Artificial intelligence

Scientific journal

ISSN 2710-1673

ONLINE: ISSN 2710-1681

Select your language


The estimation of the complex biological data processing based on the entropy criteria

Babichev S.1, Lytvynenko V.2, Taif M.2, Fefelov A.2
1 Jan Evangelista Purkyne University
2 Kherson State Maritime Academy

Full text (PDF)

UDC: 004.048
Publication Language: Ukrainian
Stuc. intelekt. 2016; 21(2):7-17

Abstract: The paper presents the system toestimatethe complex biological data qualityprocessing by the Shannon entropy criteria use. The compare analysis of the various methods of the Shannon entropy calculation by the use of the model signals with different levels of noise-to-signal ratiowere carried out during the simulation process. The paper presents also the multi-step algorithm of DNA microarray processing where the estimation of the processing quality at the each step is carried out by the average of the Shannon entropy for all objects of database.

Keywords: Shannon entropy, gene expression, DNA MicroArray, filtration

References:

  1. Shannon C. E. А mathematical theory of communication.: Bell System Technical Journal. – 1948. – V. 27. – P. 379-423, 623-656.
  2. Chumak O.V. Jentropiiifraktaly v analizedannyh. M.: Izhevsk, NIC «Reguljarnajaihaoticheskajadinamika». – 2011. – 164 s. (RUS)
  3. StinsonD.R. Cryptography. Theory and Practice. Chapman&Hall/CRC, 2006. – 611 p.
  4. YeoG. BurgeC.M. Maximum entropy modeling of short sequence motifs with applications to RNA splicing signals // Computational biology, 2004. – №11. – P. 377-471.
  5. Archer E., Park I.M., Pillow J.W. Bayesian Entropy Estimation for Countable Discrete Distribution // Journal of Machine Learning Research, 2014. – P. 2833-2868.
  6. Gibbs Dzh.V. Termodinamika. Statisticheskaja mehanika: Izbrannye trudy. M.: Nauka, 1982. – 584s.
  7. Hartli R. V. L. Peredacha informacii / Teorija informacii i ejo prilozhenija / per. s angl. / pod red. Harkevicha. M.: Fizmatgiz, 1959.(RUS)
  8. Kolmogorov A. N. Ob jentropii na edinicu vremeni kak metricheskom invariante metamorfizmov. DAN SSSR. – 1959. – T. 124. – S. 754-755.(RUS)
  9. Renyi A. On measures of entropy and information // Proc. Fourth Berkeley Symposium. Berkeley, LosAngeles: University of California Press, 1961. – Vol. 1. – P. 547-561.
  10. Tsallis C.Possible Generalization of Boltzmann-Gibbs-Statistics//J.Stat.Phys., 1988. – Vol. 52. –P. 479-487.
  11. Von Neumann J., Mathematische Grundlagen der Quantenmechanik, Springer, Berlin, 1932.
  12. Babichev S.A., Didyk A.A., Litvinenko V.I., Fefelov A.A., Shkurdoda S.V. Fil'tracijahromatogramm s pomoshh'juvejvlet-analiza s ispol'zovaniemkriterijajentropii // Sistemnyetehnologii. – Dnepropetrovsk, 2011. – № 6(77). – S. 117-131.(RUS)
  13. Lazarev V.L. Issledovanie sistemna osnov ejentropijnyhi informacionnyh harakteristik // Zhurnal tehnicheskoj fiziki, 2010. – T. 80, vyp. 2. – S. 1-7.(RUS)
  14. Beer D.G., Kardia S.L., Huang C.C., and all. Gene-expression profiles predict survival of patients with lung adenocarcinoma // Nature Medicine,2002. – №8(8). – P. 816-824.
  15. Affymetrix. Statistical Algorithms Description Document // Affymetrix, Inc., Santa Clara, CA, 2002. – P. 1-27.

View full text (PDF)