)CrossRef
15.49.
T. Li, S. Ma, M. Ogihara: Document clustering via adaptive subspace iteration, Proc. ACM SIGIRʼ04 (2004) pp. 218–225
15.50.
X. Chen, S.Y. Leung, S.T. Yeuen, K.M. Chu, J. Ji, R. Li, A.S.Y. Chan, S. Law, O.G. Troyanskaya, J. Wong, S. So, D. Botstein, P.O. Brown: Variation in gene expression patterns in human gastric cancers, Mol. Biol. Cell 14, 3208–3215 (2003)CrossRef
15.51.
O. Sjahputera, J.M. Keller, J.W. Davis, K.H. Taylor, F. Rahmatpanah, H. Shi, D.T. Anderson, S.N. Blisard, R.H. Luke, M. Popescu, G.C. Arthur, C.W. Caldwell: Relational analysis of CpG islands methylation and gene expression in human lymphomas using possibilistic c-means clustering and modified cluster fuzzy density, IEEE-ACM Trans. Comput. Biol. Bioinform. 2, 176–189 (2007)CrossRef
15.52.
P.C.H. Ma, K.C.C. Chan: Incremental fuzzy mining of gene expression data for gene function prediction, IEEE Trans. Biomed. Eng. 58, 1246–1252 (2011)CrossRef
15.53.
Y. Lee, C.K. Lee: Classification of multiple cancer types by mulitcategory support vector machines using gene expression data, Bioinformatics 19, 1132–1139 (2003)CrossRef
15.54.
J.M. Deutsch: Evolutionary algorithms for finding optimal gene sets in microarray prediction, Bioinformatics 19, 45–52 (2003)CrossRef
15.55.
B. Liu, C. Wan, L. Wang: An efficient semi-unsupervised gene selection method via spectral biclustering, IEEE Trans. Nano-Biosci. 5(2), 110–114 (2006)CrossRef
15.56.
X.J. Fu, L.P. Wang: A GA-based novel RBF classifier with class-dependent features, Proc. 2002 IEEE Congr. Evol. Comput. (CEC 2002), Vol. 2 (2002) pp. 1890–1894
15.57.
X.J. Fu, L.P. Wang: Rule extraction from an RBF classifier based on class-dependent features, Proc. 2002 IEEE Congr. Evol. Comput. (CEC 2002), Vol. 2 (2002) pp. 1916–1921
15.58.
L. Wang, N. Zhou, F. Chu: A general wrapper approach to selection of class-dependent features, IEEE Trans. Neural Netw. 19(7), 1267–1278 (2008)CrossRef
Metadaten
Titel
Fuzzy Logic and Rule-Based Methods in Bioinformatics
verfasst von
Lipo Wang
Feng Chu
Wei Xie
Copyright-Jahr
2014
Buch
Springer Handbook of Bio-/Neuroinformatics

Print ISBN: 978-3-642-30573-3

Electronic ISBN: 978-3-642-30574-0

Copyright-Jahr: 2014

DOI
https://doi.org/10.1007/978-3-642-30574-0_15