Biotechnologia Acta

...

  • Increase font size
  • Default font size
  • Decrease font size
Home Archive 2017 № 6 TECHNOLOGIES OF BRAIN IMAGES PROCESSING O.M. Klyuchko
Print PDF

ISSN 2410-7751 (Print)
ISSN 2410-776X (Online)

"Biotechnologia Acta" V. 10, No 6, 2017
https://doi.org/10.15407/biotech10.06.005
Р. 5-17 , Bibliography 98 , English
Universal Decimal Classification:  004:591.5:612:616-006

TECHNOLOGIES OF BRAIN IMAGES PROCESSING

O.M. Klyuchko

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

The purpose of present research was to analyze modern methods of processing biological images implemented before storage in databases for biotechnological purposes. The databases further were incorporated into web-based digital systems. Examples of such information systems were described in the work for two levels of biological material organization; databases for storing data of histological analysis and of whole brain were described. Methods of neuroimaging processing for electronic brain atlas were considered. It was shown that certain pathological features can be revealed in histological image processing. Several medical diagnostic techniques (for certain brain pathologies, etc.) as well as a few biotechnological methods are based on such effects. Algorithms of image processing were suggested. Electronic brain atlas was conveniently for professionals in different fields described in details. Approaches of brain atlas elaboration, “composite” scheme for large deformations as well as several methods of mathematic images processing were described as well.

Key words: mathematical methods, biotechnology, image processing methods  information and computer technologies, software, databases.

© Palladin Institute of Biochemistry of National Academy of Sciences of Ukraine, 2017

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

      2. Klyuchko O. М. Cluster analysis in biotechnology. Biotechnol. acta. 2017, 10(5), 5–18.

      https://doi.org/10.15407/biotech10.05.005.

      3. 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.

      4. Klyuchko O. М. On the mathematical methods in biology and medicine. Biotechnol. acta. 2017, 10 (3), 31–40. https://doi.org/10.15407/biotech10.03.031

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

      6. 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.

      7. 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), 321330. https://doi.org/10.1016/j.micron. 2005.01.005.

      8. 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.

      9. 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), 5057. https://doi.org/10.1016/S1053-8119(03)00252-0.

      10. 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), 8692. https://doi.org/ 10.1016/j.bspc.2006.05.002.

      11. 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), 177189. https://doi.org/10.1016/S0925-4927(03) 00070-2.

      12. Araújo T., Aresta G., Castro E., Rouco G., Aguiar P., Eloy, C., Polónia, A., Campilho C. Classification of breast cancer histology images using Convolutional Neural Networks. PloS One. 2017, 12 (6), e0177544. doi: https://dx.doi.org/10.1371/ journal.pone.0177544.

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

      14. Toga A. W., Thompson P. M. The role of image registration in brain mapping. Image Vision Comput. 2001, 19 (1–2), 3–24. Reviews 13

      15. Carro S. A., Scharcanski J. A framework for medical visual information exchange on the WEB. Comput. Biol. Med. 2006, 36 (4), 327–338.

      16. 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), 359376. https://dx.doi.org/10.1016/j.neuroimage. 2005.09.041.

      17. 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.

      18. Singh R., Schwarz N., Taesombut N. Realtime multi-scale brain data acquisition, assembly, and analysis using an end-to-end. OptIPuter Fut. Gener. Comp. Syst. 2006.

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

      20. 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.

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

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

      23. 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.

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

      25. Klyuchko O. M. Using of images’ databases for diagnostics of pathological changes in organism tissues. Electronics and control systems. 2009, 2 (20), 62–68. (In Ukrainian).

      26. Klyuchko O. M. Elements of different level organization of the brain as material for electronic databases with images. Electronics and control systems. 2009, 1 (19), 69–75. (In Ukrainian).

      27. Klyuchko O. M., Shutko V. N., Mikolushko A. M., Navrotskyi D. A. Possibility of images recognition by artificial biotechnical system. 2014 IEEE 3d Intl Conference: MSNMC Proceedings. 2014, P. 165–169.

      28. 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, N 3–4, P. 77–82. (In Ukrainian).

      29. 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. 2017, P. 275–276. (In Ukrainian).

      30. Shutko V. M, Shutko O. M., Kolganova O. O. Methods and means of compression of information. Kyiv: Naukova Dumka. 2012, 168 p. (In Ukrainian).

      31. Jecheva V., Nikolova E. Some clusteringbased methodology applications to anomaly intrusion detection systems. Int. J. Secur. Appl. 2016, 10 (1), 215228. https://doi.org/https://dx.doi.org/10.14257/ijsia.2016.10.1.20.

      32. 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. 297313. https://doi.org/10.1016/j.measurement. 2004.09.010.

      33. 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), 95112. https://doi.org/10.1016/S0010-4825(03)00046-5.

      34. 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.

      35. 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).

      36. 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.

      37. 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.

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

      39. Onopchuk Yu. M., Biloshitsky P. V., Klyuchko O. M. Development of mathematical models based on the results of researches of Ukrainian scientists at Elbrus. Visnyk NAU. 2008, N 3, P. 146155. (In Ukrainian).

      40. 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), 188193. https://dx.doi.org/10.3126/ ijasbt.v5i2.17614.

      41. 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://dx.doi.org/10.1186/s12859-016-1112-8.

      42. 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.

      43. Zhang J., Wu G., Hu X., Li S., Hao S. A Parallel Clustering Algorithm with MPI – MKmeans. J. Comput. 2013, 8 (1), 1017. https://doi.org/10.1109/PAAP. 2011.17.

      44. 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.

      45. Anderson J. G. Evaluation in health informatics: computer simulation. Comp. Biol. Med. 2002, 32 (3), 151164. https://doi.org/10.1016/S0010-4825(02)00012-4.

      46. Aruna P., Puviarasan N., Palaniappan B. An investigation of neuro-fuzzy systems in psychosomatic disorders. Exp. Syst. Appl. 2005, 28 (4), 673679. https://doi.org/10.1016/j.eswa.2004.12.024.

      47. Bange M. P., Deutscher S. A., Larsen D., Linsley D., Whiteside S. A handheld decision support system to facilitate improved insect pest management in Australian cotton systems. Comp. Electron. Agricult. 2004, 43 (2), 131147. https://doi.org/10.1016/j.compag.2003.12.003.

      48. 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.

      49. Bedathur S. J., Haritsa J. R., Sen U. S. The building of BODHI, a bio-diversity database system. Inform. Syst. 2003, 28 (4), 347367. https://doi.org/10.1016/ S0306-4379(02)00073-X.

      50. Brake I. Unifying revisionary taxonomy: insect exemplar groups. Abstr. XV SEL Congr. Berlin (Germany). 2007.

      51. 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), 185195. https://doi.org/10.1016/S0022-2011(03)00089-2

      52. BreauxA., CochraneS., EvensJ., MartindaledM., Pavlike B., Suera L., Benner D. Wetland ecological and compliance assessments in the San Francisco Bay Region, California, USA. J. Environm. Manag. 2005, 74 (3), 217237.

      53. Budura A., PhilippeCudré-Mauroux P., Aberer K. From bioinformatic web portals to semantically integrated Data Gridnetworks. Future Gener. Comp. Syst. 2007, 23 (3), 281522. https://doi.org/10.1016/j.jenvman.2004.08.017.

      54. 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. 16331641. https://doi.org/10.1016/S0925-2312(01)00520-3

      55. 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.

      56. Carro S. A., Scharcanski J. Framework for medical visual information exchange on the WEB. Comp. Biol. Med. 2006, 36 (4), 327338. https://doi.org/ 10.1016/j.compbiomed.2004.10.004.

      57. Chau M., Huang Z., Qin J., Zhou Y., Chen H. Building a scientific knowledge web portal: The

      Reviews NanoPort experience. Dec. Support Syst. 2006. https://doi.org/10.1016/j.dss.2006.01.004.

      58. Chen M., Hofestädt R. A medical bioinformatics approach for metabolic disorders: Biomedical data prediction, modeling, and systematic analysis. J. Biomed. Inform. 2006, 39 (2), 147159. https://doi.org/10.1016/j.jbi.2005.05.005.

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

      60. 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), 244255. https://doi.org/10.1016/ j.jbi.2004.12.004.

      61. 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), 244255. https://doi.org/10.1016/j.jbi.2004.12.004.

      62. Marios D., Dikaiakos M. D. Intermediary infrastructures for the World Wide Web. Comp. Networks. 2004, V. 45, P. 421447. https://doi.org/10.1016/j.comnet.2004.02.008.

      63. 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), 6370. https://doi.org/10.1016/S0166-1280(02)00618-8.

      64. Dong Y., Zhuang Y., Chen K., Tai X. A hierarchical clustering algorithm based on fuzzy graph connectedness. Fuzzy Sets. Syst. 2006, V. 157, P. 1760–1774. https://doi.org/10.1016/j.fss.2006.01.001.

      65. 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.

      66. Essen van D. C. Windows on the brain: the emerging role of atlases and databases in neuroscience. Curr. Opin. Neurobiol. 2002, 12 (5), 574579. https://doi.org/10.1016/S0959-4388(02)00361-6.

      67. 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), 321332. https://doi.org/10.1016/j.jbi.2005.09.004

      68. 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.

      69. Flower D. R., Attwood T. K. Integrative bioinformatics for functional genome annotation: trawling for G protein-coupled receptors. Semin. Cell. Dev. Biol. 2004, 15 (6), 693701. https://doi.org/10.1016/j.semcdb.2004.09.008.

      70. Fink E., Kokku P. K., Nikiforou S., Hall L. O., Goldgof D. B., Krischer J. P. Selection of patients for clinical trials: an interactive web-based system. Art. Intell. Med. 2004, 31 (3), 241254. https://doi.org/10.1016/j.artmed.2004.01.017.

      71. Fitzpatrick M. J., Ben-Shahar Y., Smid H. M., Vet L. E., Robinson G. E., Sokolowski M. B. Candidate genes for behavioural ecology. Trend Ecol. Evol. 2005, 20 (2), 96104. https://doi.org/10.1016/j.tree.2004.11.017.

      72. Fox J., Alabassi A., Patkar V., Rose T., Black E. An ontological approach to modelling tasks and goals. Comp. Biol. Med. 2006, V. 36, P. 837–856. https://doi.org/10.1016/j.compbiomed.2005.04.011.

      73. Fu Zetian, Xu Feng, Zhou Yun, Shuan X. Z. Pig-vet: a web-based expert system for pig disease diagnosis. 2006. https://doi.org/10.1016/j.eswa.2005.01.011.

      74. Gaulton A., Attwood T. K. Bioinformatics approaches for the classification of G-protein-coupled receptors. Curr. Opin. Pharmacol. 2003, 3 (2), 114120. https://doi.org/10.1016/S1471-4892(03)00005-5.

      75. Gevrey M., Worner S., Kasabov N., Pitt J., Giraudel J. L. Estimating risk of events using SOM models: A case study on invasive species establishment. Ecol. Modell. 2006, 197 (34), 361372. https://doi.org/10.1016/jecolmodel. 2006.03.032

      76. Glenisson P., Glänzel W., Janssens F., Moor B. D. Combining full text and biblio metric information in mapping scientific disciplines. Inf. Proc. Manag. 2005, 41 (6), 15481572. https://doi.org/10.1016/j.ipm.2005.03.021.

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

      78. Graham C. H., Ferrier S., Huettman F., Moritz C., Peterson A. T. New developments in museum-based informatics and applications in biodiversity analysis. Trend. Ecol. Evol. 2004, 19 (9), 497503. https://doi.org/10.1016/ j.tree.2004.07.006.

      79. Gruber T. R. A translation approach to portable ontologies. Knowl. Acquisition. 1993, 5 (2), 199–220. https://doi.org/10.1006/knac.1993.1008.

      80. Hirano S., Sun X., Tsumoto S. Comparison of clustering methods for clinical databases. Inform. Sci. 2004, 159 (34), 155165. https://doi.org/10.1016/j.ins.2003. 03.011.

      81. Hong Yu., Hatzivassiloglou V., Rzhetsky A., Wilbur W. J. Automatically identifying gene/protein terms in MEDLINE abstracts. J. Biomed. Inform. 2002, 35 (56), 322330. https://doi.org/10.1016/S1532-0464(03)00032-7.

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

      83. Hsi-Chieh Lee, Szu-Wei Huang, Li E. Y. Mining protein–protein interaction information on the internet. Exp. Syst. Appl. Elsevier. 2006, 30 (1), 142148. https://doi.org/10.1016/j.eswa.2005.09.083.

      84. Jabs R., Pivneva T., Huttmann K., Wyczynski A., Nolte C., Kettenmann H., Steinhäuser C. Synaptic transmission onto hyppocampal glial cells with hGFAP promoter activity. J. Cell Sci. 2005, V. 118, P. 37913803. https://doi.org/ 10.1242/jcs.02515.

      85. Johnson S. B., Friedman R. Bridging the gap between biological and clinical informatics in a graduate training program. J. Biomed. Inform. 2007, 40 (1), 5966. Epub. 2006 Mar 15. https://doi.org/10.1016/j.jbi.2006.02.011.

      86. Kaiser M., Hilgetag C. C. Modelling the development of cortical systems networks. Neurocomputing. 2004, V. 5860, P. 297302. https://doi.org/10.1016/ j.neucom.2004.01.059.

      87. Kane M. D., Brewer J. L. An information technology emphasis in biomedical informatics education. J. Biomed. Inform. 2007, 40 (1), 6772. https://doi.org/10.1016/j.jbi.2006.02.006.

      88. Kannathal N., Acharya U. R., Lim C. M., Sadasivan P. K. Characterization of EEG. A comparative study. Comp. Meth. Progr. Biomed. 2005, 80 (1), 1723. https://doi.org/10.1016/j.cmpb.2005.06.005.

      89. Koh W., McCormick B. H. Brain microstructure database system: an exoskeleton to 3D reconstruction and modelling. Neurocomputing. 2002, V. 4446, P. 10991105. https://doi.org/10.1016/S0925-2312(02)00426-5.

      90. Koh W., McCormick B. H. Registration of a 3D mouse brain atlas with brain microstructure data. Neurocomputing. 2003, V. 5254, P. 307312. https://doi.org/ 10.1016/S0925-2312(02)00793-2.

      91. Kulish V., Sourin A., Sourina O. Human electro encephalograms seen as fractal time series: Mathematical analysis and visualization. Comp. Biol. Med. 2006, 36 (3), 291302. https://doi.org/10.1016/j.compbiomed.2004.12.003.

      92. Lubitz von D., Wickramasinghe N. Networkcentric healthcare and bioinforma tics: Unified operations within three domains of knowledge. Exp. Syst. Appl. 2006, 30 (1), 1123. https://doi.org/10.1016/j.eswa.2005.09.069.

      93. 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., Sarachan 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), 3042. https://doi.org/10.1016/j.jbi.2003.09.003.

      94. 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), 109127. https://doi.org/10.1016/j.compbiomed.2004.09.005.

      95. Moon S., Byun Y., Han K. FSDB: A frameshift signal database. Comp. Biol. Chem. 2007, 31 (4), 298302. https://doi.org/10.1016/j.compbiolchem.2007.05.004.

      96. Orgun B., Vu J. HL7 ontology and mobile agents for interoperability in heterogeneous medical information systems. Comp. Biol. Med. 2006, 36 (78), 817836. https://doi.org/10.1016/j.compbiomed.2005.04.010.

      97. Pérez-Rey D., Maojo V., García-Remesal M., Alonso-Calvo R., Billhardt H., Martin- Sánchez F., Sousa A. Ontofusion: Ontologybased integration of genomic and clinical databases. Comp. Biol. Med. 2006, 36 (78), 712730. https://doi.org/10.1016/j.compbiomed.2005.02.004.

      98. Rana B. K., Insel P. A. G-protein-coupled receptor websites. Trend. Pharmacol. Sci. 2002, 23 (11), 535536. https://dx.doi.org/10.1016/S0165-6147(02)02113-2.