SSN 2410-7751 (Print)
ISSN 2410-776X (Online)
"Biotechnologia Acta" V. 10, No 3, 2017
https://doi.org/10.15407/biotech10.03.031
Р. 31-40, Bibliography 21, English
Universal Decimal Classification: 004:591.5:612:616-006
ON THE MATHEMATICAL METHODS IN BIOLOGY AND MEDICINE
Kavetsky Institute of Experimental Pathology, Oncology and Radiobiology of the National Academy of Sciences of Ukraine, Kyiv
The aim of the work was to analyze the range of mathematical methods and to choose the most prospective ones from the point of view of application in biology and medicine. After analyzing of approximately 200 current publications, a list of respective methods was completed. This list includes both the most recent, intensively developed methods as well as traditionally used ones — mathematical statistics, stochastic methods, regression analysis, and others. From the first group the methods of cluster analysis, artificial neural networks and image processing were subdivided. A description of each of these methods and examples of their application in practice are given. A separate group is dedicated to complex modern works, in which the problems requiring the complex application of several methods are present. In conclusions a brief assessment of the methods of cluster analysis, artificial neural networks, image processing methods are given as well as recommendations for their practical application.
Key words: processing, data bases.
© 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. Kondrashov S. N, Gorokhova M. N. Develop ment of an algorithm for optimal control of the process of formaldehyde production. Vestnik PNIPU “Chemical technology and biotechnology”. 2016, No. 1, P. 718. (In Russian).
3. “Classification and search of biomarkers in proteomics”. Available at http://bioinformatics.ru/Raznoe/Klassifikatciia-i-poiskbiomarkerov-v-proteomike.html (In Russian).
4. D’haeseleer P. How does gene expression clustering work? Nature Biotechnology. 2005, No. 23, 1499–1501. https://doi.org/10.1038/nbt1205-1499
5. Karpov P. A., Nadezhdina E. S., Emets A. I, Blum Y. B. Cluster analysis of similarity of microtubule-associated and cell cycle of human serine-threonine protein kinases with their plant homologues. Bulletin of the Moscow University. Series 16: Biology. Moscow. 2010, No 4. (In Russian).
6. 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
7. Brenton J. D. Carey L. A., Ahmed A. A. Molecular classification and molecular forecasting of breast cancer: ready for clinical application? J. Clin. Oncol. 2005, 23 (29), 7350–7360. https://doi.org/10.1200/JCO.2005.03.3845
8. 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).
9. Tashkinov A. A, Wildeman A. V, Bronnikov V. A. Application of the classification tree method to predict the level of development of motility in patients with impaired motor functions. Russian Journal of Biomechanics. 2008, 12 (4), 8495. (In Russian).
10. Vecht-Lifshitz S. E., Ison A. P. Biotechnological applications of image analysis: present and future prospects. Biotechnol. 1992, 23 (1), 118.
11. Goldys E. M. Fluorescence Applications in Biotechnology and the Life Sciences. USA: John Wiley & Sons. 2009, 367 p.
12. Perner P., Salvetti O. Advances in Mass Data Analysis of Images and Signals in Medicine, Biotechnology, Chemistry and Food Industry. Proceedings of the third International Conference, Leipzig, (Germany): Springer. 2008, 173 p. https://doi.org/10.1007/978-3-540-70715-8
13. Gavrilovich M. Spectraimage processing and application in biotechnology and pathology. Dissertation for Ph.D. Acta Universitatis Upsaliensis. Upsala. 2011, 63 p.
14. Shutko V. M, Shutko O. M., Kolganova O. O. Methods and means of compression of information. Kyiv: NAU-druk, 2012, 168 p. (In Ukrainian).
15. Natrajan R., Sailem H., Mardakheh F. K., Garcia M. F., Tape C. J., Dowsett M., Bakal C., YuanY. Micro environmental heterogeneity parallels breast cancer progression: a histology–genomic integration analysis. PLoS medicine. 2016, 13 (2), e1001961. https://doi.org/10.1371/journal. pmed.1001961.
16. Rebello S., Maheshwari U., Dsouza S., DSouza R. V. Back propagation neural network method for predicting Lac gene structures in Streptococcus pyogenes M Group A Streptococcus strains. Int. J. Mol. Biol. Res. 2 (4), 6172.
17. Moghaddam M. G., Ahmad F. B. H., Basri M., Rahman M. B. A. Artificial neural network modeling studies to predict the yield of enzymatic synthesis of betulinic acid ester. Electronic Journal of Biotechnology. 2010, 13 (3), 915. https://doi.org/10.2225/vol13-issue3-fulltext-9
18. Montague G, Morris J. Neural-network contributions in biotechnology. Trends Biotechnol. 1994, 12 (8), 31224. https://doi.org/10.1016/0167-7799(94)90048-5
19. Kallan G. Basic Concepts of Neural Networks. Moscow: Williams. 2001. 268 p. (In Russian).
20. Kruzhanivska A. Ye. Local widespread cervical cancer. Ph. D. Dissertation abstract. Ivano-Frankivsk. 2015. (In Ukrainian).
21. 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, No. 3, P. 146155. (In Ukrainian).