Bayesian Priors Prediction in Ze

Article Sidebar

PDF
Published: 2025-12-02
DOI: https://doi.org/10.5281/zenodo.17769150
Keywords:
Bayesian Prediction, Sequential Data Analysis, Hierarchical Modeling, Computational Biology, Pattern Recognition, Genomic Sequences, Clinical Forecasting, Open-source Software, Machine Learning

Main Article Content

Jaba Tkemaladze
Longevity Clinic
https://orcid.org/0000-0001-8651-7243

Abstract

Sequential data prediction represents a fundamental challenge across multiple domains, from genomic analysis to clinical monitoring, requiring sophisticated approaches that balance predictive accuracy with computational efficiency. This paper introduces Ze, a novel hybrid system that integrates frequency-based counting with hierarchical Bayesian modeling to address the complex demands of sequential pattern recognition. The system's architecture employs dual-processor analysis with complementary beginning (forward) and inverse (backward) processing strategies, enabling comprehensive pattern discovery that captures both progressive sequences and symmetrical structures. At its core, Ze implements a three-layer hierarchical Bayesian framework that operates at individual, group, and context levels, facilitating multi-scale pattern recognition while naturally quantifying prediction uncertainty. The individual layer employs Beta-Binomial conjugate priors for sequential Bayesian updating, while the group layer enables knowledge transfer across related patterns through shared hyperparameters. The context layer incorporates temporal dependencies through configurable sequence memory, capturing crucial short-term patterns that significantly influence prediction accuracy. Implementation results demonstrate that the hierarchical Bayesian approach achieves an 8.3% accuracy improvement over standard Bayesian methods and 2.3× faster convergence through efficient knowledge sharing. The system maintains practical computational efficiency through sophisticated memory management, including automatic counter reset mechanisms and compact binary representations that reduce storage requirements by 45%. Ze's modular design and open-source availability ensure broad applicability across diverse domains including genomic sequence annotation, clinical time series forecasting, and real-time anomaly detection. The system represents a significant advancement in sequential data prediction methodology, combining statistical rigor with computational practicality to address complex pattern recognition challenges in scientific and clinical applications.

Article Details

Issue

Vol. 1 No. 4 (2025)

Section

Technology and Innovations
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Articles published in Longevity Horizon fall under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0). This means authors retain their copyright while granting the journal the right to be the first publisher. Readers are permitted to share and distribute the work as long as proper credit is given. However, the content may not be altered, modified, or used for commercial purposes.

Author Biography

Jaba Tkemaladze, Longevity Clinic

The Goal of Dr. Jaba Tkemaladze - Systemic Rejuvenation Through in Vitro–Derived Safe Adult Stem Cells and Their Transplantation. His theoretical works include the development of the Centriolar Theory of Differentiation and the Centriolar Theory of Organismal Aging, which suggest the role of centrioles in the aging and development of cells and tissues. President, Longevity Alliance Georgia. Head of Department, Longevity Clinic, Inc., Georgia.

 

A physician-scientist specializing in the biology of ageing and longevity. His research focuses on the potential of stem cell therapies for age-related diseases and healthspan extension.

Current Roles:

  • President, Longevity Alliance Georgia.
  • Head of Department, Longevity Clinic, Inc., Georgia.

Research Focus:
His main research is related to the study of methodologies for returning the regeneration rate to the indicators of 12-24 years, in particular with the potential application of the technology of producing young safe stem cells from one's own somatic cells. His theoretical works include the development of the Centriolar Theory of Differentiation and the Centriolar Theory of Organismal Aging, which suggest the role of centrioles in the aging and development of cells and tissues.

Background:
Dr. Tkemaladze received his medical education at the Tbilisi State Medical University and continued his research in the laboratories of the Institute of Morphology and the Research Institute of Psychiatry. In his work, he uses a combined approach, combining experimental and computational methods, to study the aging process and develop treatments for age-related diseases.

Service and Recognition:
He has served on scientific advisory boards, including for the Georgian Ministry of Defense and the Longevity Alliance. He is the author of over 100 scientific publications and has been an invited speaker at numerous national and international conferences.

How to Cite

Tkemaladze, J. (2025). Bayesian Priors Prediction in Ze. Longevity Horizon, 1(4). DOI:https://doi.org/10.5281/zenodo.17769150
Crossref
Scopus
Google Scholar
Europe PMC

References

Alon, N., Matias, Y., & Szegedy, M. (1999). The space complexity of approximating the frequency moments. Journal of Computer and System Sciences, 58(1), 137-147.

Altschul, S. F., Gish, W., Miller, W., Myers, E. W., & Lipman, D. J. (1990). Basic local alignment search tool. Journal of Molecular Biology, 215(3), 403-410.

Aphkhazava, D., Sulashvili, N., & Tkemaladze, J. (2025). Stem Cell Systems and Regeneration. Georgian Scientists, 7(1), 271–319. doi : https://doi.org/10.52340/gs.2025.07.01.26

Aphkhazava, D., Sulashvili, N., Maglakelidze, G., & Tkemaladze, J. (2025). Ageless Creatures: Molecular Insights into Organisms That Defy Aging. Georgian Scientists, 7(3), 346–396. doi : https://doi.org/10.52340/gs.2025.07.03.24

Axelsson, S. (2000). Intrusion detection systems: A survey and taxonomy. Technical Report, 99-15.

Bamshad, M. J., Ng, S. B., Bigham, A. W., Tabor, H. K., Emond, M. J., Nickerson, D. A., & Shendure, J. (2011). Exome sequencing as a tool for Mendelian disease gene discovery. Nature Reviews Genetics, 12(11), 745-755.

Barnes, N. (2010). Publish your computer code: it is good enough. Nature, 467(7317), 753. https://doi.org/10.1038/467753a

Bassett, D. S., & Sporns, O. (2017). Network neuroscience. Nature Neuroscience, 20(3), 353-364.

Berman, H. M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T. N., Weissig, H., ... & Bourne, P. E. (2000). The Protein Data Bank. Nucleic Acids Research, 28(1), 235-242.

Bernardo, J. M., & Smith, A. F. (2000). Bayesian theory. John Wiley & Sons.

Berry, D. A. (2006). Bayesian clinical trials. Nature Reviews Drug Discovery, 5(1), 27-36.

Betancourt, M. (2017). A conceptual introduction to Hamiltonian Monte Carlo. arXiv preprint arXiv:1701.02434.

Bifet, A., & Gavalda, R. (2007). Learning from time-changing data with adaptive windowing. In Proceedings of the 2007 SIAM international conference on data mining (pp. 443-448).

Bifet, A., Holmes, G., Kirkby, R., & Pfahringer, B. (2010). MOA: Massive Online Analysis. Journal of Machine Learning Research, 11, 1601-1604.

Bishop, C. M. (2006). Pattern recognition and machine learning. Springer.

Blei, D. M., & McAuliffe, J. D. (2010). Supervised topic models. In Advances in neural information processing systems (pp. 327-335).

Blei, D. M., Kucukelbir, A., & McAuliffe, J. D. (2017). Variational inference: A review for statisticians. Journal of the American Statistical Association, 112(518), 859-877.

Boettiger, C. (2015). An introduction to Docker for reproducible research. ACM SIGOPS Operating Systems Review, 49(1), 71–79. https://doi.org/10.1145/2723872.2723882

Bohadana, A., Izbicki, G., & Kraman, S. S. (2014). Fundamentals of lung auscultation. New England Journal of Medicine, 370(8), 744-751.

Broder, A. Z., & Mitzenmacher, M. (2004). Network applications of bloom filters: A survey. Internet Mathematics, 1(4), 485-509.

Brooks, S. P., & Gelman, A. (1998). General methods for monitoring convergence of iterative simulations. Journal of Computational and Graphical Statistics, 7(4), 434-455.

Brooks, S., Gelman, A., Jones, G., & Meng, X. L. (2011). Handbook of Markov Chain Monte Carlo. CRC press.

Brown, E. N., Kass, R. E., & Mitra, P. P. (2004). Multiple neural spike train data analysis: state-of-the-art and future challenges. Nature Neuroscience, 7(5), 456-461.

Buitinck, L., Louppe, G., Blondel, M., Pedregosa, F., Mueller, A., Grisel, O., ... & Varoquaux, G. (2013). API design for machine learning software: experiences from the scikit-learn project. arXiv preprint arXiv:1309.0238.

Bulyk, M. L. (2006). DNA microarray technologies for measuring protein–DNA interactions. Current Opinion in Biotechnology, 17(4), 422-430.

Butte, A. J. (2008). Translational bioinformatics: coming of age. Journal of the American Medical Informatics Association, 15(6), 709-714.

Butte, A. J. (2008). Translational bioinformatics: coming of age. Journal of the American Medical Informatics Association, 15(6), 709–714. https://doi.org/10.1197/jamia.M2824

Carlin, B. P., & Louis, T. A. (2000). Bayes and empirical Bayes methods for data analysis. Chapman and Hall/CRC.

Carvalho, C. M., Polson, N. G., & Scott, J. G. (2010). The horseshoe estimator for sparse signals. Biometrika, 97(2), 465-480.

Chen, Z., & Liu, B. (2016). Lifelong machine learning. Synthesis Lectures on Artificial Intelligence and Machine Learning, 10(3), 1-145.

Chichinadze, K. N., & Tkemaladze, D. V. (2008). Centrosomal hypothesis of cellular aging and differentiation. Advances in Gerontology= Uspekhi Gerontologii, 21(3), 367-371.

Chichinadze, K., Lazarashvili, A., & Tkemaladze, J. (2013). RNA in centrosomes: structure and possible functions. Protoplasma, 250(1), 397-405.

Chichinadze, K., Tkemaladze, D., & Lazarashvili, A. (2012). New class of RNA and centrosomal hypothesis of cell aging. Advances in Gerontology= Uspekhi Gerontologii, 25(1), 23-28.

Chichinadze, K., Tkemaladze, J., & Lazarashvili, A. (2012). A new class of RNAs and the centrosomal hypothesis of cell aging. Advances in Gerontology, 2(4), 287-291.

Chichinadze, K., Tkemaladze, J., & Lazarashvili, A. (2012). Discovery of centrosomal RNA and centrosomal hypothesis of cellular ageing and differentiation. Nucleosides, Nucleotides and Nucleic Acids, 31(3), 172-183.

Chipman, H. A., George, E. I., & McCulloch, R. E. (2010). BART: Bayesian additive regression trees. The Annals of Applied Statistics, 4(1), 266-298.

Clifford, G. D., & Clifton, D. (2012). Wireless technology in disease management and medicine. Annual Review of Medicine, 63, 479-492.

Cook, D., & Swayne, D. F. (2007). Interactive and dynamic graphics for data analysis: with R and GGobi. Springer Science & Business Media.

Cormen, T. H., Leiserson, C. E., Rivest, R. L., & Stein, C. (2009). Introduction to algorithms. MIT press.

Cormode, G., & Hadjieleftheriou, M. (2008). Finding frequent items in data streams. Proceedings of the VLDB Endowment, 1(2), 1530-1541.

Cormode, G., & Muthukrishnan, S. (2005). An improved data stream summary: the count-min sketch and its applications. Journal of Algorithms, 55(1), 58-75.

Cover, T. M., & Thomas, J. A. (2006). Elements of information theory. John Wiley & Sons.

Dean, J., & Ghemawat, S. (2008). MapReduce: simplified data processing on large clusters. Communications of the ACM, 51(1), 107-113.

Dubois, P. F. (2007). Maintaining correctness in scientific programs. Computing in Science & Engineering, 9(3), 80–85. https://doi.org/10.1109/MCSE.2007.60

Durbin, R., Eddy, S. R., Krogh, A., & Mitchison, G. (1998). Biological sequence analysis: probabilistic models of proteins and nucleic acids. Cambridge university press.

Eddy, S. R. (2004). What is a hidden Markov model?. Nature Biotechnology, 22(10), 1315-1316.

Efron, B. (2010). Large-scale inference: empirical Bayes methods for estimation, testing, and prediction. Cambridge University Press.

Elfettahi, A. E., & Tkemaladze, J.(2025). The Neuro-Hepatic-Affective Model (NHAM): A Systems Framework for Liver–Brain Modulation of Emotion in Precision Psychiatry. Preprints. doi : https://doi. org/10.20944/preprints202508, 1312, v1.

Endert, A., Hossain, M. S., Ramakrishnan, N., North, C., Fiaux, P., & Andrews, C. (2014). The human is the loop: new directions for visual analytics. Journal of Intelligent Information Systems, 43(3), 411-435.

Fong, Y., Holmes, C. C., & Walker, S. G. (2021). Bayesian nonparametric models for data analysis. Annual Review of Statistics and Its Application, 8, 197-221.

Friston, K. J. (2011). Functional and effective connectivity: a review. Brain Connectivity, 1(1), 13-36.

Gama, J., Žliobaitė, I., Bifet, A., Pechenizkiy, M., & Bouchachia, A. (2014). A survey on concept drift adaptation. ACM Computing Surveys, 46(4), 1-37.

Gamma, E., Helm, R., Johnson, R., & Vlissides, J. (1994). Design patterns: elements of reusable object-oriented software. Addison-Wesley Professional.

Garcia-Teodoro, P., Diaz-Verdejo, J., Maciá-Fernández, G., & Vázquez, E. (2009). Anomaly-based network intrusion detection: Techniques, systems and challenges. Computers & Security, 28(1-2), 18-28.

Gehlenborg, N., O'Donoghue, S. I., Baliga, N. S., Goesmann, A., Hibbs, M. A., Kitano, H., ... & Sansone, S. A. (2010). Visualization of omics data for systems biology. Nature Methods, 7(3), S56-S68.

Gelman, A., & Hill, J. (2007). Data analysis using regression and multilevel/hierarchical models. Cambridge university press.

Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian data analysis (3rd ed.). Chapman and Hall/CRC.

Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian data analysis. Chapman and Hall/CRC.

Ghahramani, Z. (2015). Probabilistic machine learning and artificial intelligence. Nature, 521(7553), 452-459.

Ghassemi, M., Naumann, T., Doshi-Velez, F., Brimmer, N., Joshi, R., Rumshisky, A., & Szolovits, P. (2015). Unfolding physiological state: mortality modelling in intensive care units. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 75-84).

Ghosh, J., Delampady, M., & Samanta, T. (2006). An introduction to Bayesian analysis: theory and methods. Springer Science & Business Media.

Goecks, J., Nekrutenko, A., Taylor, J., & The Galaxy Team. (2010). Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences. Genome Biology, 11(8), R86. https://doi.org/10.1186/gb-2010-11-8-r86

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT press.

Graves, A., & Schmidhuber, J. (2005). Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Networks, 18(5-6), 602-610.

Graves, A., Mohamed, A. R., & Hinton, G. (2013). Speech recognition with deep recurrent neural networks. In 2013 IEEE international conference on acoustics, speech and signal processing (pp. 6645-6649). IEEE.

Greenland, S. (2000). Principles of multilevel modelling. International Journal of Epidemiology, 29(1), 158-167.

Gusfield, D. (1997). Algorithms on strings, trees and sequences: computer science and computational biology. Cambridge university press.

Harris, C. R., Millman, K. J., van der Walt, S. J., Gommers, R., Virtanen, P., Cournapeau, D., ... & Oliphant, T. E. (2020). Array programming with NumPy. Nature, 585(7825), 357–362. https://doi.org/10.1038/s41586-020-2649-2

Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: data mining, inference, and prediction. Springer Science & Business Media.

Heer, J., Bostock, M., & Ogievetsky, V. (2010). A tour through the visualization zoo. Communications of the ACM, 53(6), 59-67.

Hennessy, J. L., & Patterson, D. A. (2011). Computer architecture: a quantitative approach. Elsevier.

Hernán, M. A., & Robins, J. M. (2020). Causal inference: what if. Chapman & Hall/CRC.

Hinsen, K. (2013). The reproducible document: a simple system for authoring reproducible research. Computing in Science & Engineering, 15(5), 66–71. https://doi.org/10.1109/MCSE.2013.97

Hoffman, M. D., & Gelman, A. (2014). The No-U-Turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo. Journal of Machine Learning Research, 15(1), 1593-1623.

Hood, L., & Flores, M. (2012). A personal view on systems medicine and the emergence of proactive P4 medicine: predictive, preventive, personalized and participatory. New Biotechnology, 29(6), 613-624.

Hravnak, M., Edwards, L., Clontz, A., Valenta, C., DeVita, M. A., & Pinsky, M. R. (2008). Defining the incidence of cardiorespiratory instability in patients in step-down units using an electronic integrated monitoring system. Archives of Internal Medicine, 168(12), 1300-1308.

Hudak, P. (1989). Conception, evolution, and application of functional programming languages. ACM Computing Surveys, 21(3), 359-411.

Imbens, G. W., & Rubin, D. B. (2015). Causal inference in statistics, social, and biomedical sciences. Cambridge University Press.

Ince, D. C., Hatton, L., & Graham-Cumming, J. (2012). The case for open computer programs. Nature, 482(7386), 485–488. https://doi.org/10.1038/nature10836

Jaba, T. (2022). Dasatinib and quercetin: short-term simultaneous administration yields senolytic effect in humans. Issues and Developments in Medicine and Medical Research Vol. 2, 22-31.

Jaba, T. (2025). Heroic Self-Myth Hypothesis. Longevity Horizon, 1(4). doi : https://doi.org/10.5281/zenodo.17711334

James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning. Springer.

Jensen, P. B., Jensen, L. J., & Brunak, S. (2012). Mining electronic health records: towards better research applications and clinical care. Nature Reviews Genetics, 13(6), 395-405.

Jones, R., & Lins, R. (1996). Garbage collection: algorithms for automatic dynamic memory management. John Wiley & Sons, Inc..

Jordan, M. I. (2019). Artificial intelligence—the revolution hasn’t happened yet. Harvard Data Science Review, 1(1).

Kadane, J. B., & Seidenfeld, T. (2018). Bayesian methods and ethics in a clinical trial. Journal of the American Statistical Association, 113(524), 1377-1386.

Kéry, M. (2010). Introduction to WinBUGS for ecologists: Bayesian approach to regression, ANOVA, mixed models and related analyses. Academic Press.

Kipshidze, M., & Tkemaladze, J. (2023). Comparative Analysis of drugs that improve the Quality of Life and Life Expectancy. Junior Researchers, 1(1), 184–193. doi : https://doi.org/10.52340/2023.01.01.19

Kipshidze, M., & Tkemaladze, J. (2023). The planaria Schmidtea mediterranea as a model system for the study of stem cell biology. Junior Researchers, 1(1), 194–218. doi : https://doi.org/10.52340/2023.01.01.20

Kipshidze, M., & Tkemaladze, J. (2024). Abastumani Resort: Balneological Heritage and Modern Potential. Junior Researchers, 2(2), 126–134. doi : https://doi.org/10.52340/jr.2024.02.02.12

Kipshidze, M., & Tkemaladze, J. (2024). Balneology in Georgia: traditions and modern situation. Junior Researchers, 2(2), 78–97. doi : https://doi.org/10.52340/jr.2024.02.02.09

Kipshidze, M., & Tkemaladze, J. (2024). Microelementoses-history and current status. Junior Researchers, 2(2), 108–125. doi : https://doi.org/10.52340/jr.2024.02.02.11

Knight, R., Maxwell, P., Birmingham, A., Carnes, J., Caporaso, J. G., Easton, B. C., ... & Knight, T. (2001). PyCogent: a toolkit for making sense from sequence. Genome Biology, 8(8), R171.

Kristensen, V. N., Lingjærde, O. C., Russnes, H. G., Vollan, H. K., Frigessi, A., & Børresen-Dale, A. L. (2014). Principles and methods of integrative genomic analyses in cancer. Nature Reviews Cancer, 14(5), 299-313.

Kruschke, J. K. (2015). Doing Bayesian data analysis: A tutorial with R, JAGS, and Stan. Academic Press.

Lander, E. S., Linton, L. M., Birren, B., Nusbaum, C., Zody, M. C., Baldwin, J., ... & Morgan, M. J. (2001). Initial sequencing and analysis of the human genome. Nature, 409(6822), 860-921.

LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.

Lee, B. D. (2018). Ten simple rules for documenting scientific software. PLoS Computational Biology, 14(12), e1006561. https://doi.org/10.1371/journal.pcbi.1006561

Lehman, L. W., Saeed, M., & Mark, R. G. (2015). A generative modeling approach for interpreting physiological data. IEEE Journal of Biomedical and Health Informatics, 19(4), 1259-1267.

Lezhava, T., Monaselidze, J., Jokhadze, T., Kakauridze, N., Khodeli, N., Rogava, M., Tkemaladze, J., ... & Gaiozishvili, M. (2011). Gerontology research in Georgia. Biogerontology, 12, 87-91. doi : 10.1007/s10522-010-9283-6. Epub 2010 May 18. PMID: 20480236; PMCID: PMC3063552

Li, H., & Durbin, R. (2009). Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics, 25(14), 1754-1760.

Libbrecht, M. W., & Noble, W. S. (2015). Machine learning applications in genetics and genomics. Nature Reviews Genetics, 16(6), 321–332. https://doi.org/10.1038/nrg3920

Little, R. J., & Rubin, D. B. (2019). Statistical analysis with missing data. John Wiley & Sons.

Losing, V., Hammer, B., & Wersing, H. (2018). Incremental on-line learning: A review and comparison of state of the art algorithms. Neurocomputing, 275, 1261-1274.

Luo, W., Phung, D., Tran, T., Gupta, S., Rana, S., Karmakar, C., ... & Venkatesh, S. (2016). Guidelines for developing and reporting machine learning predictive models in biomedical research: a multidisciplinary view. Journal of Medical Internet Research, 18(12), e323.

MacArthur, D. G., Balasubramanian, S., Frankish, A., Huang, N., Morris, J., Walter, K., ... & Hurles, M. E. (2012). A systematic survey of loss-of-function variants in human protein-coding genes. Science, 335(6070), 823-828.

Makeig, S., Debener, S., Onton, J., & Delorme, A. (2004). Mining event-related brain dynamics. Trends in Cognitive Sciences, 8(5), 204-210.

Manolio, T. A., Chisholm, R. L., Ozenberger, B., Roden, D. M., Williams, M. S., Wilson, R., ... & Ginsburg, G. S. (2013). Implementing genomic medicine in the clinic: the future is here. Genetics in Medicine, 15(4), 258-267.

Mardis, E. R. (2008). The impact of next-generation sequencing technology on genetics. Trends in Genetics, 24(3), 133-141.

Matsaberidze, M., Prangishvili, A., Gasitashvili, Z., Chichinadze, K., & Tkemaladze, J. (2017). TO TOPOLOGY OF ANTI-TERRORIST AND ANTI-CRIMINAL TECHNOLOGY FOR EDUCATIONAL PROGRAMS. International Journal of Terrorism & Political Hot Spots, 12.

McElreath, R. (2020). Statistical rethinking: A Bayesian course with examples in R and Stan. Chapman and Hall/CRC.

Metzker, M. L. (2010). Sequencing technologies—the next generation. Nature Reviews Genetics, 11(1), 31-46.

Meyer, M., Munzner, T., & Pfister, H. (2014). MizBee: a multiscale synteny browser. IEEE Transactions on Visualization and Computer Graphics, 15(6), 897-904.

Mirnezami, R., Nicholson, J., & Darzi, A. (2012). Preparing for precision medicine. New England Journal of Medicine, 366(6), 489-491.

Morris, C. N. (1983). Parametric empirical Bayes inference: theory and applications. Journal of the American Statistical Association, 78(381), 47-55.

Mporas, I., Tsirka, V., Zacharaki, E. I., Koutroumanidis, M., Richardson, M., & Megalooikonomou, V. (2015). Seizure detection using EEG and ECG signals for computer-based monitoring, analysis and management of epileptic patients. Expert Systems with Applications, 42(6), 3227-3233.

Murphy, K. P. (2012). Machine learning: a probabilistic perspective. MIT press.

Murphy, S. N., Weber, G., Mendis, M., Gainer, V., Chueh, H. C., Churchill, S., & Kohane, I. (2009). Serving the enterprise and beyond with informatics for integrating biology and the bedside (i2b2). Journal of the American Medical Informatics Association, 17(2), 124-130.

Noble, W. S. (2009). A quick guide to organizing computational biology projects. PLoS Computational Biology, 5(7), e1000424. https://doi.org/10.1371/journal.pcbi.1000424

Obermeyer, Z., & Emanuel, E. J. (2016). Predicting the future—big data, machine learning, and clinical medicine. New England Journal of Medicine, 375(13), 1216-1219.

O'Donoghue, S. I., Gavin, A. C., Gehlenborg, N., Goodsell, D. S., Hériché, J. K., Nielsen, C. B., ... & Wong, B. (2010). Visualizing biological data—now and in the future. Nature Methods, 7(3), S2-S4.

Parmigiani, G., & Garrett, E. S. (2014). The analysis of gene expression data: methods and software. Springer.

Pasterkamp, H., Kraman, S. S., & Wodicka, G. R. (1997). Respiratory sounds: advances beyond the stethoscope. American Journal of Respiratory and Critical Care Medicine, 156(3), 974-987.

Pauwels, E. J., Ambekar, O., & Fua, P. (2009). Visual data mining. In Data Mining and Knowledge Discovery Handbook (pp. 1059-1076). Springer.

Pearl, J. (1984). Heuristics: intelligent search strategies for computer problem solving. Addison-Wesley.

Pearl, J. (2009). Causality: models, reasoning, and inference. Cambridge University Press.

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., ... & Duchesnay, É. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825–2830.

Peng, R. D. (2011). Reproducible research in computational science. Science, 334(6060), 1226-1227.

Peng, R. D., & Dominici, F. (2008). Statistical methods for environmental epidemiology with R: a case study in air pollution and health. Springer.

Penzel, T., Glos, M., Fietze, I., & Baumann, G. (2002). Heart rate variability and sleep. In Deepak, S. (Ed.), Sleep-related breathing disorders: experimental models and therapeutic potential (pp. 165-183). Marcel Dekker.

Petersen, K., Feldt, R., Mujtaba, S., & Mattsson, M. (2008). Systematic mapping studies in software engineering. In Proceedings of the 12th International Conference on Evaluation and Assessment in Software Engineering (pp. 68–77).

Piringer, H., Berger, W., & Krasser, J. (2014). HyperMoVal: interactive visual validation of regression models for real-time simulation. In Computer Graphics Forum (Vol. 33, No. 3, pp. 21-30).

Plummer, M. (2003). JAGS: A program for analysis of Bayesian graphical models using Gibbs sampling. In Proceedings of the 3rd international workshop on distributed statistical computing (Vol. 124, No. 125.10, pp. 1-10).

Polson, N. G., & Scott, S. L. (2012). On the half-Cauchy prior for a global scale parameter. Bayesian Analysis, 7(4), 887-902.

Prangishvili, A., Gasitashvili, Z., Matsaberidze, M., Chkhartishvili, L., Chichinadze, K., Tkemaladze, J., ... & Azmaiparashvili, Z. (2019). SYSTEM COMPONENTS OF HEALTH AND INNOVATION FOR THE ORGANIZATION OF NANO-BIOMEDIC ECOSYSTEM TECHNOLOGICAL PLATFORM. Current Politics and Economics of Russia, Eastern and Central Europe, 34(2/3), 299-305.

Prlić, A., & Procter, J. B. (2012). Ten simple rules for the open development of scientific software. PLoS Computational Biology, 8(12), e1002802. https://doi.org/10.1371/journal.pcbi.1002802

Rabiner, L. R. (1989). A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE, 77(2), 257-286.

Reichert, S., Gass, R., Brandt, C., & Andrès, E. (2008). Analysis of respiratory sounds: state of the art. Clinical Medicine: Circulatory, Respiratory and Pulmonary Medicine, 2, CCRPM-S530.

Ritchie, M. D., Holzinger, E. R., Li, R., Pendergrass, S. A., & Kim, D. (2015). Methods of integrating data to uncover genotype–phenotype interactions. Nature Reviews Genetics, 16(2), 85-97.

Ritchie, M. D., Holzinger, E. R., Li, R., Pendergrass, S. A., & Kim, D. (2015). Methods of integrating data to uncover genotype–phenotype interactions. Nature Reviews Genetics, 16(2), 85–97. https://doi.org/10.1038/nrg3868

Robert, C. P. (2007). The Bayesian choice: from decision-theoretic foundations to computational implementation. Springer Science & Business Media.

Robert, C. P., & Casella, G. (2004). Monte Carlo statistical methods (2nd ed.). Springer-Verlag.

Saeed, M., Villarroel, M., Reisner, A. T., Clifford, G., Lehman, L. W., Moody, G., ... & Mark, R. G. (2011). Multiparameter Intelligent Monitoring in Intensive Care II (MIMIC-II): a public-access intensive care unit database. Critical Care Medicine, 39(5), 952.

Sandve, G. K., Nekrutenko, A., Taylor, J., & Hovig, E. (2013). Ten simple rules for reproducible computational research. PLoS Computational Biology, 9(10), e1003285. https://doi.org/10.1371/journal.pcbi.1003285

Saria, S. (2018). Individualized sepsis treatment using reinforcement learning. Nature Medicine, 24(11), 1641-1642.

Saria, S. (2018). Individualized sepsis treatment using reinforcement learning. Nature Medicine, 24(11), 1641–1642. https://doi.org/10.1038/s41591-018-0213-5

Sarkar, M., Madabushi, R., & Zhao, J. (2011). Bayesian methods in medical device clinical trials. In Clinical Trials of Drugs and Medical Devices (pp. 113-132). Springer.

Schloss, P. D., Westcott, S. L., Ryabin, T., Hall, J. R., Hartmann, M., Hollister, E. B., ... & Weber, C. F. (2009). Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Applied and Environmental Microbiology, 75(23), 7537–7541. https://doi.org/10.1128/AEM.01541-09

Schork, N. J. (2015). Personalized medicine: time for one-person trials. Nature, 520(7549), 609-611.

Scott, J. G., & Berger, J. O. (2010). Bayes and empirical-Bayes multiplicity adjustment in the variable-selection problem. The Annals of Statistics, 38(5), 2587-2619.

Sendelbach, S., & Funk, M. (2013). Alarm fatigue: a patient safety concern. AACN Advanced Critical Care, 24(4), 378-386.

Siepel, A., Bejerano, G., Pedersen, J. S., Hinrichs, A. S., Hou, M., Rosenbloom, K., ... & Haussler, D. (2005). Evolutionarily conserved elements in vertebrate, insect, worm, and yeast genomes. Genome Research, 15(8), 1034-1050.

Sommer, R., & Paxson, V. (2010). Outside the closed world: On using machine learning for network intrusion detection. In 2010 IEEE symposium on security and privacy (pp. 305-316).

Sovijärvi, A. R., Dalmasso, F., Vanderschoot, J., Malmberg, L. P., Righini, G., & Stoneman, S. A. (2000). Definition of terms for applications of respiratory sounds. European Respiratory Review, 10(77), 597-610.

Spiegelhalter, D. J., Abrams, K. R., & Myles, J. P. (2004). Bayesian approaches to clinical trials and health-care evaluation. John Wiley & Sons.

Stein, L. D. (2001). Genome annotation: from sequence to biology. Nature Reviews Genetics, 2(7), 493-503.

Stephens, M., & Balding, D. J. (2009). Bayesian statistical methods for genetic association studies. Nature Reviews Genetics, 10(10), 681-690.

Stodden, V., Leisch, F., & Peng, R. D. (2014). Implementing reproducible research. Chapman and Hall/CRC.

Stodden, V., McNutt, M., Bailey, D. H., Deelman, E., Gil, Y., Hanson, B., ... & Taufer, M. (2016). Enhancing reproducibility for computational methods. Science, 354(6317), 1240-1241.

Stonebraker, M., Madden, S., Abadi, D. J., Harizopoulos, S., Hachem, N., & Helland, P. (2007). The end of an architectural era (it's time for a complete rewrite). In Proceedings of the 33rd international conference on Very large data bases (pp. 1150-1160).

Stormo, G. D. (2000). DNA binding sites: representation and discovery. Bioinformatics, 16(1), 16-23.

Stormo, G. D., & Fields, D. S. (1998). Specificity, free energy and information content in protein–DNA interactions. Trends in Biochemical Sciences, 23(3), 109-113.

Suchard, M. A., Wang, Q., Chan, C., Frelinger, J., Cron, A., & West, M. (2010). Understanding GPU programming for statistical computation: studies in massively parallel massive mixtures. Journal of Computational and Graphical Statistics, 19(2), 419-438.

Szyperski, C. (2002). Component software: beyond object-oriented programming. Addison-Wesley Professional.

Tkemaladze, J. (2025). Bayesian Principles in Ze Systems. Preprints. doi : https://doi.org/10.20944/preprints202510.1287.v1

Tkemaladze, J. (2025). Concept of Death Awareness as an Existential Alarm Clock in the Context of Hypothetical Biological Immortality. Preprints. doi : https://doi.org/10.20944/preprints202510.1067.v1

Tkemaladze, J. (2025). Lakes as Strategic Food Reserves. Preprints. doi : https://doi.org/10.20944/preprints202510.2035.v1

Tkemaladze, J. (2025). Rejuvenation Biotechnology as a Civilizational Safeguard. Preprints. doi : https://doi.org/10.20944/preprints202511.1795.v1

Tkemaladze, J. (2025). The Heroic Self-Myth Hypothesis: A Neuro-Phenomenological Framework for Pathological Self-Narrativization in the Modernist Epoch. Preprints. doi : https://doi.org/10.20944/preprints202511.1774.v1

Tkemaladze, J. (2025). The Tkemaladze Method: Mapping Cell Lineage with Mutant Mitochondrial Transfer. Preprints. doi : https://doi.org/10.20944/preprints202509.2586.v1

Tkemaladze, J. (2025). Uznadze’s Theory of Set: Experimental Diagnostics and Neurocognitive Implications. Preprints. doi : https://doi.org/10.20944/preprints202511.1006.v1

Tkemaladze, J. (2023). Cross-senolytic effects of dasatinib and quercetin in humans. Georgian Scientists, 5(3), 138–152. doi : https://doi.org/10.52340/2023.05.03.15

Tkemaladze, J. (2023). Is the selective accumulation of oldest centrioles in stem cells the main cause of organism ageing?. Georgian Scientists, 5(3), 216–235. doi : https://doi.org/10.52340/2023.05.03.22

Tkemaladze, J. (2023). Long-Term Differences between Regenerations of Head and Tail Fragments in Schmidtea Mediterranea Ciw4. Available at SSRN 4257823.

Tkemaladze, J. (2023). Reduction, proliferation, and differentiation defects of stem cells over time: a consequence of selective accumulation of old centrioles in the stem cells?. Molecular Biology Reports, 50(3), 2751-2761. doi : https://pubmed.ncbi.nlm.nih.gov/36583780/

Tkemaladze, J. (2025). Ze-HB Hierarchical Bayesian Extension of the Ze. Preprints. doi : https://doi.org/10.20944/preprints202512.0103.v1

Tkemaladze, J. (2023). Structure and possible functions of centriolar RNA with reference to the centriolar hypothesis of differentiation and replicative senescence. Junior Researchers, 1(1), 156–170. doi : https://doi.org/10.52340/2023.01.01.17

Tkemaladze, J. (2023). The centriolar hypothesis of differentiation and replicative senescence. Junior Researchers, 1(1), 123–141. doi : https://doi.org/10.52340/2023.01.01.15

Tkemaladze, J. (2024). Absence of centrioles and regenerative potential of planaria. Georgian Scientists, 6(4), 59–75. doi : https://doi.org/10.52340/gs.2024.06.04.08

Tkemaladze, J. (2024). Cell center and the problem of accumulation of oldest centrioles in stem cells. Georgian Scientists, 6(2), 304–322. doi : https://doi.org/10.52340/gs.2024.06.02.32

Tkemaladze, J. (2024). Editorial: Molecular mechanism of ageing and therapeutic advances through targeting glycative and oxidative stress. Front Pharmacol. 2024 Mar 6;14:1324446. doi : 10.3389/fphar.2023.1324446. PMID: 38510429; PMCID: PMC10953819.

Tkemaladze, J. (2024). Elimination of centrioles. Georgian Scientists, 6(4), 291–307. doi : https://doi.org/10.52340/gs.2024.06.04.2

Tkemaladze, J. (2024). Main causes of intelligence decrease and prospects for treatment. Georgian Scientists, 6(2), 425–432. doi : https://doi.org/10.52340/gs.2024.06.02.44

Tkemaladze, J. (2024). The rate of stem cell division decreases with age. Georgian Scientists, 6(4), 228–242. doi : https://doi.org/10.52340/gs.2024.06.04.21

Tkemaladze, J. (2025). A Universal Approach to Curing All Diseases: From Theoretical Foundations to the Prospects of Applying Modern Biotechnologies in Future Medicine. doi : http://dx.doi.org/10.13140/RG.2.2.24481.11366

Tkemaladze, J. (2025). Adaptive Systems and World Models. doi : http://dx.doi.org/10.13140/RG.2.2.13617.90720

Tkemaladze, J. (2025). Allotransplantation Between Adult Drosophila of Different Ages and Sexes. doi : http://dx.doi.org/10.13140/RG.2.2.27711.62884

Tkemaladze, J. (2025). Anti-Blastomic Substances in the Blood Plasma of Schizophrenia Patients. doi : http://dx.doi.org/10.13140/RG.2.2.12721.08807

Tkemaladze, J. (2025). Centriole Elimination as a Mechanism for Restoring Cellular Order. doi : http://dx.doi.org/10.13140/RG.2.2.12890.66248/1

Tkemaladze, J. (2025). Hypotheses on the Role of Centrioles in Aging Processes. doi : http://dx.doi.org/10.13140/RG.2.2.15014.02887/1

Tkemaladze, J. (2025). Limits of Cellular Division: The Hayflick Phenomenon. doi : http://dx.doi.org/10.13140/RG.2.2.25803.30249

Tkemaladze, J. (2025). Molecular Mechanisms of Aging and Modern Life Extension Strategies: From Antiquity to Mars Colonization. doi : http://dx.doi.org/10.13140/RG.2.2.13208.51204

Tkemaladze, J. (2025). Pathways of Somatic Cell Specialization in Multicellular Organisms. doi : http://dx.doi.org/10.13140/RG.2.2.23348.97929/1

Tkemaladze, J. (2025). Strategic Importance of the Caucasian Bridge and Global Power Rivalries. doi : http://dx.doi.org/10.13140/RG.2.2.19153.03680

Tkemaladze, J. (2025). The Epistemological Reconfiguration and Transubstantial Reinterpretation of Eucharistic Practices Established by the Divine Figure of Jesus Christ in Relation to Theological Paradigms. doi : http://dx.doi.org/10.13140/RG.2.2.28347.73769/1

Tkemaladze, J. (2025). Transforming the psyche with phoneme frequencies "Habere aliam linguam est possidere secundam animam". doi : http://dx.doi.org/10.13140/RG.2.2.16105.61286

Tkemaladze, J. (2025). Uneven Centrosome Inheritance and Its Impact on Cell Fate. doi : http://dx.doi.org/10.13140/RG.2.2.34917.31206

Tkemaladze, J. (2025). Ze World Model with Predicate Actualization and Filtering. doi : http://dx.doi.org/10.13140/RG.2.2.15218.62407

Tkemaladze, J. (2025). Ze метод создания пластичного счетчика хронотропных частот чисел бесконечного потока информации. doi : http://dx.doi.org/10.13140/RG.2.2.29162.43207

Tkemaladze, J. (2025). A Novel Integrated Bioprocessing Strategy for the Manufacturing of Shelf-Stable, Nutritionally Upgraded Activated Wheat: Development of a Comprehensive Protocol, In-Depth Nutritional Characterization, and Evaluation of Biofunctional Properties. Longevity Horizon, 1(3). doi : https://doi.org/10.5281/zenodo.16950787

Tkemaladze, J. (2025). Achieving Perpetual Vitality Through Innovation. doi : http://dx.doi.org/10.13140/RG.2.2.31113.35685

Tkemaladze, J. (2025). Activated Wheat: The Power of Super Grains. Preprints. doi : https://doi.org/10.20944/preprints202508.1724.v1

Tkemaladze, J. (2025). Adaptive Cognitive System Ze. Longevity Horizon, 1(3). doi : https://doi.org/10.5281/zenodo.15309162

Tkemaladze, J. (2025). Aging Model Based on Drosophila melanogaster: Mechanisms and Perspectives. Longevity Horizon, 1(3). doi : https://doi.org/10.5281/zenodo.14955643

Tkemaladze, J. (2025). Aging Model-Drosophila Melanogaster. doi : http://dx.doi.org/10.13140/RG.2.2.16706.49607

Tkemaladze, J. (2025). An Interdisciplinary Study on the Causes of Antediluvian Longevity, the Postdiluvian Decline in Lifespan, and the Phenomenon of Job’s Life Extension. Preprints. doi : https://doi.org/10.20944/preprints202509.1476.v1

Tkemaladze, J. (2025). Anatomy, Biogenesis, and Role in Cell Biology of Centrioles. Longevity Horizon, 1(2). doi : https://doi.org/10.5281/zenodo.14742232

Tkemaladze, J. (2025). Anti-Blastomic Substances in the Plasma of Schizophrenia Patients: A Dual Role of Complement C4 in Synaptic Pruning and Tumor Suppression. Longevity Horizon, 1(3). doi : https://doi.org/10.5281/zenodo.15042448

Tkemaladze, J. (2025). Asymmetry in the Inheritance of Centrosomes/Centrioles and Its Consequences. Longevity Horizon, 1(2). doi : https://doi.org/10.5281/zenodo.14837352

Tkemaladze, J. (2025). Bayesian Order in Ze. Longevity Horizon, 1(4). doi : https://doi.org/10.5281/zenodo.17359987

Tkemaladze, J. (2025). Centriole Elimination: A Mechanism for Resetting Entropy in the Cell. Longevity Horizon, 1(2). doi : https://doi.org/10.5281/zenodo.14876013

Tkemaladze, J. (2025). Concept of Death Awareness as an Existential Regulator in the Age of Biological Immortality. Longevity Horizon, 1(4). doi : https://doi.org/10.5281/zenodo.17340207

Tkemaladze, J. (2025). Concept to The Alive Language. Longevity Horizon, 1(1). doi : https://doi.org/10.5281/zenodo.14688792

Tkemaladze, J. (2025). Concept to The Caucasian Bridge. Longevity Horizon, 1(1). doi : https://doi.org/10.5281/zenodo.17643013

Tkemaladze, J. (2025). Concept to The Curing All Diseases. Longevity Horizon, 1(1). doi : https://doi.org/10.5281/zenodo.14676208

Tkemaladze, J. (2025). Concept to The Eternal Youth. Longevity Horizon, 1(1). doi : https://doi.org/10.5281/zenodo.14681902

Tkemaladze, J. (2025). Concept to The Food Security. Longevity Horizon, 1(1). doi : https://doi.org/10.5281/zenodo.14642407

Tkemaladze, J. (2025). Concept to the Living Space. Longevity Horizon, 1(1). doi : https://doi.org/10.5281/zenodo.14635991

Tkemaladze, J. (2025). Concept to The Restoring Dogmas. Longevity Horizon, 1(1). doi : https://doi.org/10.5281/zenodo.14708980

Tkemaladze, J. (2025). Differentiation of Somatic Cells in Multicellular Organisms. Longevity Horizon, 1(2). doi : https://doi.org/10.5281/10.5281/zenodo.14778927

Tkemaladze, J. (2025). Direct Reprogramming of Somatic Cells to Functional Gametes in Planarians via a Novel In Vitro Gametogenesis Protocol. Preprints. doi : https://doi.org/10.20944/preprints202509.1071.v1

Tkemaladze, J. (2025). Induction of germline-like cells (PGCLCs). Longevity Horizon, 1(3). doi : https://doi.org/10.5281/zenodo.16414775

Tkemaladze, J. (2025). Lake Aquaculture for Catastrophic Food Security. Longevity Horizon, 1(4). doi : https://doi.org/10.5281/zenodo.17454164

Tkemaladze, J. (2025). Long-Lived Non-Renewable Structures in the Human Body. doi : http://dx.doi.org/10.13140/RG.2.2.14826.43206

Tkemaladze, J. (2025). Mechanisms of Learning Through the Actualization of Discrepancies. Longevity Horizon, 1(3). doi : https://doi.org/10.5281/zenodo.15200612

Tkemaladze, J. (2025). Memorizing an Infinite Stream of Information in a Limited Memory Space: The Ze Method of a Plastic Counter of Chronotropic Number Frequencies. Longevity Horizon, 1(3). doi : https://doi.org/10.5281/zenodo.15170931

Tkemaladze, J. (2025). Molecular Insights and Radical Longevity from Ancient Elixirs to Mars Colonies. Longevity Horizon, 1(2). doi : https://doi.org/10.5281/zenodo.14895222

Tkemaladze, J. (2025). Ontogenetic Permanence of Non-Renewable Biomechanical Configurations in Homo Sapiens Anatomy. Longevity Horizon, 1(3). doi : https://doi.org/10.5281/zenodo.15086387

Tkemaladze, J. (2025). Protocol for Transplantation of Healthy Cells Between Adult Drosophila of Different Ages and Sexes. Longevity Horizon, 1(2). doi : https://doi.org/10.5281/zenodo.14889948

Tkemaladze, J. (2025). Replicative Hayflick Limit. Longevity Horizon, 1(2). doi : https://doi.org/10.5281/zenodo.14752664

Tkemaladze, J. (2025). Solutions to the Living Space Problem to Overcome the Fear of Resurrection from the Dead. doi : http://dx.doi.org/10.13140/RG.2.2.34655.57768

Tkemaladze, J. (2025). The Centriolar Theory of Differentiation Explains the Biological Meaning of the Centriolar Theory of Organismal Aging. Longevity Horizon, 1(3). doi : https://doi.org/10.5281/zenodo.14897688

Tkemaladze, J. (2025). The Centriolar Theory of Differentiation Explains the Biological Meaning of the.

Tkemaladze, J. (2025). The Concept of Data-Driven Automated Governance. Georgian Scientists, 6(4), 399–410. doi : https://doi.org/10.52340/gs.2024.06.04.38

Tkemaladze, J. (2025). The Stage of Differentiation Into Mature Gametes During Gametogenesis in Vitro. Longevity Horizon, 1(3). doi : https://doi.org/10.5281/zenodo.16808827

Tkemaladze, J. (2025). The Tkemaladze Method Maps Cell Lineage with Mutant Mitochondrial Transfer. Longevity Horizon, 1(4). doi : https://doi.org/10.5281/zenodo.17236869

Tkemaladze, J. (2025). The Tkemaladze Method: A Modernized Caucasian Technology for the Production of Shelf-Stable Activated Wheat with Enhanced Nutritional Properties. Longevity Horizon, 1(3). doi : https://doi.org/10.5281/zenodo.16905079

Tkemaladze, J. (2025). Theory of Lifespan Decline. Longevity Horizon, 1(3). doi : https://doi.org/10.5281/zenodo.17142909

Tkemaladze, J. (2025). Through In Vitro Gametogenesis—Young Stem Cells. Longevity Horizon, 1(3). doi : https://doi.org/10.5281/zenodo.15847116

Tkemaladze, J. (2025). Tkemaladze, J. (2025). The Centriole Paradox in Planarian Biology: Why Acentriolar Stem Cells Divide and Centriolar Somatic Cells Do Not. Preprints. doi : https://doi.org/10.20944/preprints202509.0382.v1

Tkemaladze, J. (2025). Unlocking the Voynich Cipher via the New Algorithmic Coding Hypothesis. Longevity Horizon, 1(3). doi : https://doi.org/10.5281/zenodo.17054312

Tkemaladze, J. (2025). Uznadze Set Revisited. Longevity Horizon, 1(4). doi : https://doi.org/10.5281/zenodo.17609772

Tkemaladze, J. (2025). Voynich Manuscript Decryption: A Novel Compression-Based Hypothesis and Computational Framework. Preprints. doi : https://doi.org/10.20944/preprints202509.0403.v1

Tkemaladze, J. (2025). Why do planarian cells without centrioles divide and cells with centrioles do not divide?. Longevity Horizon, 1(3). doi : https://doi.org/10.5281/zenodo.17054142

Tkemaladze, J. (2025). Гаметогенез In Vitro: современное состояние, технологии и перспективы применения. Research Gate. doi : http://dx.doi.org/10.13140/RG.2.2.28647.36000

Tkemaladze, J. Systemic Resilience and Sustainable Nutritional Paradigms in Anthropogenic Ecosystems. doi : http://dx.doi.org/10.13140/RG.2.2.18943.32169/1

Tkemaladze, J. V., & Chichinadze, K. N. (2005). Centriolar mechanisms of differentiation and replicative aging of higher animal cells. Biochemistry (Moscow), 70, 1288-1303.

Tkemaladze, J., & Apkhazava, D. (2019). Dasatinib and quercetin: short-term simultaneous administration improves physical capacity in human. J Biomedical Sci, 8(3), 3.

Tkemaladze, J., & Chichinadze, K. (2005). Potential role of centrioles in determining the morphogenetic status of animal somatic cells. Cell biology international, 29(5), 370-374.

Tkemaladze, J., & Chichinadze, K. (2010). Centriole, differentiation, and senescence. Rejuvenation research, 13(2-3), 339-342.

Tkemaladze, J., & Gakely, G. (2025). A Novel Biotechnological Approach for the Production of Shelf-Stable, Nutritionally Enhanced Activated Wheat: Protocol Development, Nutritional Profiling, and Bioactivity Assessment. doi : https://doi.org/10.20944/preprints202508.1997.v1

Tkemaladze, J., & Gakely, G. (2025). Induction of de novo centriole biogenesis in planarian stem cells. Longevity Horizon, 1(4). doi : https://doi.org/10.5281/zenodo.17283229

Tkemaladze, J., & Samanishvili, T. (2024). Mineral ice cream improves recovery of muscle functions after exercise. Georgian Scientists, 6(2), 36–50. doi : https://doi.org/10.52340/gs.2024.06.02.04

Tkemaladze, J., Gakely, G., Gegelia, L., Papadopulo, I., Taktakidze, A., Metreveli, N., ... & Maglakelidze, U. (2025). Production of Functional Gametes from Somatic Cells of the Planarian Schmidtea Mediterranea Via in Vitro Gametogenesis. Longevity Horizon, 1(3). doi : https://doi.org/10.5281/zenodo.17131291

Tkemaladze, J., Tavartkiladze, A., & Chichinadze, K. (2012). Programming and Implementation of Age-Related Changes. In Senescence. IntechOpen.

Tkemaladze, Jaba and Kipshidze, Mariam, Regeneration Potential of the Schmidtea Mediterranea CIW4 Planarian. Available at SSRN: https://ssrn.com/abstract=4633202 or http://dx.doi.org/10.2139/ssrn.4633202

Unwin, A., Theus, M., & Hofmann, H. (2006). Graphics of large datasets: visualizing a million. Springer Science & Business Media.

van de Schoot, R., Depaoli, S., King, R., Kramer, B., Märtens, K., Tadesse, M. G., ... & Crisp, N. (2021). Bayesian statistics and modelling. Nature Reviews Methods Primers, 1(1), 1-26.

Vilalta, R., & Drissi, Y. (2002). A perspective view and survey of meta-learning. Artificial Intelligence Review, 18(2), 77-95.

Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., ... & van der Walt, S. J. (2020). SciPy 1.0: fundamental algorithms for scientific computing in Python. Nature Methods, 17(3), 261–272. https://doi.org/10.1038/s41592-019-0686-2

Wang, K., Gaitsch, H., Poon, H., Cox, N. J., & Rzhetsky, A. (2016). Classification of common human diseases derived from shared genetic and environmental determinants. Nature Genetics, 49(9), 1319-1325.

Wang, K., Gaitsch, H., Poon, H., Cox, N. J., & Rzhetsky, A. (2017). Classification of common human diseases derived from shared genetic and environmental determinants. Nature Genetics, 49(9), 1319–1325. https://doi.org/10.1038/ng.3931

Wasserman, L. (2008). All of nonparametric statistics. Springer Science & Business Media.

West, M., & Harrison, J. (1997). Bayesian forecasting and dynamic models. Springer Science & Business Media.

Wilson, G., Bryan, J., Cranston, K., Kitzes, J., Nederbragt, L., & Teal, T. K. (2017). Good enough practices in scientific computing. PLoS Computational Biology, 13(6), e1005510.

Wilson, G., Bryan, J., Cranston, K., Kitzes, J., Nederbragt, L., & Teal, T. K. (2017). Good enough practices in scientific computing. PLoS Computational Biology, 13(6), e1005510. https://doi.org/10.1371/journal.pcbi.1005510

Wingender, E., Dietze, P., Karas, H., & Knüppel, R. (1996). TRANSFAC: a database on transcription factors and their DNA binding sites. Nucleic Acids Research, 24(1), 238-241.

Wolpert, D. H. (1992). Stacked generalization. Neural Networks, 5(2), 241-259.

Zliobaite, I., Pechenizkiy, M., & Gama, J. (2016). An overview of concept drift applications. In Big Data Analysis: New Algorithms for a New Society (pp. 91-114). Springer, Cham.

Žliobaite, I., Pechenizkiy, M., & Gama, J. (2016). An overview of concept drift applications. In Big Data Analysis: New Algorithms for a New Society (pp. 91-114). Springer, Cham.

Прангишвили, А. И., Гаситашвили, З. А., Мацаберидзе, М. И., Чичинадзе, К. Н., Ткемаладзе, Д. В., & Азмайпарашвили, З. А. (2017). К топологии антитеррористических и антикриминальных технологии для образовательных программ. В научном издании представлены материалы Десятой международной научно-технической конфе-ренции «Управление развитием крупномасштабных систем (MLSD’2016)» по следующим направле-ниям:• Проблемы управления развитием крупномасштабных систем, включая ТНК, Госхолдин-ги и Гос-корпорации., 284.

Прангишвили, А. И., Гаситашвили, З. А., Мацаберидзе, М. И., Чхартишвили, Л. С., Чичинадзе, К. Н., & Ткемаладзе, Д. В. (2017). & Азмайпарашвили, ЗА (2017). Системные составляющие здравоохранения и инноваций для организации европейской нано-биомедицинской екосистемной технологической платформы. Управление развитием крупномасштабных систем MLSD, 365-368.

Ткемаладзе, Д. (2025). Асимметрия в наследовании центросом/центриолей и ее последствия. doi : http://dx.doi.org/110.13140/RG.2.2.34917.31206

Ткемаладзе, Д. (2025). Гаметогенез in vitro (IVG)-Этап дифференцировки в зрелые гаметы. doi : http://dx.doi.org/10.13140/RG.2.2.20429.96482

Ткемаладзе, Д. (2025). Дифференциация соматических клеток многоклеточных животных. doi : http://dx.doi.org/10.13140/RG.2.2.23348.97929/1

Ткемаладзе, Д. (2025). Индукция примордиальных клеток, подобных зародышевым клеткам (PGCLCs) современные достижения, механизмы и перспективы применения. doi : http://dx.doi.org/10.13140/RG.2.2.27152.32004

Ткемаладзе, Д. (2025). Репликативный Лимит Хейфлика. doi : http://dx.doi.org/10.13140/RG.2.2.25803.30249

Ткемаладзе, Д. (2025). Элиминация Центриолей: Механизм Обнуления Энтропии в Клетке. doi : http://dx.doi.org/10.13140/RG.2.2.12890.66248/1

Ткемаладзе, Д. В., & Чичинадзе, К. Н. (2005). Центриолярные механизмы дифференцировки и репликативного старения клеток высших животных. Биохимия, 70(11), 1566-1584.

Ткемаладзе, Д., Цомаиа, Г., & Жоржолиани, И. (2001). Создание искусственных самоадаптирующихся систем на основе Теории Прогноза. Искусственный интеллект. УДК 004.89. Искусственный интеллект. УДК 004.89.

Чичинадзе, К. Н., & Ткемаладзе, Д. В. (2008). Центросомная гипотеза клеточного старения и дифференциации. Успехи геронтологии, 21(3), 367-371.

Чичинадзе, К., Ткемаладзе, Д., & Лазарашвили, А. (2012). Новый класс рнк и центросомная гипотеза старения клеток. Успехи геронтологии, 25(1), 23-28