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Home Archive 2018 № 3 INFORMATION COMPUTER TECHNOLOGIES FOR USING IN BIOTECHNOLOGY: ELECTRONIC MEDICAL INFORMATION SYSTEMS O. M. Klyuchko
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ISSN 2410-7751 (Print)
ISSN 2410-776X (Online)

"Biotechnologia Acta" V. 11, No 3, 2018
https://doi.org/10.15407/biotech11.03.005
Р. 5-26, Bibliography 148, English
Universal Decimal Classification: 004:591.5:612:616-006

INFORMATION COMPUTER TECHNOLOGIES FOR USING IN BIOTECHNOLOGY: ELECTRONIC MEDICAL INFORMATION SYSTEMS

O. M. Klyuchko

Kavetsky Institute of Experimental Pathology, Oncology and Radiobiology of the National Academy of Sciences of Ukraine, Kyiv

The aim of the work was to generalize and analyze the use of electronic information systems in biotechnology in order to create new versions of these systems. The publications concerning the systems of different types for solving the problems in biotechnology were studied. Similar systems which traditionally belong to biology and medicine were classified. The prospects of their application for development of more advanced electronic systems were considered.

Key words: bioinformatics, electronic information systems, databases.

© Palladin Institute of Biochemistry of National Academy of Sciences of Ukraine, 2018

  • References
    • 1. Klyuchko O. M. Information and computer technologies in biology and medicine. Kyiv: NAU-druk. 2008, 252 p. (In Ukrainian).

      2. Klyuchko O. M., Shutko V. N., Navrotskyi D. O., Mikolushko A. M. The set of program models for ecological monitoring technical system based on principles of biophysics. Кyiv (Ukraine), Publ. «Osvita Ukraini», Electronics and Control Systems. 2014, 4 (42), 135–142.

      3. Klyuchko O. М. On the mathematical methods in biology and medicine. Biotechnol. acta. 2017, 10 (3), 31–40. https://doi.org/10.15407/biotech10.03

      4. Klyuchko O. М. Application of artificial neural networks method in biotechnology. Biotechnol. acta. 2017, 10 (4), 5–13. https://doi.org/10.15407/biotech10.04.005

      5. Klyuchko O. М. Cluster analysis in biotechnology. Biotechnol. acta. 2017, 10 (5), 5–18. https://doi.org/10.15407/biotech10.05.005

      6. Klyuchko O. М. Technologies of brain images processing. Biotechnol. acta. 2017, 10 (6), 5–17. https://doi.org/10.15407/biotech10.05.005

      7. Klyuchko O. М., Onopchuk Yu. M. Some trends in mathematical modeling for biotechnology. Biotechnol. acta. 2018, 11 (1), 39–57.

      8. Klyuchko O. М. Electronic information systems in biotechnology. Biotechnol. acta. 2018, 11 (2), 5–22. https://doi.org/10.15407/biotech11.02.005

      9. Gordeev L. S. Mathematical Modeling in Chemical Engineering and Biotechnology. Theor. Found. Chem. Engin. 2014, 48 (3), 225–229. https://link.springer.com/article/10.1134/S0040579514030099.

      10. Piatigorsky B. Ya., Zaitman G. A., Cherkassky V. L., Chinarov B. A. Automatic electrophysio logical experiment. Kyiv: Nauk. dumka. 1985, 216 p. (In Russian).

      11. Rana B. K., Insel P. A. G-protein-coupled receptor websites. Trend. Pharmacol. Sci. 2002, 23 (11), 535–536.  http://dx.doi.org/10.1016/S0165-6147(02) 02113-2.

      12. Akaike N., Kawai N., Kiskin N. I., Krishtal O. A., Tsyndrenko A. Ya., Klyuchko O. M. Spider toxin blocks excitatory amino acid responses in isolated hippocampal pyramidal neurons. Neurosci. Lett. 1987, V. 79, P. 326–330.

      13. Sigworth F. Single channel registration. Moskva: Mir. 1987, 448 p. (In Russian).

      14. Kostyuk P. G. Mechanisms of electrical excitability of nerve cells. Moskva: Nauka. 1981, 208 p. (In Russian).

      15. Seredenko M., Gonchar O., Klyuchko O., Oliynyk S. Peculiarities of prooxidant – antioxidant balance of organism under hypoxia of different genesis and its corrections by new pharmacological preparations. Acta Physiologica Hungarica. Budapest (Hungary). 2002, 89 (1–3), 292.

      16. Klyuchko O. M., Kiskin N. I., Krishtal O. A., Tsyndrenko A. Ya. Araneidae toxins as antagonists of excitatory amino acid responses in isolated hippocampal neurons. X School on biophysics of membrane transport. Szczyrk (Poland). 1990, 2, 271.

      17. Trinus K. F., Klyuchko E. M. Mediators influence on motoneurons retrogradly marked by primulin. Physiol. J. 1984. 30 (6), 730–733. (In Russian).

      18. Aralova N. I., Klyuchko O. M., Mashkin V. I., Mashkina I. V. Algorithmic and program support for optimization of interval hypoxic training modes selection of pilots. Electr. Contr. Syst. 2017, 2 (52), 85–93.

      19. Aralova N. I., Klyuchko O. M., Mashkin V. I., Mashkina I. V. Mathematic and program models for investigation of reliability of operator professional activity in “Human-Machine” systems. Electr. Contr. Syst. 2017, 1 (51), 105–113.

      20. Aralova N. I., Klyuchko O. M., Mashkin V. I., Mashkina I. V. Mathematical model for research of organism restoring for operators of continuously interacted systems. Electr. Contr. Syst. 2016, 3 (49), 100–105.

      21. Aralova N. I., Klyuchko O. M., Mashkin V. I., Mashkina I. V. Investigation of reliability of operators work at fluctuating temperature conditions. Electr. Contr. Syst. 2016, 2 (48), 132–139.

      22. Plakhotnij S. A., Klyuchko O. M., Krotinova M. V. Information support for automatic industrial environment monitoring systems. Electr. Contr. Syst. 2016, 1 (47), 19–34.

      23. Onopchuk Yu. M., Aralova N. I., Klyuchko O. M., Beloshitsky P. V. Mathematic models and integral estimation of organism systems reliability in extreme conditions. Electr. Contr. Syst. 2015, 4 (46), 109–115.

      24. Onopchuk Yu. M., Aralova N. I., Klyuchko O. M., Beloshitsky P. V. Integral estimations of human reliability and working capacity in sports wrestling. J. Engin. Acad. 2015, 3, P. 145–148. (In Russian).

      25. Klyuchko O. M., Shutko V. N., Navrotskyi D. O., Mikolushko A. M. The set of program models for ecological monitoring technical system based on principles of biophysics. Electr. Contr. Syst. 2014, 4 (42), 135–142.

      26. Klyuchko O. M., Sheremet D. Yu. Computer simulation of biological nanogenerator functions. Electr. Contr. Syst. 2014, 2 (40), 103–111.

      27. Klyuchko O. M., Shutko V. N. Computer modeling of auto-oscillating phenomena in neuron complexes. Electr. Contr. Syst. 2014, 1 (39), 127–132.

      28. Klyuchko O. M., Sheremet D. Yu. Computer modeling of biologic voltage-activated nanostructures. Electr. Contr. Syst. 2014, 1 (39), 133–139.

      29. Beloshitsky P. V., Klyuchko O. M., Onopchuk Yu. M. Radiation damage of organism and its correction in conditions of adaptation to highmountain meteorological factors. Bulletin of NAU. 2010, No 1, Р. 224–231. (In Ukrainian).

      30. Beloshitsky P. V., Klyuchko O. M., Onopchuk Yu. М., Makarenko M. V. Estimation of psycho-physiological functions of a person and operator work in extreme conditions. Bulletin of NAU. 2009, No 3, P. 96–104. (In Ukrainian).

      31. Beloshitsky P. V., Klyuchko O. M., Onopchuk Yu. М., Kolchinska A. Z. Results of research of higher nervous activity problems by Ukrainian scientists in Prielbrussie. Bulletin of NAU. 2009, No 2, P. 105–112. (In Ukrainian).

      32. Beloshitsky P. V., Klyuchko O. M., Onopchuk Yu. М. Results of research of structural and functional interdependencies by Ukrainian scientists in Prielbrussie. Bulletin of NAU. 2009, No 1, P. 61–67. (In Ukrainian).

      33. Beloshitsky P. V., Klyuchko O. M., Onopchuk Yu. М. Results of research of highlands factors influence on health and longevity by Ukrainian scientists in Prielbrussie. Bulletin of NAU. 2008, No 4, P. 108–117. (In Ukrainian).

      34. Onopchuk Yu. M., Klyuchko O. M., Beloshitsky P. V. Development of mathematical models basing on researches of Ukrainian scientists at Elbrus. Bulletin of NAU. 2008, No 3, P. 146–155. (In Ukrainian).

      35. Beloshitsky P. V., Klyuchko O. M., Onopchuk Yu. М. Results of research of adaptation problems by Ukrainian scientists in Prielbrussie. Bulletin of NAU. 2008, No 1, P. 102–108. (In Ukrainian).

      36. Beloshitsky P. V., Klyuchko O. M., Onopchuk Yu. М. Results of research of hypoxia problems by Ukrainian scientists in Elbrus region. Bulletin of NAU. 2007, No 3–4, P. 44–50. (In Ukrainian).

      37. Beloshitsky P. V., Klyuchko O. M., Onopchuk Yu. М. Results of medical and biological research of Ukrainian scientists at Elbrus. Bulletin of NAU. 2007, No 2, P. 0–16. (In Ukrainian).

      38. Belan P. V, Gerasimenko O. V., Tepikin A. V., Petersen O. H. Localization of Ca++ extrusion sites in pancreatic acinar cells. J. Biol. Chem. 1996, V. 271, P. 7615–7619.

      39. Belan P., Gardner J., Gerasimenko O. Extracellular Ca++ spikes due to secretory events in salivary gland cells. J. Biol. Chem. 1998, V. 273, P. 4106–4111.

      40. Jabs R., Pivneva T., Huttmann K. Synaptic transmission onto hyppocampal glial cells with hGFAP promoter activity. J. Cell Sci. 2005, V. 118, P. 3791–3803.

      41. Gavrilovich M. Spectra image processing and application in biotechnology and pathology. Dissertation for Ph.D. Acta Universitatis Upsaliensis. Upsala. 2011, 63 p.

      42. Perner P., Salvetti O. Advances in Mass Data Analysis of Images and Signals in Medicine, Biotechnology, Chemistry and Food Industry. Third International Conference, Leipzig, (Germany): Springer, 2008, Proceedings. 2008, 173 p.

      43. Baert P., Meesen G., De Schynkel S., Poffijn A., Oostveldt P. V. Simultaneous in situ profiling of DNA lesion endpoints based on image cytometry and a single cell database approach. Micron. 2005, 36 (4), 321–330. https://doi.org/10.1016/j.micron.2005.01.005

      44. Berks G., Ghassemi A., von Keyserlingk D. G. Spatial registration of digital brain atlases based on fuzzy set theory. Comp. Med. Imag. Graph. 2001, 25 (1), 1–10. https://doi.org/10.1016/S0895-6111(00)00038-0

      45. Nowinski W. L., Belov D. The Cerefy Neuroradiology Atlas: a Talairach–Tournoux atlas-based tool for analysis of neuroimages available over the internet. NeuroImage. 2003, 20(1), 50–57. https://doi.org/10.1016/S1053-8119(03)00252-0

      46. Chaplot S., Patnaik L. M., Jagannathan N. R. Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network. Biomed. Signal Process. Control. 2006, 1 (1), 86–92. https://doi.org/10.1016/j.bspc.2006.05.002

      47. Kovalev V. A., Petrou M., Suckling J. Detection of structural differences between the brains of schizophrenic patients and controls. Psychiatry Research: Neuroimaging. 2003, 124 (3), 177–189. https://doi.org/10.1016/S0925-4927(03) 00070-2.

      48. Ara jo T. Classification of breast cancer histology images using Convolutional Neural Networks. PloS One. 2017, 12 (6), e0177544.

      49. Vecht-Lifshitz S. E., Ison A. P. Biotechnological applications of image analysis: present and future prospects. J. Biotechnol. 1992, 23 (1), 1–18.

      50. Toga A. W., Thompson P. M. The role of image registration in brain mapping. Image Vis. Comput. 2001, 19 (1–2), 3–24.

      51. Carro S. A., Scharcanski J. A framework for medical visual information exchange on the WEB. Comput. Biol. Med. 2006, V. 4, P. 327–338.

      52. Chakravarty M. M., Bertrand G., Hodge C. P., Sadikot A. F., Collins D. L. The creation of a brain atlas for image guided neurosurgery using serial histological data. NeuroImage. 2006, 30 (2), 359–376. https://doi.org/10.1016/j.neuroimage.2005.09.041

      53. Dikshit A., Wu D., Wu C., Zhao W. An online interactive simulation system for medical imaging education. Comp. Med. Imag. Graph. 2005, 29 (6), 395404. https://doi.org/10.1016/j.compmedimag.2005.02.001

      54. Singh R., Schwarz N., Taesombut N., Lee D., Jeong B., Renambot L., Lin A. W., West R., Otsuka H., Naito S., Peltier S. T., Martone M. E., Nozaki K., Leigh J., EllismanM.H. Real-time multi-scale brain data acquisition, assembly, and analysis using an end-to-end. OptIPuter Fut. Gener. Comp. Syst. 2006, 22, 1032–1039.

      55. Stefanescu R., Pennec X., Ayache N. Grid powered nonlinear image registration with locally adaptive regularization. Med. Image Anal. 2004. 8 (3), 325–342.

      56. Ma Y., Hof P. R., Grant S. C., Blackband S. J., Bennett R., Slatest L., McGuigan M. D., Benveniste H. A three-dimensional digital atlas database of the adult C57BL/6J mouse brain by magnetic resonance microscopy. Neuroscience. 2005, 135 (4), 12031215. https://doi.org/10.1016/j.neuroscience.2005.07.014

      57. Yu-Len Huang. Computer-aided Diagnosis Using Neural Networks and Support Vector Machines for Breast Ultrasonography. J. Med. Ultrasound. 2009,17 (1), 17–24.

      58. Prachi Damodhar Shahare, Ram Nivas Giri. Comparative Analysis of Artificial Neural Network and Support Vector Machine Classification for Breast Cancer Detection. Int. Res. J. Engin. Technol. (IRJET). 2015, 2 (9).

      59. Natrajan R., Sailem H., Mardakheh F. K., Garcia M. F., Tape C. G., Dowsett M., Bakal C., Yuan Y. Microenvironmental heterogeneity parallels breast cancer progression: a histology–genomic integration analysis. PLoS Med. 2016. 13(2), e1001961.https://doi.org/10.1371/journal.pmed.1001961

      60. Klyuchko O. M. Brain images in informa tion systems for neurosurgery and neurophysiology. Electronics and control systems. 2009, 3(21), 152–156. (In Ukrainian).

      61. Klyuchko O. M. Using of images’ databases for diagnostics of pathological changes in organism tissues. Electr.Contr. Syst. 2009, 2 (20), 62–68. (In Ukrainian).

      62. Klyuchko O. M. Elements of different level organization of the brain as material for electronic databases with images. Electr. Contr. Syst. 2009, 1 (19), 69–75. (In Ukrainian).

      63. Steimann F. On the representation of roles in object-oriented and conceptual modelling. Data& Knowedge Engineering. 2000, 35 (1), 83–106.

      64. Klyuchko O. M., Managadze Yu. L., Pashkivsky A. O. Program models of 2D neuronal matrix for ecological monitoring and images’ coding. Bulletin of the Engineering Academy. 2013, No 3–4, P. 77–82. (In Ukrainian).

      65. Klyuchko O. M., Piatchanina T. V., Mazur M. G. Combined use of relation databases of images for diagnostics, therapy and prognosis of oncology diseases. “Integrated robototechnic complexes”. Х ІІRTC-2017 Conference Proceedings. P. 75–276. (In Ukrainian).

      66. Aralova N. I., Klyuchko O. M., Mashkin V. I., Mashkina I. V. Algorithms for data models processing for integral estimation of flight crews’ personnel states. Electr.Contr. Syst. 2018, 1 (55), 80–86.

      67. Shutko V. M, Shutko O. M., Kolganova O. O. Methods and means of compression of information. Kyiv: Nauk. dumka. 2012, 168 p. (In Ukrainian).

      68. Iakovidis D. K., Maroulis D. E., Karkanis S. A. Texture multichannel measurements for cancer precursors’ identification using support vector machines. Measurement. 2004, V. 36, P. 297–313. 2004.09.010 https://doi.org/10.1016/j.measurement.

      69. Nguyen H. Q., Carrieri-Kohlman V., Rankin S. H., Slaughter R, Stulbarg M. S. Internet-based patient education and support interventions: a review of evaluation studies and directions for future research. Comp. Biol. Med. 2004, 34 (2), 95–112. https://doi.org/10.1016/S0010- 4825(03)00046-5.

      70. Jézéquel P., Loussouarn L., Guérin-Charbonnel C., Campion L., Vanier A., Gouraud W., Lasla H., Guette C., Valo I., Verrièle V. Campone M. Gene-expression molecular subtyping of triple-negative breast cancer tumours: importance of immune response. Breast Cancer Res. 2015, 17 (1), 43. https://doi.org/10.1186/s13058-015-0550-y

      71. Bozhenko V. K. Multivariable analysis of laboratory blood parameters for obtaining diagnostic information in experimental and clinical oncology. The dissertation author’s abstract on scientific degree editions. Dc. Med. Study. Moscow. 2004. (In Russian).

      72. Ko J. H., Ko E. A., Gu W., Lim I., Bang H., Zhou T. Expression profiling of ion channel genes predicts clinical outcome in breast cancer. Mol. Cancer. 2013, 12 (1), 106. https://doi.org/10.1186/1476-4598-12-106

      73. Kawai M., Nakashima A., Kamada S., Kikkawa U. Midostaurin preferentially attenuates proliferation of triple-negative breast cancer cell lines through inhibition of Aurora kinase family. J. Biomed. Sci. 2015, 22 (1), 48. https://doi.org/10.1186/s12929-015-0150-2

      74. Uhr K., Wendy J. C., Prager-van der Smissen, Anouk A. J. Heine, Bahar Ozturk, Marcel Smid, Hinrich W. H. Ghlmann, Agnes Jager, John A. Foekens, John W. M. Martens. Understanding drugs in breast cancer through drug sensitivity screening. SpringerPlus. 2015, 4 (1), 611. https://doi.org/10.1186/s40064-015-1406-8

      75. Onopchuk Yu. M., Biloshitsky P. V., Klyuchko O. M. Development of mathematical models based on the results of researches of Ukrainian scientists at Elbrus. Bulletin of NAU. 2008, No 3, P. 146–155. (In Ukrainian).

      76. Ankur Poudel, Dhruba Bahadur Thapa, Manoj Sapkota. Cluster Analysis of Wheat (Triticum aestivum L.) Genotypes Based Upon Response to Terminal Heat Stress. Int. J. Appl. Sci. Biotechnol. 2017, 5 (2), 188–193. https://doi.org/10.3126/ijasbt.v5i2.17614

      77. Zaslavsky L., Ciufo S., Fedorov B., Tatusova T. Clustering analysis of proteins from microbial genomes at multiple levels of resolution. BMC Bioinform. 2016, 17 (8), 276. Published online 2016 Aug 31. https://doi.org/10.1186/s12859-016-1112-8

      78. Zhou J., Richardson A. J., Rudd K. E. EcoGene-RefSeq: EcoGene tools applied to the RefSeq prokaryotic genomes. Bioinformatics. 2013, 29 (15), 1917–1918. Published: 04 June 2013. https://doi.org/10.1093/bioinformatics/btt302

      79. Zhang J., Sun J., Yang Y. Web-based electronic patient records for collaborative medical applications. Comput. Med. Imag. Graph. 2005, 29 (2–3), 115–124. https://doi.org/10.1016/j.compmedimag.2004.09.005

      80. Tatusova T., Zaslavsky L., Fedorov B., Haddad D., Vatsan A., Ako-adjei D., Blinkova O., Ghazal H. Protein Clusters. The NCBI Handbook [Internet]. 2nd edition. Available at https://www.ncbi.nlm.nih.gov/books/NBK242632.

      81. Anderson J. G. Evaluation in health informatics: computer simulation. Comput. Biol. Med. 2002, 32 (3), 151–164. https://doi.org/10.1016/S0010-4825(02)00012-4

      82. Aruna P., Puviarasan N., Palaniappan B. An investigation of neuro-fuzzy systems in psychosomatic disorders. Exp. Syst. Appl. 2005, 28 (4), 673–679. https://doi.org/10.1016/j.eswa.2004.12.024

      83. Beaulieu A. From brainbank to database: the informational turn in the study of the brain. Stud. Hist. Phil. Biol. Biomed. Sci. 2004, V. 35, P. 367–390. https://doi.org/10.1016/j.shpsc.2004.03.011

      84. Bedathur S. J., Haritsa J. R., Sen U. S. The building of BODHI, a bio-diversity database system. Inform. Syst. 2003, 28 (4), 347–367. https://doi.org/10.1016/S0306-4379(02)00073-X

      85. Braxton S. M., Onstad D. W., Dockter D. E., Giordano R., Larsson R., Humber R. A. Description and analysis of two internetbased databases of insect pathogens: EDWIP and VIDIL. J. Invertebr. Pathol. 2003, 83 (3), 185–195.https://doi.org//10.1016/S0022-2011(03)00089-2.

      86. Budura A., Cudré-Mauroux P., Aberer K. From bioinformatic web portals to semantically integrated Data Grid networks. Future Generation Computer Systems. 2007, 23 (3), 281–522. https://doi.org/10.1016/j.jenvman.2004.08.017

      87. Burns G., Stephan K. E., Ludäscher B., Gupta A., Kötter R. Towards a federated neuroscientific knowledge management system using brain atlases. Neurocomputing. 2001, V. 3840, P. 1633–1641. https://doi.org/10.1016/S0925-2312(01)00520-3

      88. Butenko S., Wilhelm W. E. Clique-detection models in computational biochemistry and genomics. Eur. J. Oper. Res. 2006, 173 (1), 117. https://doi.org/10.1016/j.ejor.2005.05.026

      89. Carro S. A., Scharcanski J. Framework for medical visual information exchange on the WEB. Comp. Biol. Med. 2006, 36 (4), 327–338. https://doi.org/10.1016/j.compbiomed.2004.10.004 .

      90. Chau M., Huang Z., Qin J., Zhou Y., Chen H. Building a scientific knowledge web portal: The NanoPort experience. Dec. Supp. Syst. 2006. https://doi.org/10.1016/j.dss.2006.01.004

      91. Chen M., Hofest dt R. A medical bioinforma tics approach for metabolic disorders: Biomedical data prediction, modeling, and systematic analysis. J. Biomed. Inform. 2006, 39 (2), 147–159. https://doi.org/10.1016/j.jbi.2005.05.005

      92. Chli M., De Wilde P. Internet search: Subdivision-based interactive query expansion and the soft semantic web. Appl. Soft Comput. 2006. https://doi.org/10.1016/j.asoc.2005.11.003

      93. Despont-Gros C., Mueller H., Lovis C. Evaluating user interactions with clinical information systems: A model based on human– computer interaction models. J. Biomed. Inform. 2005, 38 (3), 244–255. https://doi.org/10.1016/j.jbi.2004.12.004

      94. Sun W., Starly B., Nam J., Darling A. BioCAD modeling and its applications in computeraided tissue engineering. Computer–Aided Design. 2005, 37 (11), 1097–1114. https://doi.org/10.1016/j.cad.2005.02.002

      95. Marios D., Dikaiakos M. D. Intermediary infrastructures for the World Wide Web. Comp. Networks. 2004, V. 45, P. 421–447. https://doi.org/10.1016/j.comnet.2004.02.008.

      96. Dimitrov S. D., Mekenyan O. G, Sinks G. D., Schultz T. W. Global modeling of narcotic chemicals: ciliate and fish toxicity. J. Mol. Struc.: Theochem. 2003, 622 (12), 63–70. https://doi.org/10.1016/S0166-1280(02)00618-8

      97. Yan H., Y. Jiang, J. Zheng. The internet-based knowledge acquisition and management method to construct large-scale distributed medical expert systems. Comp. Meth. Progr. Biomed. 2004, V. 74 (1), P. 1–10. https://doi.org/10.1016/S0169-2607(03)00076-2

      98. Duan Y., Edwards J. S., Xu M. X. Web-based expert systems: benefits and challenges. Inf. Manag. 2005, 42 (6), 799811 https://doi.org/10.1016/j.im.2004.08.005

      99. Essen van D. C. Windows on the brain: the emerging role of atlases and databases in neuroscience. Curr. Opin. Neurobiol. 2002, 12 (5), 574–579. https://doi.org/10.1016/S0959-4388(02)00361-6

      100. Fellbaum C., Hahn U., Smith B. Towards new information resources for public health From Word Net to Medical Word Net. J. Biomed. Inform. 2006, 39 (3), 321–332. https://doi.org/10.1016/j.jbi.2005.09.004

      101. Ferraris M., Frixione P., Squarcia S. Network oriented radiological and medical archive. Comp. Physics Commun. 2001, V. 140, P. 226–232. https://doi.org/10.1016/S0010-4655(01)00273-9

      102. Flower D. R., Attwood T. K. Integrative bioinformatics for functional genome annotation: trawling for G protein-coupled receptors. Reviews 55 Semin. Cell. Dev. Biol. 2004, 15 (6), 693–701. https://doi.org/10.1016/j.semcdb.2004.09.008

      103. Fink E., Kokku P. K., Nikiforou S., Hall L. O., Goldgof D. B, Krischer J. P. Selection of patients for clinical trials: an interactive webbased system. Art. Intell. Med. 2004, 31 (3), 241–254. https://doi.org/10.1016/j.artmed.2004.01.017

      104. Suzuki I., K. Yamada, T. Yamakawa. Delivery of medical multimedia contents through the TCP/IP network using RealSystem. Comput. Meth. Progr. Biomed. 2003, 70 (3), 253–258. https://doi.org/10.1016/S0169-2607(02)00012-3

      105. Gaulton A., Attwood T. K. Bioinformatics approaches for the classification of G-protein-coupled receptors. Curr. Opin. Pharmacol. 2003, 3 (2), 114–120. https://doi.org/10.1016/S1471-4892(03)00005-5

      106. Palla K., Ghahramani Z., Knowles D. A. A nonparametric variable clustering model. Adv. Neural Inform. Proc. Syst. 2012, P. 2987–2995.

      107. Goldys E. M. Fluorescence Applications in Biotechnology and the Life Sciences. USA: John Wiley & Sons. 2009, 367 p.

      108. Hirano S., Sun X., Tsumoto S. Comparison of clustering methods for clinical databases. Inform. Sci. 2004, 159 (34), P. 155–165. https://doi.org/10.1016/j.ins.2003.03.011

      109. Hong Yu., Hatzivassiloglou V., Rzhetsky A., Wilbur W. J. Automatically identifying gene/ protein terms in MEDLINE abstracts. J. Biomed. Inform. 2002, 35 (56), 322–330. https://doi.org/10.1016/S1532-0464(03)00032-7

      110. Horn W. AI in medicine on its way from knowledgeintensive to data-intensive systems. Art. Intell. Med. Elsevier. 2001, 23 (1), 512. https://doi.org/10.1016/S0933-3657(01)00072-0

      111. Hsi-Chieh Lee, Szu-Wei Huang, Li E. Y. Mining protein–protein interaction information on the internet. Exp. Syst. Appl. Elsevier. 2006, 30 (1), 142–148. https://doi.org/10.1016/j.eswa.2005.09.083

      112. Young R. Genetic toxicology: web–resources. Toxicology. 2002, 173 (1–2), P. 103–121. https://doi.org/10.1016/S0300-483X(02)00026-4

      113. Johnson S. B., Friedman R. Bridging the gap between biological and clinical informatics in a graduate training program. J. Biomed. Inform. 2007, 40 (1), 59–66. Epub. 2006 Mar 15. https://doi.org/10.1016/j.jbi.2006.02.011

      114. Kaiser M., Hilgetag C. C. Modeling the development of cortical systems networks. Neurocomputing. 2004, V. 5860, P. 297–302. https://doi.org/10.1016/j.neucom.2004.01.059 .

      115. Kane M. D., Brewer J. L. An information technology emphasis in biomedical informatics education. J. Biomed. Inform. 2007, 40 (1), 67–72. https://doi.org/10.1016/j.jbi.2006.02.006

      116. Kannathal N., Acharya U. R., Lim C. M., Sadasivan P. K. Characterization of EEG. A comparative study. Comp. Meth. Progr. Biomed. 2005, 80 (1), 17–23. https://doi.org/10.1016/j.cmpb.2005.06.005

      117. Koh W., McCormick B. H. Brain micro structure database system: an exoskeleton to 3D reconstruction and modeling. Neurocomputing. 2002, V. 4446, P. 1099–1105. https://doi.org/10.1016/S0925-2312(02)00426-5

      118. Koh W., McCormick B. H. Registration of a 3D mouse brain atlas with brain microstructure data. Neurocomputing. 2003, V. 5254, P. 307–312. ttps://doi.org/10.1016/S0925-2312(02)00793-2

      119. Li Q., Wu Y. Identifying important concepts from medical documents. J. Biomed. Inform. 2006, 39 (6), 668–679. https://doi.org/10.1016/j.jbi.2006.02.001

      120. Lubitz von D., Wickramasinghe N. Networkcentric healthcare and bioinformatics: Unified operations within three domains of knowledge. Exp. Syst. Appl. 2006, 30 (1), 11–23. https://doi.org/10.1016/j.eswa.2005.09.069

      121. Martin-Sanchez F., Iakovidis I., Norager S., Maojo V., de Groen P., Van der Lei J., Jones T., Abraham-Fuchs K., Apweiler R., Babic A., Baud R., Breton V., Cinquin P., Doupi P., Dugas M., Eils R., Engelbrecht R., Ghazal P., Jehenson P., Kulikowski C., Lampe K., De Moor G., Orphanoudakis S., Rossing N., Sara chan B., Sousa A., Spekowius G., Thireos G., Zahlmann G., Zvárová J., Hermosilla I., Vicente F. J. Synergy between medical informatics and bioinformatics: facilitating genomic medicine for future health care. J. Biomed. Inform. 2004, 37 (1), 30–42. https://doi.org/10.1016/j.jbi.2003.09.003.

      122. Masseroli M., Visconti A., Bano S. G. Pinciroli F. He@lthCo-op: a web-based system to support distributed healthcare co-operative work. Comp. Biol. Med. 2006, 36 (2), 109–127. https://doi.org/10.1016/j.compbiomed.2004.09.005

      123. Moon S., Byun Y., Han K. FSDB: A frameshift signal database. Comp. Biol. Chem. 2007, 31 (4), 298–302. https://doi.org/10.1016/j.compbiolchem.2007.05.004

      124. Orgun B., Vu J. HL7 ontology and mobile agents for interoperability in heterogeneous medical information systems. Comp. Biol. Med. 2006, 36 (78), 817–836. https://doi.org/10.1016/j.compbiomed.2005.04.010

      125. P rez-Rey D., Maojo V., Garc a-Remesal M., Alonso-Calvo R., Billhardt H., Martin-S nchez F., Sousa A. Ontofusion: Ontology-based integration of genomic and clinical databases. Comp. Biol. Med. 2006, 36 (78), 712–730. https://doi.org/10.1016/j.compbiomed.2005.02.004

      126. Krishtal O. A., Kiskin N. I., Tsyndrenko A. Ya., Klyuchko E. M. Pharmacological properties of amino acid receptors in isolated hippocampal neurons. In: Receptors and ion channels. Ed. By Ovchinnikov Y. A., Hucho F. Berlin-New York: Walter de Gruyter. 1987, P. 127–137.

      127. Klyuchko E. M, Klyuchko Z. F., Beloshitsky P. V. Some adaptation characteristics of insects in mountains of Prielbrussie. Nalchik (Russia), “Hypoxia: automatic analysis of hypoxic states of healthy people and sick ones”. 2005, V. 1, P. 137–140. (In Russian).

      128. Klyuchko Z. F., Klyuchko E. M. Analysis of taxonomic structure of moth fauna (Lepidoptera: Noctuidae s.l.) of Ukraine according to monitoring data. Eversmannia, 2012, 3 (33), 41–45. (In Russian).

      129. Klyuchko Z. F., Klyuchko E. M. Moth (Lepidoptera: Noctuidae s. l.) of Chercasska region of Ukraine according to results of many-year monitoring. Eversmannia. 2014, No 37, Р. 32–49. (In Russian).

      130. Gonchar O., Klyuchko E., Mankovskaya I. Role of complex nucleosides in the reversal of oxidative stress and metabolic disorders induced by acute nitrite poisoning. Ind. J. Pharmacol. 2006, 38 (6), 414–418.

      131. Gonchar O., Klyuchko E., Seredenko M., Oliynik S. Corrections of prooxidant —antioxidant homeostasis of organism under hypoxia of different genesis by yackton, new pharmacological preparation. Acta Physiol. Pharmacol. Bulg. 2003, V. 27, P. 53–58.

      132. Klyuchko O., Klyuchko Z., Lizunova A. Electronic Noctuidae database: some problems and solutions. Proceed. 16th European Congress of Lepidopterology. Cluj (Romania), 2009, P. 31–32.

      133. Klyuchko O., Klyuchko Z., Lizunova A. Noctuidae fauna of Ukrainian Karpathy: results of monitoring (1956–2008). Proceed. 16th European Congress of Lepidopterology. Cluj (Romania), 2009, P. 31.

      134. Klyuchko O. M., Beloshitsky P. V. Inves tigation of insect adaptation characteristics in Prielbrussie in 2004–2005. Mater. VIII World Congress of International Society for Adaptive Medicine (ISAM). Moskva (Russia). 2006, Р. 165–166.

      135. Beloshitsky P. V., Klyuchko O. M. Contribution of Sirotinin’s school into adaptation medicine. Mater. VIII World Congress of International Society for Adaptive Medicine (ISAM). Moskva (Russia). 2006, Р. 158.

      136. Klyuchko O. M., Klyuchko Z. F. Ukrainian Noctuidae Database. Mater. XIV SEL Congress. Roma (Italy). 2005, P. 49.

      137. Klyuchko Z. F., Klyuchko O. M. Noctuidae (Lepidoptera) of Donbass, Ukraine. Mater. XIV SEL Congress. Roma (Italy). 2005, P. 41–42.

      138. Beloshitsky P., Klyuchko O., Onopch uck Yu., Onopchuck G. Mathematic model for hypoxic states development for healthy people and ones with ischemic heart disease. High altitude medicine and biology: Mater. ISMM Congress. Beijing (China), 2004, V. 5, P. 251.

      139. Beloshitsky P., Klyuchko O., Kostyuk O., Beloshitsky S. Peculiarities of high mountain factors influence on organism. High altitude medicine and biology: Mater. ISMM Congress. Beijing (China), 2004, V. 5, P. 250.

      140. Gonchar O., Klyuchko O., Beloshitsky P. Ways of myocardial metabolic disorders correction at hypoxia by new pharmacological preparations. High altitude medicine and biology: Mater. ISMM Congress. Beijing (China). 2004, V. 5, P. 249.

      141. Gonchar O., Klyuchko O., Seredenko M., Oliynyk B. Correction of metabolic disorders at hypoxia by new pharmacological preparations. Mater. 3 FEPS Congress. Nice (France). 2003, P. 228.

      142. Klyuchko O. M., Paskivsky A. O., Sheremet D. Y. Computer modeling of some nanoelements for radio and television systems. Electr. Contr. Syst. 2012. 3 (33), 102–107. (In Ukrainian).

      143. Klyuchko O. M., Hayrutdinov R. R. Modeling of electrical signals propagation in neurons and its nanostructures. Electr. Contr. Syst. 2011, 2 (28), 120–124. (In Ukrainian).

      144. Patent 1370136 USSR, МКИ С12N 5/00. The method for dissociation of hippocampal cells. Klyuchko E. М., Tzyndrenko A. Ya. Priority: 31.01.1986; Issued: 30.01.1988, Bull. No 4, 3 p.

      145. Klyuchko O. M., Tzal-Tzalko V. I. Elaboration of new monitoring system for Ukrainian Polissia conditions with data defence. Bull. Engin. Acad. 2014, 2, 239–246. (In Ukrainian).

      146. Klyuchko O. M., Piatchanina T. V., Mazur M. G., Basarak O. V. Ontological methods in the development of biomedical information systems. “Integrated intellec tual robototechnical complexes”–“IIRTC-2018”: Materials of ХІ Intl. Scient. Tech. Conference. 2018, P. 270–272. (In Ukrainian).

      147. Klyuchko O. M., Piatchanina T. V., Mazur M. G., Basarak O. V. Elaboration of network-based biomedical systems with databases. “Integrated intellectual robototechnical complexes” —“IIRTC-2018”. Materials of ХІ Intl. Scient. Tech. Conference. 2018, P. 273–274. (In Ukrainian).

      148. Aralova N. I., Klyuchko O. M. Mathematic modeling of functional self-organization of pilots’ respiratory systems. “Integrated intellectual robototechnical complexes”-“IIRTC-2018”. Materials of ХІ Intl. Scient. Tech. Conference. 2018, P. 268–269. (In Ukrainian).


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