https://oldena.lpnu.ua/handle/ntb/52518| Title: | Information Technology of Gene Expression Profiles Processing for Purpose of Gene Regulatory Networks Reconstruction |
| Authors: | Babichev, Sergii Lytvynenko, Volodymyr Korobchynskyi, Maxim Škvor, Jiři Voronenko, Maria |
| Affiliation: | Jan Evangelista Purkyne University Kherson National Technical University Military-Diplomatic Academy named Eugene Bereznyak |
| Bibliographic description (Ukraine): | Information Technology of Gene Expression Profiles Processing for Purpose of Gene Regulatory Networks Reconstruction / Sergii Babichev, Volodymyr Lytvynenko, Maxim Korobchynskyi, Jiři Škvor, Maria Voronenko // Data stream mining and processing : proceedings of the IEEE second international conference, 21-25 August 2018, Lviv. — Львів : Lviv Politechnic Publishing House, 2018. — P. 336–341. — (Hybrid Systems of Computational Intelligence). |
| Bibliographic description (International): | Information Technology of Gene Expression Profiles Processing for Purpose of Gene Regulatory Networks Reconstruction / Sergii Babichev, Volodymyr Lytvynenko, Maxim Korobchynskyi, Jiři Škvor, Maria Voronenko // Data stream mining and processing : proceedings of the IEEE second international conference, 21-25 August 2018, Lviv. — Lviv Politechnic Publishing House, 2018. — P. 336–341. — (Hybrid Systems of Computational Intelligence). |
| Is part of: | Data stream mining and processing : proceedings of the IEEE second international conference, 2018 |
| Conference/Event: | IEEE second international conference "Data stream mining and processing" |
| Issue Date: | 28-Feb-2018 |
| Publisher: | Lviv Politechnic Publishing House |
| Place of the edition/event: | Львів |
| Temporal Coverage: | 21-25 August 2018, Lviv |
| Keywords: | objective clustering reduction biclustering gene expression profiles gene regulatory network reconstruction validation |
| Number of pages: | 6 |
| Page range: | 336-341 |
| Start page: | 336 |
| End page: | 341 |
| Abstract: | The paper presents the information technology of gene expression profiles processing in order to reconstruct gene regulatory networks. The information technology is presented as a structural block-chart, which contains all stages of studied data processing. DNA microchips of patients, which were studied on different types of diseases, were used as experimental data. The relative criteria of validation for all reconstructed networks were calculated during simulation process. The obtained results show high efficiency of the proposed technology. High values of the validation criteria indicate a high level of the obtained gene networks objectivity. |
| URI: | https://ena.lpnu.ua/handle/ntb/52518 |
| ISBN: | © Національний університет „Львівська політехніка“, 2018 © Національний університет „Львівська політехніка“, 2018 |
| Copyright owner: | © Національний університет “Львівська політехніка”, 2018 |
| References (Ukraine): | [1] D. Zak, R. Vadigepalli, E. Gonye, F. Doyle, at al. “Unconventional systems analysis problem in molecular biology: a case study in gene regulatory network modeling,” Computational and Chemical Engineering, 29(3), pp. 547-563, 2005. [2] M. Schena, and R. W. Davis. “Microarray biochip technology,” Eaton Publishing, pp. 1-18, 2000. [3] J. M. Heather, and B. Chain, “The sequence of sequencers: The history of sequencing DNA,” Genomics, vol. 107, pp. 1-8, 2016. [4] G. Setlak, Y. Bodyanskiy, I. Pliss, O. Vynokurova, D. Peleshko, and I. Kobylin, “Adaptive fuzzy clustering of multivariate short series with unevenly distributed observations based on matrix neuro-fuzzy self-organizing network,” Advances in Intelligent Systems and Computing, 643, pp. 308-315, 2018. [5] Y. Bodyanskiy, O. Vynokurova, V. Savvo, T. Tverdokhlib, and P. Mulesa, “Hybrid clustering-classification neural network in the medical diagnostics of the reactive arthritis,” International Journal of Intelligent Systems and Applications, 8 (8), pp. 1-9, 2016. [6] Y. Bodyanskiy, O. Tyshchenko, and D. S. Kopaliani, “An evolving connectionist system for data stream fuzzy clustering and its online learning,” Neurocomputing, 262, pp. 41-56, 2017. [7] Z. Hu, Y. Bodyanskiy, O. Tyshchenko, and O. Boiko, “A neuro-fuzzy Kohonen network for data stream possibilistic clustering and its online self-learning procedure,” Applied soft computing, 2017. [8] V. Mashkov, “Task allocation among aggents of restricted alliance,” Eighth IASTED International Conference on Intelligent Systems and Control, ISC 2005, pp. 13-18, 2005. [9] V. A. Mashkov, and O. V. Barabash, “Self-testing of multimodule systems on optimal check-connection structures,” Engineering Simulation, 13 (3), pp. 479-492, 1996. [10] P. Bidyuk, A. Gozhyj, I. Kalinina, V. Gozhyj. Analysis of uncertainly types for model building and forecacting dynamic processes, Advances in Intelligent Systems and Computing, 689, pp. 66-78, 2018. [11] A. Gozhyj, I. Kalinina, and V. Gozhyj, “Fuzzy cognitive analysis and modeling of water quality,” IEEE 9th International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, IDAACS 2017, 1, art. no. 8095092, pp. 289-293, 2017. [12] P. Bidyuk, A. Gozhyj, I. Kalinina, and V. Gozhyj, “Methods for processing uncertainties in solving dynamic planning problems,” 12th International Scientific and Technical Conference on Computer Sciences and Information Technologies, CSIT 2017, 1, art. no. 8098757, pp. 151-155, 2017. [13] A. Mukhopadhyay, U. Maulik, and S. Bandyopadhyay, “On biclustering of gene expression data,” Current Bioinformatics, vol. 5, pp. 204-216, 2010. [14] B. Pontes, R. Giráldez, and J. S. Aguilar-Ruiz, “Biclustering on expression data: A review,” Journal of Biomedical Informatics, vol. 57 pp. 163-180, 2015. [15] J. Hausser and K. Strimmer, “Entropy inference and the james-stein estimator with application to nonlinear gene association networks,” Journal of Machine Learning Research, vol. 10, pp.1469–1484, 2009. [16] S. Babichev, V. Lytvynenko, M. Korobchynskyi, and M. A. Taif, “Objective clustering inductive technology of gene expression sequences features,” Communications in Computer and Information Science, vol. 716, pp.359-372, 2016. [17] S. Babichev, V. Lytvynenko, J. Skvor, and J. Fiser. “Model of the objective clustering inductive technology of gene expression profiles based on SOTA and DBSCAN clustering algorithms,” Advances in Intelligent Systems and Computing, vol. 689, pp. 21-39, 2018. [18] S. Babichev, M. A. Taif, V. Lytvynenko, and V. Osypenko, “Criterial analysis of gene expression sequences to create the objective clustering inductive technology,” IEEE 37th International Conference on Electronics and Nanotechnology, ELNANO 2017, pp. 244-248, 2017. [19] S. Kaiser, Biclustering: Methods, Software and Application, Minchin, 2011. [20] S. Babichev, M. Korobchynskyi, O. Lahodynskyi, O. Korchomnyi, and V. Borynskyi, “Development of a technique for the reconstruction and validation of gene network models based on gene expression profiles,” East-European journal of enterprise technologies, vol. 1/4 (91), pp. 19-32, 2018. [21] W. S. Liang, E. M. Reiman, J. Valla, T. Dunckley, et al. “Alzheimer's disease is associated with reduced expression of energy metabolism genes in posterior cingulate neurons,” Proc. Nat. Acad. Sci. USA, vol. 105(11), pp. 4441-4446, 2008. [22] B. Zheng, Z. Liao, J. J. Locascio, K. A. Lesniak, et al. “PGC-1α, a potential therapeutic target for early intervention in Parkinson's disease,” Sci. Transl. Med., vol. 2(52), pp. 52-73, 2010. |
| References (International): | [1] D. Zak, R. Vadigepalli, E. Gonye, F. Doyle, at al. "Unconventional systems analysis problem in molecular biology: a case study in gene regulatory network modeling," Computational and Chemical Engineering, 29(3), pp. 547-563, 2005. [2] M. Schena, and R. W. Davis. "Microarray biochip technology," Eaton Publishing, pp. 1-18, 2000. [3] J. M. Heather, and B. Chain, "The sequence of sequencers: The history of sequencing DNA," Genomics, vol. 107, pp. 1-8, 2016. [4] G. Setlak, Y. Bodyanskiy, I. Pliss, O. Vynokurova, D. Peleshko, and I. Kobylin, "Adaptive fuzzy clustering of multivariate short series with unevenly distributed observations based on matrix neuro-fuzzy self-organizing network," Advances in Intelligent Systems and Computing, 643, pp. 308-315, 2018. [5] Y. Bodyanskiy, O. Vynokurova, V. Savvo, T. Tverdokhlib, and P. Mulesa, "Hybrid clustering-classification neural network in the medical diagnostics of the reactive arthritis," International Journal of Intelligent Systems and Applications, 8 (8), pp. 1-9, 2016. [6] Y. Bodyanskiy, O. Tyshchenko, and D. S. Kopaliani, "An evolving connectionist system for data stream fuzzy clustering and its online learning," Neurocomputing, 262, pp. 41-56, 2017. [7] Z. Hu, Y. Bodyanskiy, O. Tyshchenko, and O. Boiko, "A neuro-fuzzy Kohonen network for data stream possibilistic clustering and its online self-learning procedure," Applied soft computing, 2017. [8] V. Mashkov, "Task allocation among aggents of restricted alliance," Eighth IASTED International Conference on Intelligent Systems and Control, ISC 2005, pp. 13-18, 2005. [9] V. A. Mashkov, and O. V. Barabash, "Self-testing of multimodule systems on optimal check-connection structures," Engineering Simulation, 13 (3), pp. 479-492, 1996. [10] P. Bidyuk, A. Gozhyj, I. Kalinina, V. Gozhyj. Analysis of uncertainly types for model building and forecacting dynamic processes, Advances in Intelligent Systems and Computing, 689, pp. 66-78, 2018. [11] A. Gozhyj, I. Kalinina, and V. Gozhyj, "Fuzzy cognitive analysis and modeling of water quality," IEEE 9th International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, IDAACS 2017, 1, art. no. 8095092, pp. 289-293, 2017. [12] P. Bidyuk, A. Gozhyj, I. Kalinina, and V. Gozhyj, "Methods for processing uncertainties in solving dynamic planning problems," 12th International Scientific and Technical Conference on Computer Sciences and Information Technologies, CSIT 2017, 1, art. no. 8098757, pp. 151-155, 2017. [13] A. Mukhopadhyay, U. Maulik, and S. Bandyopadhyay, "On biclustering of gene expression data," Current Bioinformatics, vol. 5, pp. 204-216, 2010. [14] B. Pontes, R. Giráldez, and J. S. Aguilar-Ruiz, "Biclustering on expression data: A review," Journal of Biomedical Informatics, vol. 57 pp. 163-180, 2015. [15] J. Hausser and K. Strimmer, "Entropy inference and the james-stein estimator with application to nonlinear gene association networks," Journal of Machine Learning Research, vol. 10, pp.1469–1484, 2009. [16] S. Babichev, V. Lytvynenko, M. Korobchynskyi, and M. A. Taif, "Objective clustering inductive technology of gene expression sequences features," Communications in Computer and Information Science, vol. 716, pp.359-372, 2016. [17] S. Babichev, V. Lytvynenko, J. Skvor, and J. Fiser. "Model of the objective clustering inductive technology of gene expression profiles based on SOTA and DBSCAN clustering algorithms," Advances in Intelligent Systems and Computing, vol. 689, pp. 21-39, 2018. [18] S. Babichev, M. A. Taif, V. Lytvynenko, and V. Osypenko, "Criterial analysis of gene expression sequences to create the objective clustering inductive technology," IEEE 37th International Conference on Electronics and Nanotechnology, ELNANO 2017, pp. 244-248, 2017. [19] S. Kaiser, Biclustering: Methods, Software and Application, Minchin, 2011. [20] S. Babichev, M. Korobchynskyi, O. Lahodynskyi, O. Korchomnyi, and V. Borynskyi, "Development of a technique for the reconstruction and validation of gene network models based on gene expression profiles," East-European journal of enterprise technologies, vol. 1/4 (91), pp. 19-32, 2018. [21] W. S. Liang, E. M. Reiman, J. Valla, T. Dunckley, et al. "Alzheimer's disease is associated with reduced expression of energy metabolism genes in posterior cingulate neurons," Proc. Nat. Acad. Sci. USA, vol. 105(11), pp. 4441-4446, 2008. [22] B. Zheng, Z. Liao, J. J. Locascio, K. A. Lesniak, et al. "PGC-1α, a potential therapeutic target for early intervention in Parkinson's disease," Sci. Transl. Med., vol. 2(52), pp. 52-73, 2010. |
| Content type: | Conference Abstract |
| Appears in Collections: | Data stream mining and processing : proceedings of the IEEE second international conference |
| File | Description | Size | Format | |
|---|---|---|---|---|
| 2018_Babichev_S-Information_Technology_336-341.pdf | 1.22 MB | Adobe PDF | View/Open | |
| 2018_Babichev_S-Information_Technology_336-341__COVER.png | 570.92 kB | image/png | View/Open |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.