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Journal articles on the topic 'Data driven model'

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Author: Grafiati

Published: 4 June 2021

Last updated: 9 March 2023

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1

Cho, Kunhee, Joahun Kim, Hyeongcheol Lee, Seung-Han You, and Wanki Cho. "Data-driven Based Accuracy Improvement for Vehicle Lateral Model." Transaction of the Korean Society of Automotive Engineers 30, no. 2 (February 1, 2022): 133–42. http://dx.doi.org/10.7467/ksae.2022.30.2.133.

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2

Eggersmann, R., T. Kirchdoerfer, S. Reese, L. Stainier, and M. Ortiz. "Model-Free Data-Driven inelasticity." Computer Methods in Applied Mechanics and Engineering 350 (June 2019): 81–99. http://dx.doi.org/10.1016/j.cma.2019.02.016.

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3

Hewitt, Mike, and Emma Frejinger. "Data-driven optimization model customization." European Journal of Operational Research 287, no. 2 (December 2020): 438–51. http://dx.doi.org/10.1016/j.ejor.2020.05.010.

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4

Matusik, Wojciech, Hanspeter Pfister, Matt Brand, and Leonard McMillan. "A data-driven reflectance model." ACM Transactions on Graphics 22, no. 3 (July 2003): 759–69. http://dx.doi.org/10.1145/882262.882343.

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5

Sorescu, Alina. "Data-Driven Business Model Innovation." Journal of Product Innovation Management 34, no. 5 (June 27, 2017): 691–96. http://dx.doi.org/10.1111/jpim.12398.

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6

Le-Tien, Thuong, Hanh Phan-Xuan, Thuy Nguyen-Chinh, and Thien Do-Tieu. "Image Forgery Detection: A Low Computational-Cost and Effective Data-Driven Model." International Journal of Machine Learning and Computing 9, no. 2 (April 2019): 181–88. http://dx.doi.org/10.18178/ijmlc.2019.9.2.784.

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7

Cuzzocrea, Alfredo, Jose Norberto Mazon, Juan Trujillo, and Jose Zubcoff. "Model-driven data mining engineering: from solution-driven implementations to 'composable' conceptual data mining models." International Journal of Data Mining, Modelling and Management 3, no. 3 (2011): 217. http://dx.doi.org/10.1504/ijdmmm.2011.041808.

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8

Wang, Nannan, Mingrui Zhu, Jie Li, Bin Song, and Zan Li. "Data-driven vs. model-driven: Fast face sketch synthesis." Neurocomputing 257 (September 2017): 214–21. http://dx.doi.org/10.1016/j.neucom.2016.07.071.

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9

CHE-JUNG, CHANG, LI GUIPING, GUO JIANHONG, and YU KUN-PENG. "Data-Driven Forecasting Model for Small Data Sets." ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH 54, no. 4/2020 (December 15, 2020): 217–29. http://dx.doi.org/10.24818/18423264/54.4.20.14.

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10

Forgione, Marco, Xavier Bombois, and Paul M. J. Van den Hof. "Data-driven model improvement for model-based control." Automatica 52 (February 2015): 118–24. http://dx.doi.org/10.1016/j.automatica.2014.11.006.

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Le, Trang, Sumeyye Su, Arkadz Kirshtein, and Leili Shahriyari. "Data-Driven Mathematical Model of Osteosarcoma." Cancers 13, no. 10 (May 14, 2021): 2367. http://dx.doi.org/10.3390/cancers13102367.

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As the immune system has a significant role in tumor progression, in this paper, we develop a data-driven mathematical model to study the interactions between immune cells and the osteosarcoma microenvironment. Osteosarcoma tumors are divided into three clusters based on their relative abundance of immune cells as estimated from their gene expression profiles. We then analyze the tumor progression and effects of the immune system on cancer growth in each cluster. Cluster 3, which had approximately the same number of naive and M2 macrophages, had the slowest tumor growth, and cluster 2, with the highest population of naive macrophages, had the highest cancer population at the steady states. We also found that the fastest growth of cancer occurred when the anti-tumor immune cells and cytokines, including dendritic cells, helper T cells, cytotoxic cells, and IFN-γ, switched from increasing to decreasing, while the dynamics of regulatory T cells switched from decreasing to increasing. Importantly, the most impactful immune parameters on the number of cancer and total cells were the activation and decay rates of the macrophages and regulatory T cells for all clusters. This work presents the first osteosarcoma progression model, which can be later extended to investigate the effectiveness of various osteosarcoma treatments.

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Kheradmandi, Masoud, and Prashant Mhaskar. "Data Driven Economic Model Predictive Control." Mathematics 6, no. 4 (April 2, 2018): 51. http://dx.doi.org/10.3390/math6040051.

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13

Zhao, Hongbo, and Bingrui Chen. "Data-Driven Model for Rockburst Prediction." Mathematical Problems in Engineering 2020 (August 17, 2020): 1–14. http://dx.doi.org/10.1155/2020/5735496.

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Rockburst is an extremely complex dynamic instability phenomenon for rock engineering. Due to the complex and unclear mechanism of rockburst, it is difficult to predict precisely and evaluate reasonably the potential of rockburst. With the development of data science and increasing of case history from rock engineering, the data-driven method provides a good way to mine the complex phenomenon of rockburst and then was used to predict the potential of rockburst. In this study, deep learning was adopted to build the data-driven model of rockburst prediction based on the rockburst datasets collected from the literature. The data-driven model was built based on a convolutional neural network (CNN) and compared with the traditional neural network. The results show that the data-driven model can effectively mine the complex phenomenon and mechanism of rockburst. And the proposed method not only can predict the rank of rockburst but also can compute the probability of rockburst for each corresponding rank. It provides a promising and reasonable approach to predict or evaluate the rockburst.

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14

Scarciotti, Giordano, Zhong-Ping Jiang, and Alessandro Astolfi. "Data-driven constrained optimal model reduction." European Journal of Control 53 (May 2020): 68–78. http://dx.doi.org/10.1016/j.ejcon.2019.10.006.

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15

Califano, Andrea, Rick Kjeldsen, and Ruud M. Bolle. "Data- and Model-Driven Multiresolution Processing." Computer Vision and Image Understanding 63, no. 1 (January 1996): 27–49. http://dx.doi.org/10.1006/cviu.1996.0003.

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16

Renardy, Marissa, Timothy Wessler, Silvia Blemker, Jennifer Linderman, Shayn Peirce, and Denise Kirschner. "Data-Driven Model Validation Across Dimensions." Bulletin of Mathematical Biology 81, no. 6 (March 4, 2019): 1853–66. http://dx.doi.org/10.1007/s11538-019-00590-4.

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Qiang Zhang, Qiang Zhang. "A Novel Data-Driven Rolling Force Predictive Model Based on PSO-BAS-HKSVR Approach." 電腦學刊 32, no. 4 (August 2021): 025–41. http://dx.doi.org/10.53106/199115992021083204003.

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18

Tan, Yi, Yuanyang Chen, Yong Li, and Yijia Cao. "Linearizing Power Flow Model: A Hybrid Physical Model-Driven and Data-Driven Approach." IEEE Transactions on Power Systems 35, no. 3 (May 2020): 2475–78. http://dx.doi.org/10.1109/tpwrs.2020.2975455.

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19

Jianhong, Wang. "Dynamic Programming in Data Driven Model Predictive Control?" WSEAS TRANSACTIONS ON SYSTEMS 20 (July 21, 2021): 170–77. http://dx.doi.org/10.37394/23202.2021.20.19.

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In this short note, one data driven model predictive control is studied to design the optimal control sequence. The idea of data driven means the actual output value in cost function for model predictive control is identi_ed through input-output observed data in case of unknown but bounded noise and martingale di_erence sequence. After substituting the identi_ed actual output in cost function, the total cost function in model predictive control is reformulated as the other standard form, so that dynamic programming can be applied directly. As dynamic programming is only used in optimization theory, so to extend its advantage in control theory, dynamic programming algorithm is proposed to construct the optimal control sequence. Furthermore, stability analysis for data drive model predictive control is also given based on dynamic programming strategy. Generally, the goal of this short note is to bridge the dynamic programming, system identi_cation and model predictive control. Finally, one simulation example is used to prove the e_ciency of our proposed theory

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20

Zhao, Wentao, Qian Li, Chengzhang Zhu, Jianglong Song, Xinwang Liu, and Jianping Yin. "Model-aware categorical data embedding: a data-driven approach." Soft Computing 22, no. 11 (April 27, 2018): 3603–19. http://dx.doi.org/10.1007/s00500-018-3170-5.

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21

Giancarlo, Raffaele, Giosué Lo Bosco, and Filippo Utro. "Bayesian versus data driven model selection for microarray data." Natural Computing 14, no. 3 (July 16, 2014): 393–402. http://dx.doi.org/10.1007/s11047-014-9446-5.

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22

Pathiraja, S., H. Moradkhani, L. Marshall, A. Sharma, and G. Geenens. "Data-Driven Model Uncertainty Estimation in Hydrologic Data Assimilation." Water Resources Research 54, no. 2 (February 2018): 1252–80. http://dx.doi.org/10.1002/2018wr022627.

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23

Jin, Xue Bo, Jing Jing Du, and Jia Bao. "Data-Driven Tracking Based on Kalman Filter." Applied Mechanics and Materials 226-228 (November 2012): 2476–79. http://dx.doi.org/10.4028/www.scientific.net/amm.226-228.2476.

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A good model of the target will extract useful information about the target’s state from observations effectively. There are many models used to maneuvering target tracking, such as constant-velocity (CV) models, Singer acceleration model (zero-mean first-order Markov model) and “current” model (Mean-Adaptive Acceleration Model), etc. While due to the complexity of maneuvering target, to seek the target model which can get better performance is still a subject worthy of study. For the AR process, autocorrelation function is estimated by the random sampling points in this paper. We have the statistics relation between the autocorrelation function and variance based on a first-order stationary Markov process. Then the system parameters are obtained and a model is developed based on statistics relation, which needn’t set unknown parameter. Simulation shows the model developed can adaptively get the model parameter and obtain good performance.

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24

Verma, Nishchal K., and Madasu Hanmandlu. "Data driven model using adaptive fuzzy system." International Journal of Automation and Control 2, no. 4 (2008): 447. http://dx.doi.org/10.1504/ijaac.2008.022896.

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25

Gomez, A., A. Boronat, J. A. Carsi, I. Ramos, C. Taubner, and S. Eckstein. "Biological Data Processing using Model Driven Engineering." IEEE Latin America Transactions 6, no. 4 (August 2008): 324–31. http://dx.doi.org/10.1109/tla.2008.4815285.

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26

Choudhury, M. A. A. S., N. F. Thornhill, and S. L. Shah. "A Data-Driven Model for Valve Stiction." IFAC Proceedings Volumes 37, no. 1 (January 2004): 245–50. http://dx.doi.org/10.1016/s1474-6670(17)38739-6.

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27

Hu, Guang-Zhong, Xin-Jian Xu, Shou-Ne Xiao, Guang-Wu Yang, and Fan Pu. "Product Data Model for Performance-driven Design." Chinese Journal of Mechanical Engineering 30, no. 5 (August 4, 2017): 1112–22. http://dx.doi.org/10.1007/s10033-017-0173-6.

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28

Zdravković, Milan, and Ricardo Jardim-Gonçalves. "Model-driven data-intensive Enterprise Information Systems." Enterprise Information Systems 12, no. 8-9 (October 4, 2018): 910–14. http://dx.doi.org/10.1080/17517575.2018.1526327.

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29

Messaris, Ioannis, Alexander Serb, Spyros Stathopoulos, Ali Khiat, Spyridon Nikolaidis, and Themistoklis Prodromakis. "A Data-Driven Verilog-A ReRAM Model." IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 37, no. 12 (December 2018): 3151–62. http://dx.doi.org/10.1109/tcad.2018.2791468.

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30

Mukherjee, Tridib, Ayan Banerjee, Georgios Varsamopoulos, and Sandeep K. S. Gupta. "Model-driven coordinated management of data centers." Computer Networks 54, no. 16 (November 2010): 2869–86. http://dx.doi.org/10.1016/j.comnet.2010.08.011.

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31

Gryning, Sven-Erik, and Rogier Floors. "Investigating predictability of offshore winds using a mesoscale model driven by forecast and reanalysis data." Meteorologische Zeitschrift 29, no. 2 (August 4, 2020): 117–30. http://dx.doi.org/10.1127/metz/2019/1002.

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32

Wu, Zhong, Qi Wang, JianXiong Hu, Yi Tang, and YuNan Zhang. "Integrating model-driven and data-driven methods for fast state estimation." International Journal of Electrical Power & Energy Systems 139 (July 2022): 107982. http://dx.doi.org/10.1016/j.ijepes.2022.107982.

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33

Gibanica, Mladen, and Thomas J. S. Abrahamsson. "Data-driven modal surrogate model for frequency response uncertainty propagation." Probabilistic Engineering Mechanics 66 (October 2021): 103142. http://dx.doi.org/10.1016/j.probengmech.2021.103142.

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34

Peherstorfer, Benjamin, Serkan Gugercin, and Karen Willcox. "Data-Driven Reduced Model Construction with Time-Domain Loewner Models." SIAM Journal on Scientific Computing 39, no. 5 (January 2017): A2152—A2178. http://dx.doi.org/10.1137/16m1094750.

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35

Petersen, Anne H., Merete Osler, and Claus T. Ekstrøm. "Data-Driven Model Building for Life-Course Epidemiology." American Journal of Epidemiology 190, no. 9 (March 29, 2021): 1898–907. http://dx.doi.org/10.1093/aje/kwab087.

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Abstract Life-course epidemiology is useful for describing and analyzing complex etiological mechanisms for disease development, but existing statistical methods are essentially confirmatory, because they rely on a priori model specification. This limits the scope of causal inquiries that can be made, because these methods are suited mostly to examine well-known hypotheses that do not question our established view of health, which could lead to confirmation bias. We propose an exploratory alternative. Instead of specifying a life-course model prior to data analysis, our method infers the life-course model directly from the data. Our proposed method extends the well-known Peter-Clark (PC) algorithm (named after its authors) for causal discovery, and it facilitates including temporal information for inferring a model from observational data. The extended algorithm is called temporal PC. The obtained life-course model can afterward be perused for interesting causal hypotheses. Our method complements classical confirmatory methods and guides researchers in expanding their models in new directions. We showcase the method using a data set encompassing almost 3,000 Danish men followed from birth until age 65 years. Using this data set, we inferred life-course models for the role of socioeconomic and health-related factors on development of depression.

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36

Mount, N. J., C. W. Dawson, and R. J. Abrahart. "Legitimising data-driven models: exemplification of a new data-driven mechanistic modelling framework." Hydrology and Earth System Sciences 17, no. 7 (July 17, 2013): 2827–43. http://dx.doi.org/10.5194/hess-17-2827-2013.

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Abstract. In this paper the difficult problem of how to legitimise data-driven hydrological models is addressed using an example of a simple artificial neural network modelling problem. Many data-driven models in hydrology have been criticised for their black-box characteristics, which prohibit adequate understanding of their mechanistic behaviour and restrict their wider heuristic value. In response, presented here is a new generic data-driven mechanistic modelling framework. The framework is significant because it incorporates an evaluation of the legitimacy of a data-driven model's internal modelling mechanism as a core element in the modelling process. The framework's value is demonstrated by two simple artificial neural network river forecasting scenarios. We develop a novel adaptation of first-order partial derivative, relative sensitivity analysis to enable each model's mechanistic legitimacy to be evaluated within the framework. The results demonstrate the limitations of standard, goodness-of-fit validation procedures by highlighting how the internal mechanisms of complex models that produce the best fit scores can have lower mechanistic legitimacy than simpler counterparts whose scores are only slightly inferior. Thus, our study directly tackles one of the key debates in data-driven, hydrological modelling: is it acceptable for our ends (i.e. model fit) to justify our means (i.e. the numerical basis by which that fit is achieved)?

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Gupta, Shobhit, Olivia Jacome, Stephanie Stockar, and Marcello Canova. "Data-driven Driver Model for Speed Advisory Systems in Partially Automated Vehicles." IFAC-PapersOnLine 55, no. 37 (2022): 706–11. http://dx.doi.org/10.1016/j.ifacol.2022.11.265.

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38

Eggersmann, Robert, Laurent Stainier, Michael Ortiz, and Stefanie Reese. "Efficient data structures for model-free data-driven computational mechanics." Computer Methods in Applied Mechanics and Engineering 382 (August 2021): 113855. http://dx.doi.org/10.1016/j.cma.2021.113855.

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39

Biagi, Vittoria, and Angela Russo. "Data Model Design to Support Data-Driven IT Governance Implementation." Technologies 10, no. 5 (October 8, 2022): 106. http://dx.doi.org/10.3390/technologies10050106.

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Organizations must quickly adapt their processes to understand the dynamic nature of modern business environments. As highlighted in the literature, centralized governance supports decision-making and performance measurement processes in technology companies. For this reason, a reliable decision-making system with an integrated data model that enables the rapid collection and transformation of data stored in heterogeneous and different sources is needed. Therefore, this paper proposes the design of a data model to implement data-driven governance through a literature review of adopted approaches. The lack of a standardized procedure and a disconnection between theoretical frameworks and practical application has emerged. This paper documented the suggested approach following these steps: (i) mapping of monitoring requirements to the data structure, (ii) documentation of ER diagram design, and (iii) reporting dashboards used for monitoring and reporting. The paper helped fill the gaps highlighted in the literature by supporting the design and development of a DWH data model coupled with a BI system. The application prototype shows benefits for top management, particularly those responsible for governance and operations, especially for risk monitoring, audit compliance, communication, knowledge sharing on strategic areas of the company, and identification and implementation of performance improvements and optimizations.

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Gökalp, Mert Onuralp, Ebru Gökalp, Kerem Kayabay, Altan Koçyiğit, and P. Erhan Eren. "Data-driven manufacturing: An assessment model for data science maturity." Journal of Manufacturing Systems 60 (July 2021): 527–46. http://dx.doi.org/10.1016/j.jmsy.2021.07.011.

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41

Sá, Jorge Oliveira e., and Maribel Yasmina Santos. "Process-driven data analytics supported by a data warehouse model." International Journal of Business Intelligence and Data Mining 12, no. 4 (2017): 383. http://dx.doi.org/10.1504/ijbidm.2017.086986.

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Oliveira e Sá, Jorge, and Maribel Yasmina Santos. "Process-driven Data Analytics supported by a Data Warehouse Model." International Journal of Business Intelligence and Data Mining 1, no. 1 (2017): 1. http://dx.doi.org/10.1504/ijbidm.2017.10004786.

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43

Cech, Thomas G., Trent J. Spaulding, and Joseph A. Cazier. "Data competence maturity: developing data-driven decision making." Journal of Research in Innovative Teaching & Learning 11, no. 2 (August 10, 2018): 139–58. http://dx.doi.org/10.1108/jrit-03-2018-0007.

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Purpose The purpose of this paper is to lay out the data competence maturity model (DCMM) and discuss how the application of the model can serve as a foundation for a measured and deliberate use of data in secondary education. Design/methodology/approach Although the model is new, its implications, and its application are derived from key findings and best practices from the software development, data analytics and secondary education performance literature. These principles can guide educators to better manage student and operational outcomes. This work builds and applies the DCMM model to secondary education. Findings The conceptual model reveals significant opportunities to improve data-driven decision making in schools and local education agencies (LEAs). Moving past the first and second stages of the data competency maturity model should allow educators to better incorporate data into the regular decision-making process. Practical implications Moving up the DCMM to better integrate data into their decision-making process has the potential to produce profound improvements for schools and LEAs. Data science is about making better decisions. Understanding the path laid out in the DCMM to helping an organization move to a more mature data-driven decision-making process will help improve both student and operational outcomes. Originality/value This paper brings a new concept, the DCMM, to the educational literature and discusses how these principles can be applied to improve decision making by integrating them into their decision-making process and trying to help the organization mature within this framework.

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44

Khudhair, Basim Hussein, Awatif Soaded Alsaqqar, and Rehab Karim Jbbar. "Performance Evaluation of Al-Karkh Water Treatment Plant Using Model-driven and Data-Driven Models." IOP Conference Series: Earth and Environmental Science 779, no. 1 (June 1, 2021): 012110. http://dx.doi.org/10.1088/1755-1315/779/1/012110.

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45

Lu, Fei. "Data-Driven Model Reduction for Stochastic Burgers Equations." Entropy 22, no. 12 (November 30, 2020): 1360. http://dx.doi.org/10.3390/e22121360.

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We present a class of efficient parametric closure models for 1D stochastic Burgers equations. Casting it as statistical learning of the flow map, we derive the parametric form by representing the unresolved high wavenumber Fourier modes as functionals of the resolved variable’s trajectory. The reduced models are nonlinear autoregression (NAR) time series models, with coefficients estimated from data by least squares. The NAR models can accurately reproduce the energy spectrum, the invariant densities, and the autocorrelations. Taking advantage of the simplicity of the NAR models, we investigate maximal space-time reduction. Reduction in space dimension is unlimited, and NAR models with two Fourier modes can perform well. The NAR model’s stability limits time reduction, with a maximal time step smaller than that of the K-mode Galerkin system. We report a potential criterion for optimal space-time reduction: the NAR models achieve minimal relative error in the energy spectrum at the time step, where the K-mode Galerkin system’s mean Courant–Friedrichs–Lewy (CFL) number agrees with that of the full model.

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46

Freestone, Dean R., Kelvin J. Layton, Levin Kuhlmann, and Mark J. Cook. "Statistical Performance Analysis of Data-Driven Neural Models." International Journal of Neural Systems 27, no. 01 (November 8, 2016): 1650045. http://dx.doi.org/10.1142/s0129065716500453.

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Data-driven model-based analysis of electrophysiological data is an emerging technique for understanding the mechanisms of seizures. Model-based analysis enables tracking of hidden brain states that are represented by the dynamics of neural mass models. Neural mass models describe the mean firing rates and mean membrane potentials of populations of neurons. Various neural mass models exist with different levels of complexity and realism. An ideal data-driven model-based analysis framework will incorporate the most realistic model possible, enabling accurate imaging of the physiological variables. However, models must be sufficiently parsimonious to enable tracking of important variables using data. This paper provides tools to inform the realism versus parsimony trade-off, the Bayesian Cramer-Rao (lower) Bound (BCRB). We demonstrate how the BCRB can be used to assess the feasibility of using various popular neural mass models to track epilepsy-related dynamics via stochastic filtering methods. A series of simulations show how optimal state estimates relate to measurement noise, model error and initial state uncertainty. We also demonstrate that state estimation accuracy will vary between seizure-like and normal rhythms. The performance of the extended Kalman filter (EKF) is assessed against the BCRB. This work lays a foundation for assessing feasibility of model-based analysis. We discuss how the framework can be used to design experiments to better understand epilepsy.

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47

Khorasgani, Hamed, Ahmed Farahat, and Chetan Gupta. "Data-driven Residual Generation for Early Fault Detection with Limited Data." Annual Conference of the PHM Society 12, no. 1 (November 3, 2020): 9. http://dx.doi.org/10.36001/phmconf.2020.v12i1.1162.

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Traditionally, fault detection and isolation community have used system dynamic equations to generate diagnosers and to analyze detectability and isolability of the dynamic systems. Model-based fault detection and isolation methods use system model to generate a set of residuals as the bases for fault detection and isolation. However, in many complex systems it is not feasible to develop highly accurate models for the systems and to keep the models updated during the system lifetime. Recently, data- driven solutions have received an immense attention in the industrial applications for several practical reasons. First, these methods do not require the initial investment and expertise for developing accurate models. Moreover, it is possible to update and retrain the diagnosers as the system or the environment change over time automatically. Finally, unlike the model-based methods it is straightforward to combine time series measurements such as pressure and voltage with other sources of information such as system operating hours to achieve a higher accuracy. In this paper, we extend the traditional model- based fault detection and isolation concepts such as residuals, and detectable and isolable faults to the data-driven domain. We then propose an algorithm to automatically generate residuals from the normal operating data. We compare the performance of our proposed approach with traditional model-based methods through a case study.

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48

Toleu, Alymzhan, Gulmira Tolegen, Rustam Mussabayev, Alexander Krassovitskiy, and Irina Ualiyeva. "Data-Driven Approach for Spellchecking and Autocorrection." Symmetry 14, no. 11 (October 27, 2022): 2261. http://dx.doi.org/10.3390/sym14112261.

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This article presents an approach for spellchecking and autocorrection using web data for morphologically complex languages (in the case of Kazakh language), which can be considered an end-to-end approach that does not require any manually annotated word–error pairs. A sizable web of noisy data is crawled and used as a base to infer the knowledge of misspellings with their correct forms. Using the extracted corpus, a sub-string error model with a context model for morphologically complex languages are trained separately, then these two models are integrated with a regularization parameter. A sub-string alignment model is applied to extract symmetric and non-symmetric patterns in two sequences of word–error pairs. The model calculates the probability for symmetric and non-symmetric patterns of a given misspelling and its candidates to obtain a suggestion list. Based on the proposed method, a Kazakh Spellchecking and Autocorrection system is developed, which we refer to as QazSpell. Several experiments are conducted to evaluate the proposed approach from different angles. The results show that the proposed approach achieves a good outcome when only using the error model, and the performance is boosted after integrating the context model. In addition, the developed system, QazSpell, outperforms the commercial analogs in terms of overall accuracy.

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Lahouel, Kamel, Laurent Younes, Ludmila Danilova, Francis M. Giardiello, Ralph H. Hruban, John Groopman, Kenneth W. Kinzler, Bert Vogelstein, Donald Geman, and Cristian Tomasetti. "Revisiting the tumorigenesis timeline with a data-driven generative model." Proceedings of the National Academy of Sciences 117, no. 2 (December 27, 2019): 857–64. http://dx.doi.org/10.1073/pnas.1914589117.

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Abstract:

Cancer is driven by the sequential accumulation of genetic and epigenetic changes in oncogenes and tumor suppressor genes. The timing of these events is not well understood. Moreover, it is currently unknown why the same driver gene change appears as an early event in some cancer types and as a later event, or not at all, in others. These questions have become even more topical with the recent progress brought by genome-wide sequencing studies of cancer. Focusing on mutational events, we provide a mathematical model of the full process of tumor evolution that includes different types of fitness advantages for driver genes and carrying-capacity considerations. The model is able to recapitulate a substantial proportion of the observed cancer incidence in several cancer types (colorectal, pancreatic, and leukemia) and inherited conditions (Lynch and familial adenomatous polyposis), by changing only 2 tissue-specific parameters: the number of stem cells in a tissue and its cell division frequency. The model sheds light on the evolutionary dynamics of cancer by suggesting a generalized early onset of tumorigenesis followed by slow mutational waves, in contrast to previous conclusions. Formulas and estimates are provided for the fitness increases induced by driver mutations, often much larger than previously described, and highly tissue dependent. Our results suggest a mechanistic explanation for why the selective fitness advantage introduced by specific driver genes is tissue dependent.

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Stangier, Isabelle, C. Liedtke, S. I. Ziegler, M. E. Spilker, and H. Boecker. "Kinetic modelling of [11C]flumazenil using model-driven and data-driven methods." NeuroImage 31 (January 2006): T97. http://dx.doi.org/10.1016/j.neuroimage.2006.04.084.

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