Skip to main content

2019 | OriginalPaper | Buchkapitel

15. Computational Modelling and Pattern Recognition in Bioinformatics

verfasst von : Nikola K. Kasabov

Erschienen in: Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence

Verlag: Springer Berlin Heidelberg

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

search-config
loading …

Abstract

This chapter explores the ability of SNN to capture changes in Bioinformatics data for predicting events or classifying biological states from DNA, gene and protein data. It starts with a bioinformatics primer.

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!

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!

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!

Literatur
1.
Zurück zum Zitat N. Kasabov, Evolving Connectionist Systems: The Knowledge Engineering Approach (Springer, London, 2007). (1st edn 2002)MATH N. Kasabov, Evolving Connectionist Systems: The Knowledge Engineering Approach (Springer, London, 2007). (1st edn 2002)MATH
2.
Zurück zum Zitat N. Kasabov (ed.), Springer Handbook of Bio-/Neuroinformatics (Springer, Berlin, 2014) N. Kasabov (ed.), Springer Handbook of Bio-/Neuroinformatics (Springer, Berlin, 2014)
3.
Zurück zum Zitat D. Hofstadter, Godel, Escher, Bach: An Eternal Golden Braid (Basic Books, New York, 1979) D. Hofstadter, Godel, Escher, Bach: An Eternal Golden Braid (Basic Books, New York, 1979)
4.
Zurück zum Zitat P. Baldi, S. Brunak, Bioinformatics—A Machine Learning Approach (MIT Press, Cambridge, 1998, 2001) P. Baldi, S. Brunak, Bioinformatics—A Machine Learning Approach (MIT Press, Cambridge, 1998, 2001)
5.
Zurück zum Zitat T. Friend, Genome projects complete sequence. USA Today, 23 June 2000 T. Friend, Genome projects complete sequence. USA Today, 23 June 2000
6.
Zurück zum Zitat L. Fu, An expert network for DNA sequence analysis. IEEE Intell. Syst. Appl 14(1), 65–71 (1999)CrossRef L. Fu, An expert network for DNA sequence analysis. IEEE Intell. Syst. Appl 14(1), 65–71 (1999)CrossRef
7.
Zurück zum Zitat Y. Okazaki et al., Analysis of the mouse transcriptome based on functional annotation of 60,770 full-length cDNAs. Nature 420(6915), 563–573 (2002)MathSciNetCrossRef Y. Okazaki et al., Analysis of the mouse transcriptome based on functional annotation of 60,770 full-length cDNAs. Nature 420(6915), 563–573 (2002)MathSciNetCrossRef
8.
Zurück zum Zitat J.S. Mattick, I.V. Makunin, Small regulatory RNAs in mammals. Hum Mol Genet. 14(Spec No 1), R121–R132 (2005) J.S. Mattick, I.V. Makunin, Small regulatory RNAs in mammals. Hum Mol Genet. 14(Spec No 1), R121–R132 (2005)
9.
Zurück zum Zitat A.F. Bompfünewerer, et al., Evolutionary patterns of non-coding RNAs. Theory Biosci. 123, 301–369 (2005) A.F. Bompfünewerer, et al., Evolutionary patterns of non-coding RNAs. Theory Biosci. 123, 301–369 (2005)
10.
Zurück zum Zitat J. Allen et al., Discrimination of modes of action of antifungal substances by use of metabolic footprinting. Appl. Environ. Microbiol. 70(10), 6157–6165 (2004)CrossRef J. Allen et al., Discrimination of modes of action of antifungal substances by use of metabolic footprinting. Appl. Environ. Microbiol. 70(10), 6157–6165 (2004)CrossRef
11.
Zurück zum Zitat S. Brown, S. Holtzman, T. Kaufman, R. Denell, Characterization of the tribolium deformed ortholog and its ability to directly regulate deformed target genes in the rescue of a Drosophila deformed null mutant. Dev. Genes. Evol. 209(7), 389–398 (1999)CrossRef S. Brown, S. Holtzman, T. Kaufman, R. Denell, Characterization of the tribolium deformed ortholog and its ability to directly regulate deformed target genes in the rescue of a Drosophila deformed null mutant. Dev. Genes. Evol. 209(7), 389–398 (1999)CrossRef
12.
Zurück zum Zitat A.E. Pasquinelli, B.J. Reinhart, F. Slack, M.Q. Martindale, M.I. Kuroda, B. Maller, D.C. Hayward, E.E. Ball, B. Degnan, P. Müller, J. Spring, A. Srinivasan, M. Fishman M, Finnerty, J. Corbo, M. Levine, P. Leahy, E. Davidson, G. Ruvkun, Conservation of the sequence and temporal expression of let-7 heterochronic regulatory RNA. Nature 408(6808), 86–89 (2000) A.E. Pasquinelli, B.J. Reinhart, F. Slack, M.Q. Martindale, M.I. Kuroda, B. Maller, D.C. Hayward, E.E. Ball, B. Degnan, P. Müller, J. Spring, A. Srinivasan, M. Fishman M, Finnerty, J. Corbo, M. Levine, P. Leahy, E. Davidson, G. Ruvkun, Conservation of the sequence and temporal expression of let-7 heterochronic regulatory RNA. Nature 408(6808), 86–89 (2000)
13.
Zurück zum Zitat C.S. L. Lai, D. Gerrelli, A.P. Monaco, S.E. Fisher, A.J. Copp, FOXP2 expression during brain development coincides with adult sites of pathology in a severe speech and language disorder. Brain 126, 2455–2462 (2003). https://doi.org/10.1093/brain/awg247 C.S. L. Lai, D. Gerrelli, A.P. Monaco, S.E. Fisher, A.J. Copp, FOXP2 expression during brain development coincides with adult sites of pathology in a severe speech and language disorder. Brain 126, 2455–2462 (2003). https://​doi.​org/​10.​1093/​brain/​awg247
14.
Zurück zum Zitat F.L. Lim et al., Mcm1p-induced DNA bending regulates the formation of ternary transcription factor complexes. Mol. Cell. Biol. 23(2), 450–461 (2003)CrossRef F.L. Lim et al., Mcm1p-induced DNA bending regulates the formation of ternary transcription factor complexes. Mol. Cell. Biol. 23(2), 450–461 (2003)CrossRef
15.
Zurück zum Zitat E. Berezikov, R.H. Plasterk, Camels and zebrafish, viruses and cancer: a microRNA update. Hum. Mol. Genet. 14, 183–190 (2005)CrossRef E. Berezikov, R.H. Plasterk, Camels and zebrafish, viruses and cancer: a microRNA update. Hum. Mol. Genet. 14, 183–190 (2005)CrossRef
16.
Zurück zum Zitat M. Schena (ed.), Microarray Biochip Technology (Eaton Publishing, Natick, MA, 2000) M. Schena (ed.), Microarray Biochip Technology (Eaton Publishing, Natick, MA, 2000)
17.
Zurück zum Zitat M. Futschik, A. Jeffs, S. Pattison, N. Kasabov, M. Sullivan, A. Merrie, A. Reeve, Gene expression profiling of metastatic and non-metastatic colorectal cancer cell-lines. Genome Lett. 1(1), 1–9 (2002)CrossRef M. Futschik, A. Jeffs, S. Pattison, N. Kasabov, M. Sullivan, A. Merrie, A. Reeve, Gene expression profiling of metastatic and non-metastatic colorectal cancer cell-lines. Genome Lett. 1(1), 1–9 (2002)CrossRef
18.
Zurück zum Zitat M. Futschik, M. Sullivan, A. Reeve, N. Kasabov, Prediction of clinical behaviour and treatment of cancers. Appl. Bioinform. 3, 553–558 (2003) M. Futschik, M. Sullivan, A. Reeve, N. Kasabov, Prediction of clinical behaviour and treatment of cancers. Appl. Bioinform. 3, 553–558 (2003)
19.
Zurück zum Zitat J.L. DeRisi, V.R. Iyer, P.O. Brown, Exploring the metabolic and genetic control of gene expression on a genomic scale. Science 278(5338), 680–686 (1997)CrossRef J.L. DeRisi, V.R. Iyer, P.O. Brown, Exploring the metabolic and genetic control of gene expression on a genomic scale. Science 278(5338), 680–686 (1997)CrossRef
20.
Zurück zum Zitat H. Chu, C. Parras, K. White, F. Jimenez, Formation and specification of ventral neuroblasts is controlled by vnd in Drosophila neurogenesis. Genes Dev. 12(22), 3613–3624 (1998)CrossRef H. Chu, C. Parras, K. White, F. Jimenez, Formation and specification of ventral neuroblasts is controlled by vnd in Drosophila neurogenesis. Genes Dev. 12(22), 3613–3624 (1998)CrossRef
21.
Zurück zum Zitat N. Pal, J.C. Bezdek, On cluster validity for the fuzzy c-means model. IEEE Trans. Fuzzy Syst. 370–379 (1995) N. Pal, J.C. Bezdek, On cluster validity for the fuzzy c-means model. IEEE Trans. Fuzzy Syst. 370–379 (1995)
22.
Zurück zum Zitat M. Futschik, N. Kasabov, Fuzzy clustering in gene expression data analysis. In Proceedings of the World Congress of Computational Intelligence WCCI’2002, Hawaii, May 2002. IEEE Press M. Futschik, N. Kasabov, Fuzzy clustering in gene expression data analysis. In Proceedings of the World Congress of Computational Intelligence WCCI’2002, Hawaii, May 2002. IEEE Press
23.
Zurück zum Zitat N. Qian, T.J. Sejnowski, Predicting the secondary structure of globular protein using neural network models. J. Mol. Biol. 202, 065–884 (1988)CrossRef N. Qian, T.J. Sejnowski, Predicting the secondary structure of globular protein using neural network models. J. Mol. Biol. 202, 065–884 (1988)CrossRef
24.
Zurück zum Zitat D.S. Dimitrov, I.A. Sidorov, N.K. Kasabov, Computational biology, in Handbook of Theoretical and Computational Nanotechnology, vol. 1, ed. by M. Rieth, W. Sommers (American Scientific Publisher, 2004) D.S. Dimitrov, I.A. Sidorov, N.K. Kasabov, Computational biology, in Handbook of Theoretical and Computational Nanotechnology, vol. 1, ed. by M. Rieth, W. Sommers (American Scientific Publisher, 2004)
25.
Zurück zum Zitat N. Kasabov, I.A. Sidorov, D.S. Dimitrov, Computational intelligence, bioinformatics and computational biology: a brief overview of methods, problems and perspectives. J. Comput. Theor. Nanosci. 2(4), 473–491 (2005)CrossRef N. Kasabov, I.A. Sidorov, D.S. Dimitrov, Computational intelligence, bioinformatics and computational biology: a brief overview of methods, problems and perspectives. J. Comput. Theor. Nanosci. 2(4), 473–491 (2005)CrossRef
27.
Zurück zum Zitat J. Schaff, L.M. Loew, The virtual cell., in Pacific Symposium on Biocomputing (1999), pp. 228–239 J. Schaff, L.M. Loew, The virtual cell., in Pacific Symposium on Biocomputing (1999), pp. 228–239
28.
Zurück zum Zitat M. Tomita, Whole-cell simulation: a grand challenge of the 21st century. Trends Biotechnol. 19(6), 205–210 (2001)CrossRef M. Tomita, Whole-cell simulation: a grand challenge of the 21st century. Trends Biotechnol. 19(6), 205–210 (2001)CrossRef
29.
Zurück zum Zitat K.W. Kohn, D.S. Dimitrov, Mathematical Models of Cell Cycles, Computer Modelling and Simulation of Complex Biological Systems (1999) K.W. Kohn, D.S. Dimitrov, Mathematical Models of Cell Cycles, Computer Modelling and Simulation of Complex Biological Systems (1999)
30.
Zurück zum Zitat M.A. Gibson, E. Mjolsness, Modelling the activity of single genes, in Computational Modelling of Genetic and Biochemical Networks, ed. by J.M. Bower, H. Bolouri (MIT Press, Cambridge, 2001), pp. 3–48 M.A. Gibson, E. Mjolsness, Modelling the activity of single genes, in Computational Modelling of Genetic and Biochemical Networks, ed. by J.M. Bower, H. Bolouri (MIT Press, Cambridge, 2001), pp. 3–48
31.
Zurück zum Zitat R. Somogyi, S. Fuhrman, X. Wen, Genetic network inference in computational models and applications to large-scale gene expression data, in Computational Modelling of Genetic and Biochemical Networks, ed. by J.M. Bower, H. Bolouri (MIT Press, Cambridge, 2001), pp. 120–157 R. Somogyi, S. Fuhrman, X. Wen, Genetic network inference in computational models and applications to large-scale gene expression data, in Computational Modelling of Genetic and Biochemical Networks, ed. by J.M. Bower, H. Bolouri (MIT Press, Cambridge, 2001), pp. 120–157
32.
Zurück zum Zitat P. D’haeseleer, S. Liang, R. Somogyi, Genetic network inference; from co-expression clustering to reverse engineering. Bioinformatics 16(8), 707–726 (2000)CrossRef P. D’haeseleer, S. Liang, R. Somogyi, Genetic network inference; from co-expression clustering to reverse engineering. Bioinformatics 16(8), 707–726 (2000)CrossRef
33.
Zurück zum Zitat S.Z. Chan, N. Kasabov, L. Collins, A hybrid genetic algorithm and expectation maximization method for global gene trajectory clustering. J. Bioinform. Comput. Biol. 3(5), 1227–1242 (2005) S.Z. Chan, N. Kasabov, L. Collins, A hybrid genetic algorithm and expectation maximization method for global gene trajectory clustering. J. Bioinform. Comput. Biol. 3(5), 1227–1242 (2005)
34.
Zurück zum Zitat E. Capecci, J.L. Lobo, I. Lana, J.I. Espinosa-Ramos, N. Kasabov, Modelling Gene Interaction Networks from Time-Series Gene Expression Data using Evolving Spiking Neural Networks, Evolving Systems (Springer, Berlin, 2018) E. Capecci, J.L. Lobo, I. Lana, J.I. Espinosa-Ramos, N. Kasabov, Modelling Gene Interaction Networks from Time-Series Gene Expression Data using Evolving Spiking Neural Networks, Evolving Systems (Springer, Berlin, 2018)
35.
Zurück zum Zitat J. Dray, E. Capecci, N. Kasabov, Spiking neural networks for cancer gene expression time series modelling and analysis, in Proc. ICONIP, Springer, 2018 J. Dray, E. Capecci, N. Kasabov, Spiking neural networks for cancer gene expression time series modelling and analysis, in Proc. ICONIP, Springer, 2018
36.
Zurück zum Zitat L. Koefoed, E. Capecci, V. Jansari, N. Kasabov, Analysis of gene expression data of Ebola vaccine using spiking neural networks, in Proc. IJCNN, 2018) L. Koefoed, E. Capecci, V. Jansari, N. Kasabov, Analysis of gene expression data of Ebola vaccine using spiking neural networks, in Proc. IJCNN, 2018)
37.
Zurück zum Zitat C. Kuma, M. Mann, Bioinformatics analysis of mass spectrometry-based proteomics data sets. FEBS Lett. 583(11), 1703–1712 (2009) C. Kuma, M. Mann, Bioinformatics analysis of mass spectrometry-based proteomics data sets. FEBS Lett. 583(11), 1703–1712 (2009)
38.
Zurück zum Zitat M. Pertea, S.L. Salzberg, Between a chicken and a grape: estimating the number of human genes. Genome Biol. 11(5), 206 (2010) M. Pertea, S.L. Salzberg, Between a chicken and a grape: estimating the number of human genes. Genome Biol. 11(5), 206 (2010)
39.
Zurück zum Zitat I. Ezkurdia, D. Juan, J.M. Rodriguez, A. Frankish, M. Diekhans, J. Harrow, J. Vazquez, A. Valencia, M.L. Tress, The shrinking human protein coding complement: are there now fewer than 20,000 genes? ArXiv e-prints, 2013, 1312.7111 (2013) I. Ezkurdia, D. Juan, J.M. Rodriguez, A. Frankish, M. Diekhans, J. Harrow, J. Vazquez, A. Valencia, M.L. Tress, The shrinking human protein coding complement: are there now fewer than 20,000 genes? ArXiv e-prints, 2013, 1312.7111 (2013)
40.
Zurück zum Zitat E.H. Shen, C.C. Overly, A.R. Jones, The allen human brain atlas: comprehensive gene expression mapping of the human brain. Trends Neurosci. 35(12), 711–714 (2012)CrossRef E.H. Shen, C.C. Overly, A.R. Jones, The allen human brain atlas: comprehensive gene expression mapping of the human brain. Trends Neurosci. 35(12), 711–714 (2012)CrossRef
41.
Zurück zum Zitat S. Panda, T.K. Sato, G.M. Hampton, J.B. Hogenesch, An array of insights: application of dna chip technology in the study of cell biology. Trends Cell Biol. 13(3), 151–156 (2003)CrossRef S. Panda, T.K. Sato, G.M. Hampton, J.B. Hogenesch, An array of insights: application of dna chip technology in the study of cell biology. Trends Cell Biol. 13(3), 151–156 (2003)CrossRef
42.
Zurück zum Zitat X. Wang, M. Wu, Z. Li, C. Chan, Short time-series microarray analysis: methods and challenges. BMC Syst. Biol. 2(1), 58 (2008) X. Wang, M. Wu, Z. Li, C. Chan, Short time-series microarray analysis: methods and challenges. BMC Syst. Biol. 2(1), 58 (2008)
43.
Zurück zum Zitat A. Mortazavi, B.A. Williams, K. McCue, L. Schaeffer, B. Wold, Mapping and quantifying mammalian transcriptomes by rna-seq. Nat. Meth. 5(7), 621–628 (2008)CrossRef A. Mortazavi, B.A. Williams, K. McCue, L. Schaeffer, B. Wold, Mapping and quantifying mammalian transcriptomes by rna-seq. Nat. Meth. 5(7), 621–628 (2008)CrossRef
44.
Zurück zum Zitat L. Feuk, A.R. Carson, S.W. Scherer, Structural variation in the human genome. Nat. Rev. Genet. 7(2), 85–97 (2006)CrossRef L. Feuk, A.R. Carson, S.W. Scherer, Structural variation in the human genome. Nat. Rev. Genet. 7(2), 85–97 (2006)CrossRef
45.
Zurück zum Zitat W. Maass, Networks of spiking neurons: the third generation of neural network models. Neural Networks 10(9), 1659–1671 (1997)CrossRef W. Maass, Networks of spiking neurons: the third generation of neural network models. Neural Networks 10(9), 1659–1671 (1997)CrossRef
46.
Zurück zum Zitat W. Gerstner, Time structure of the activity in neural network models. Phys. Rev. E 51(1), 738 (1995)CrossRef W. Gerstner, Time structure of the activity in neural network models. Phys. Rev. E 51(1), 738 (1995)CrossRef
47.
Zurück zum Zitat W. Gerstner, Plausible Neural Networks for Biological Modelling. What’s different with spiking neurons? (Kluwer Academic Publishers, Dordrecht, 2001), p. 2345 W. Gerstner, Plausible Neural Networks for Biological Modelling. What’s different with spiking neurons? (Kluwer Academic Publishers, Dordrecht, 2001), p. 2345
48.
Zurück zum Zitat W. Gerstner, H. Sprekeler, G. Deco, in Theory and simulation in neuroscience. Science 338(6103), 60–65 W. Gerstner, H. Sprekeler, G. Deco, in Theory and simulation in neuroscience. Science 338(6103), 60–65
49.
Zurück zum Zitat S. Ghosh-Dastidar, H. Adeli, Improved spiking neural networks for eeg classification and epilepsy and seizure detection. Integr. Comput.-Aided Eng. 14(3), 187–212 (2007) S. Ghosh-Dastidar, H. Adeli, Improved spiking neural networks for eeg classification and epilepsy and seizure detection. Integr. Comput.-Aided Eng. 14(3), 187–212 (2007)
50.
Zurück zum Zitat N. Kasabov, E. Capecci, Spiking neural network methodology for modelling, recognition and understanding of eeg spatio-temporal data measuring cognitive processes during mental tasks. Inf. Sci. 294, 565–575 (2015)CrossRef N. Kasabov, E. Capecci, Spiking neural network methodology for modelling, recognition and understanding of eeg spatio-temporal data measuring cognitive processes during mental tasks. Inf. Sci. 294, 565–575 (2015)CrossRef
51.
Zurück zum Zitat N. Kasabov, Neucube evospike architecture for spatio-temporal modelling and pattern recognition of brain signals, in Artificial Neural Networks in Pattern Recognitioned, vol. 7477, ed. by N. Mana, F. Schwenker, E. Trentin. Lecture Notes in Computer Science (Springer, Berlin, 2012), pp. 225–243 N. Kasabov, Neucube evospike architecture for spatio-temporal modelling and pattern recognition of brain signals, in Artificial Neural Networks in Pattern Recognitioned, vol. 7477, ed. by N. Mana, F. Schwenker, E. Trentin. Lecture Notes in Computer Science (Springer, Berlin, 2012), pp. 225–243
52.
Zurück zum Zitat Y. Chen, J. Hu, N. Kasabov, Z.-G. Hou, L. Cheng, Neucuberehab: a pilot study for eeg classification in rehabilitation practice based on spiking neural networks. Neural Inf. Process. 8228(2013), 70–77 (2013) Y. Chen, J. Hu, N. Kasabov, Z.-G. Hou, L. Cheng, Neucuberehab: a pilot study for eeg classification in rehabilitation practice based on spiking neural networks. Neural Inf. Process. 8228(2013), 70–77 (2013)
53.
Zurück zum Zitat N. Kasabov, Neucube: a spiking neural network architecture for mapping, learning and understanding of spatio-temporal brain data. Neural Networks 52(2014), 62–76 (2014)CrossRef N. Kasabov, Neucube: a spiking neural network architecture for mapping, learning and understanding of spatio-temporal brain data. Neural Networks 52(2014), 62–76 (2014)CrossRef
54.
Zurück zum Zitat E. Tu, N. Kasabov, M. Othman, Y. Li, S. Worner, J. Yang, Z. Jia, Neucube(st) for spatio-temporal data predictive modelling with a case study on ecological data, in 2014 International Joint Conference on Neural Networks (IJCNN) (2014), pp. 638–645. https://doi.org/10.1109/ijcnn.2014.6889717 E. Tu, N. Kasabov, M. Othman, Y. Li, S. Worner, J. Yang, Z. Jia, Neucube(st) for spatio-temporal data predictive modelling with a case study on ecological data, in 2014 International Joint Conference on Neural Networks (IJCNN) (2014), pp. 638–645. https://​doi.​org/​10.​1109/​ijcnn.​2014.​6889717
55.
Zurück zum Zitat N. Kasabov, N.M. Scott, E. Tu, S. Marks, N. Sengupta, E. Capecci, M. Othman, M.G. Doborjeh, N. Murli, R. Hartono et al., Evolving spatio-temporal data machines based on the neucube neuromorphic framework: design methodology and selected applications. Neural Networks 78(2016), 1–14 (2016)CrossRef N. Kasabov, N.M. Scott, E. Tu, S. Marks, N. Sengupta, E. Capecci, M. Othman, M.G. Doborjeh, N. Murli, R. Hartono et al., Evolving spatio-temporal data machines based on the neucube neuromorphic framework: design methodology and selected applications. Neural Networks 78(2016), 1–14 (2016)CrossRef
56.
Zurück zum Zitat E. Tu, N. Kasabov, J. Yang, Mapping temporal variables into the neucube for improved pattern recognition, predictive modeling, and understanding of stream data. IEEE Trans. Neural Networks Learn. Syst. 28(6), 1305–1317 (2017)MathSciNetCrossRef E. Tu, N. Kasabov, J. Yang, Mapping temporal variables into the neucube for improved pattern recognition, predictive modeling, and understanding of stream data. IEEE Trans. Neural Networks Learn. Syst. 28(6), 1305–1317 (2017)MathSciNetCrossRef
58.
Zurück zum Zitat D.O. Hebb, The Organization of Behavior: A Neuropsychological Approach (Wiley, New York, 1949) D.O. Hebb, The Organization of Behavior: A Neuropsychological Approach (Wiley, New York, 1949)
59.
Zurück zum Zitat Song, K.D. Miller, L.F. Abbott, Competitive hebbian learning through spike-timing-dependent synaptic plasticity. Nat. Neurosci. 3(9), 919–926 (2000)CrossRef Song, K.D. Miller, L.F. Abbott, Competitive hebbian learning through spike-timing-dependent synaptic plasticity. Nat. Neurosci. 3(9), 919–926 (2000)CrossRef
60.
Zurück zum Zitat N. Kasabov, K. Dhoble, N. Nuntalid, G. Indiveri, Dynamic evolving spiking neural networks for on-line spatio- and spectro-temporal pattern recognition. Neural Networks 41(2013), 188–201 (2013)CrossRef N. Kasabov, K. Dhoble, N. Nuntalid, G. Indiveri, Dynamic evolving spiking neural networks for on-line spatio- and spectro-temporal pattern recognition. Neural Networks 41(2013), 188–201 (2013)CrossRef
61.
Zurück zum Zitat R. Edgar, M. Domrachev, A.E. Lash, Gene expression omnibus: Ncbi gene expression and hybridization array data repository. Nucleic Acids Res. 30(1), 207–210 (2002)CrossRef R. Edgar, M. Domrachev, A.E. Lash, Gene expression omnibus: Ncbi gene expression and hybridization array data repository. Nucleic Acids Res. 30(1), 207–210 (2002)CrossRef
62.
Zurück zum Zitat T. Barrett, S.E. Wilhite, P. Ledoux, C. Evangelista, I.F. Kim, M. Tomashevsky, K.A. Marshall, K.H. Phillippy, P.M. Sherman, M. Holko et al., Ncbi geo: archive for functional genomics data sets - update. Nucleic Acids Res. 41(D1), 991–995 (2012)CrossRef T. Barrett, S.E. Wilhite, P. Ledoux, C. Evangelista, I.F. Kim, M. Tomashevsky, K.A. Marshall, K.H. Phillippy, P.M. Sherman, M. Holko et al., Ncbi geo: archive for functional genomics data sets - update. Nucleic Acids Res. 41(D1), 991–995 (2012)CrossRef
63.
Zurück zum Zitat M.B. Pedersen, L. Skov, T. Menn´e, J.D. Johansen, J. Olsen, Gene expression time course in the human skin during elicitation of allergic contact dermatitis. J. Invest. Dermatol. 127(11), 2585–2595 (2007)CrossRef M.B. Pedersen, L. Skov, T. Menn´e, J.D. Johansen, J. Olsen, Gene expression time course in the human skin during elicitation of allergic contact dermatitis. J. Invest. Dermatol. 127(11), 2585–2595 (2007)CrossRef
64.
Zurück zum Zitat G. Hughes, On the mean accuracy of statistical pattern recognizers. IEEE Trans. Inf. Theory 14(1), 55–63 (1968)CrossRef G. Hughes, On the mean accuracy of statistical pattern recognizers. IEEE Trans. Inf. Theory 14(1), 55–63 (1968)CrossRef
65.
Zurück zum Zitat E. Keogh, A. Mueen, in Curse of Dimensionality, ed. by C. Sammut, G.I. Webb (Springer, Berlin, 2010), pp. 257–258 E. Keogh, A. Mueen, in Curse of Dimensionality, ed. by C. Sammut, G.I. Webb (Springer, Berlin, 2010), pp. 257–258
66.
Zurück zum Zitat M.C. Alonso, J.A. Malpica, A.M. de Agirre, Consequences of the hughes phenomenon on some classification techniques, in ASPRS 2011 Annual Conference, Milwaukee, Wisconsin, May 2011, pp. 1–5 M.C. Alonso, J.A. Malpica, A.M. de Agirre, Consequences of the hughes phenomenon on some classification techniques, in ASPRS 2011 Annual Conference, Milwaukee, Wisconsin, May 2011, pp. 1–5
70.
Zurück zum Zitat E. Rendo´n-Huerta, F. Teresa, G.M. Teresa, G.-S. Xochitl, A.F. Georgina, Z.-Z. Veronica, L.F. Montan˜o, Distribution and expression pattern of claudins 6, 7, and 9 in diffuse-and intestinal-type gastric adenocarcinomas. J. Gastrointest. Cancer 41(1), 52–59 (2010)CrossRef E. Rendo´n-Huerta, F. Teresa, G.M. Teresa, G.-S. Xochitl, A.F. Georgina, Z.-Z. Veronica, L.F. Montan˜o, Distribution and expression pattern of claudins 6, 7, and 9 in diffuse-and intestinal-type gastric adenocarcinomas. J. Gastrointest. Cancer 41(1), 52–59 (2010)CrossRef
71.
Zurück zum Zitat A. Rizzi, E. Nucera, L. Laterza, E. Gaetani, V. Valenza, G.M. Corbo, R. Inchingolo, A. Buonomo, D. Schiavino, A. Gasbarrini, Irritable bowel syndrome and nickel allergy: what is the role of the low nickel diet? J. Neurogastroenterol. Motility 23(1), 101 (2017)CrossRef A. Rizzi, E. Nucera, L. Laterza, E. Gaetani, V. Valenza, G.M. Corbo, R. Inchingolo, A. Buonomo, D. Schiavino, A. Gasbarrini, Irritable bowel syndrome and nickel allergy: what is the role of the low nickel diet? J. Neurogastroenterol. Motility 23(1), 101 (2017)CrossRef
72.
Zurück zum Zitat M. Radovic, M. Ghalwash, N. Filipovic, Z. Obradovic, Minimum redundancy maximum relevance feature selection approach for temporal gene expression data. BMC Bioinform. 18(1), 9 (2017) M. Radovic, M. Ghalwash, N. Filipovic, Z. Obradovic, Minimum redundancy maximum relevance feature selection approach for temporal gene expression data. BMC Bioinform. 18(1), 9 (2017)
Metadaten
Titel
Computational Modelling and Pattern Recognition in Bioinformatics
verfasst von
Nikola K. Kasabov
Copyright-Jahr
2019
Verlag
Springer Berlin Heidelberg
DOI
https://doi.org/10.1007/978-3-662-57715-8_15