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Journal articles on the topic 'Computational approaches'

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Author: Grafiati

Published: 11 March 2023

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1

McClelland, James L., and David C. Plaut. "Computational approaches to cognition: top-down approaches." Current Opinion in Neurobiology 3, no. 2 (April 1993): 209–16. http://dx.doi.org/10.1016/0959-4388(93)90212-h.

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2

Rustad, J. R., W. Dzwinel, and D. A. Yuen. "Computational Approaches to Nanomineralogy." Reviews in Mineralogy and Geochemistry 44, no. 1 (January 1, 2001): 191–216. http://dx.doi.org/10.2138/rmg.2001.44.06.

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3

Stephan, Klaas Enno, and Christoph Mathys. "Computational approaches to psychiatry." Current Opinion in Neurobiology 25 (April 2014): 85–92. http://dx.doi.org/10.1016/j.conb.2013.12.007.

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4

Roman Čerešňák, Karol Matiaško, and Adam Dudáš. "Various Approaches Proposed for Eliminating Duplicate Data in a System." Communications - Scientific letters of the University of Zilina 23, no. 4 (October 1, 2021): A223—A232. http://dx.doi.org/10.26552/com.c.2021.4.a223-a232.

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The growth of big data processing market led to an increase in the overload of computation data centers, change of methods used in storing the data, communication between the computing units and computational time needed to process or edit the data. Methods of distributed or parallel data processing brought new problems related to computations with data which need to be examined. Unlike the conventional cloud services, a tight connection between the data and the computations is one of the main characteristics of the big data services. The computational tasks can be done only if relevant data are available. Three factors, which influence the speed and efficiency of data processing are - data duplicity, data integrity and data security. We are motivated to study the problems related to the growing time needed for data processing by optimizing these three factors in geographically distributed data centers.

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5

Hemmo, Meir, and Orly Shenker. "The Multiple-Computations Theorem and the Physics of Singling Out a Computation." Monist 105, no. 2 (March 9, 2022): 175–93. http://dx.doi.org/10.1093/monist/onab030.

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Abstract The problem of multiple-computations discovered by Hilary Putnam presents a deep difficulty for functionalism (of all sorts, computational and causal). We describe in outline why Putnam’s result, and likewise the more restricted result we call the Multiple-Computations Theorem, are in fact theorems of statistical mechanics. We show why the mere interaction of a computing system with its environment cannot single out a computation as the preferred one amongst the many computations implemented by the system. We explain why nonreductive approaches to solving the multiple-computations problem, and in particular why computational externalism, are dualistic in the sense that they imply that nonphysical facts in the environment of a computing system single out the computation. We discuss certain attempts to dissolve Putnam’s unrestricted result by appealing to systems with certain kinds of input and output states as a special case of computational externalism, and show why this approach is not workable without collapsing to behaviorism. We conclude with some remarks about the nonphysical nature of mainstream approaches to both statistical mechanics and the quantum theory of measurement with respect to the singling out of partitions and observables.

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6

Garg, Diksha, Ankita Jiwan, and Shailendra Singh. "Computational Approaches for Variant Identification." International Journal of Computer Applications 165, no. 8 (May 17, 2017): 18–24. http://dx.doi.org/10.5120/ijca2017913970.

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7

Brogi. "Computational Approaches for Drug Discovery." Molecules 24, no. 17 (August 22, 2019): 3061. http://dx.doi.org/10.3390/molecules24173061.

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8

HOU, Yan-Yan. "Computational approaches to microRNA discovery." Hereditas (Beijing) 30, no. 6 (July 4, 2008): 687–96. http://dx.doi.org/10.3724/sp.j.1005.2008.00687.

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9

Cochrane, Peter, David W. Kuecker, and Carl H. Smith. "Learning and Geometry: Computational Approaches." Mathematical Gazette 81, no. 490 (March 1997): 183. http://dx.doi.org/10.2307/3618830.

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10

Yousef, Malik, Naim Najami, Loai Abedallah, and Waleed Khalifa. "Computational Approaches for Biomarker Discovery." Journal of Intelligent Learning Systems and Applications 06, no. 04 (2014): 153–61. http://dx.doi.org/10.4236/jilsa.2014.64012.

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11

Vangrevelinghe, Eric, and Simon Rudisser. "Computational Approaches for Fragment Optimization." Current Computer Aided-Drug Design 3, no. 1 (March 1, 2007): 69–83. http://dx.doi.org/10.2174/157340907780058781.

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12

Jadaun, Alka, Durga Prasad, Pavan Gupta, Raj Kumar Singh, and Sudeep Shukla. "Computational Approaches for Drug Designing." Biotech Today : An International Journal of Biological Sciences 5, no. 2 (2015): 11. http://dx.doi.org/10.5958/2322-0996.2015.00016.2.

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13

Watters, Paul A., and Michael R. Brent. "Computational Approaches to Language Acquisition." Language 75, no. 1 (March 1999): 190. http://dx.doi.org/10.2307/417518.

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14

Cohen, Jonathan D., Nathaniel Daw, Barbara Engelhardt, Uri Hasson, Kai Li, Yael Niv, Kenneth A. Norman, et al. "Computational approaches to fMRI analysis." Nature Neuroscience 20, no. 3 (February 23, 2017): 304–13. http://dx.doi.org/10.1038/nn.4499.

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15

Ciccarelli, Francesca. "Computational approaches in cancer genomics." ACM SIGEVOlution 10, no. 2 (July 28, 2017): 6. http://dx.doi.org/10.1145/3129161.3129162.

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16

Goldbeter, Albert. "Computational approaches to cellular rhythms." Nature 420, no. 6912 (November 2002): 238–45. http://dx.doi.org/10.1038/nature01259.

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17

Morelli, L. G., K. Uriu, S. Ares, and A. C. Oates. "Computational Approaches to Developmental Patterning." Science 336, no. 6078 (April 12, 2012): 187–91. http://dx.doi.org/10.1126/science.1215478.

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18

Jennings, Charles, and Sandra Aamodt. "Computational approaches to brain function." Nature Neuroscience 3, S11 (November 2000): 1160. http://dx.doi.org/10.1038/81417.

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19

Pouget, Alexandre, and Lawrence H. Snyder. "Computational approaches to sensorimotor transformations." Nature Neuroscience 3, S11 (November 2000): 1192–98. http://dx.doi.org/10.1038/81469.

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20

Doytchinova, Irini A., and Darren R. Flower. "Quantitative approaches to computational vaccinology." Immunology & Cell Biology 80, no. 3 (June 2002): 270–79. http://dx.doi.org/10.1046/j.1440-1711.2002.01076.x.

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21

Appella, Ettore, and Carl W. Anderson. "Identifying protein interactions. Computational approaches." FEBS Journal 272, no. 20 (October 2005): 5099–100. http://dx.doi.org/10.1111/j.1742-4658.2005.04944.x.

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22

Coveney, Peter V., Vanessa Diaz-Zuccarini, Norbert Graf, Peter Hunter, Peter Kohl, Jesper Tegner, and Marco Viceconti. "Integrative approaches to computational biomedicine." Interface Focus 3, no. 2 (April 6, 2013): 20130003. http://dx.doi.org/10.1098/rsfs.2013.0003.

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The new discipline of computational biomedicine is concerned with the application of computer-based techniques and particularly modelling and simulation to human health. Since 2007, this discipline has been synonymous, in Europe, with the name given to the European Union's ambitious investment in integrating these techniques with the eventual aim of modelling the human body as a whole: the virtual physiological human. This programme and its successors are expected, over the next decades, to transform the study and practice of healthcare, moving it towards the priorities known as ‘4P's’: predictive, preventative, personalized and participatory medicine.

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23

Liu, Xiaohui. "Computational Approaches for Urban Environments." AAG Review of Books 4, no. 3 (July 2, 2016): 148–49. http://dx.doi.org/10.1080/2325548x.2016.1187499.

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24

Zaenen, A. "Polysemy. Theoretical and Computational Approaches." International Journal of Lexicography 15, no. 3 (September 1, 2002): 238–42. http://dx.doi.org/10.1093/ijl/15.3.238.

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25

Lamb, Michelle L., and William L. Jorgensen. "Computational approaches to molecular recognition." Current Opinion in Chemical Biology 1, no. 4 (December 1997): 449–57. http://dx.doi.org/10.1016/s1367-5931(97)80038-5.

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26

Pappalardo, Francesco, Darren Flower, Giulia Russo, Marzio Pennisi, and Santo Motta. "Computational modelling approaches to vaccinology." Pharmacological Research 92 (February 2015): 40–45. http://dx.doi.org/10.1016/j.phrs.2014.08.006.

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27

Wolpert, Daniel M. "Computational approaches to motor control." Trends in Cognitive Sciences 1, no. 6 (September 1997): 209–16. http://dx.doi.org/10.1016/s1364-6613(97)01070-x.

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28

Martin, James H. "Computational Approaches to Figurative Language." Metaphor and Symbolic Activity 11, no. 1 (March 1996): 85–100. http://dx.doi.org/10.1207/s15327868ms1101_5.

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29

Flash, Tamar, and Terrence J. Sejnowski. "Computational approaches to motor control." Current Opinion in Neurobiology 11, no. 6 (December 2001): 655–62. http://dx.doi.org/10.1016/s0959-4388(01)00265-3.

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30

Schueler-Furman, Ora, and Shoshana J. Wodak. "Computational approaches to investigating allostery." Current Opinion in Structural Biology 41 (December 2016): 159–71. http://dx.doi.org/10.1016/j.sbi.2016.06.017.

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31

Fuxreiter, M., R. Osman, and I. Simon. "Computational approaches to restriction endonucleases." Journal of Molecular Structure: THEOCHEM 666-667 (December 2003): 469–79. http://dx.doi.org/10.1016/j.theochem.2003.08.071.

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32

Skaggs, William E., and Bruce L. McNaughton. "Computational approaches to hippocampal function." Current Biology 2, no. 4 (April 1992): 198. http://dx.doi.org/10.1016/0960-9822(92)90528-i.

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33

Finn, P. W., and L. E. Kavraki. "Computational Approaches to Drug Design." Algorithmica 25, no. 2-3 (June 1999): 347–71. http://dx.doi.org/10.1007/pl00008282.

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34

Laubrock, Jochen, and Alexander Dunst. "Computational Approaches to Comics Analysis." Topics in Cognitive Science 12, no. 1 (November 8, 2019): 274–310. http://dx.doi.org/10.1111/tops.12476.

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35

K. Sah, M., J. Sadanand, and K. Pramanik. "Computational Approaches in Tissue Engineering." International Journal of Computer Applications 27, no. 4 (August 31, 2011): 13–20. http://dx.doi.org/10.5120/3290-4484.

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36

Balcells, David, and Feliu Maseras. "Computational approaches to asymmetric synthesis." New Journal of Chemistry 31, no. 3 (2007): 333. http://dx.doi.org/10.1039/b615528f.

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37

Reed, Jennifer L., Ryan S. Senger, Maciek R. Antoniewicz, and Jamey D. Young. "Computational Approaches in Metabolic Engineering." Journal of Biomedicine and Biotechnology 2010 (2010): 1–7. http://dx.doi.org/10.1155/2010/207414.

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38

Krumsiek, Jan, Jörg Bartel, and Fabian J. Theis. "Computational approaches for systems metabolomics." Current Opinion in Biotechnology 39 (June 2016): 198–206. http://dx.doi.org/10.1016/j.copbio.2016.04.009.

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39

Hall, Rogers P. "Computational approaches to analogical reasoning." Artificial Intelligence 39, no. 1 (May 1989): 39–120. http://dx.doi.org/10.1016/0004-3702(89)90003-9.

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40

Skaggs, William E., and Bruce L. McNaughton. "Computational approaches to hippocampal function." Current Opinion in Neurobiology 2, no. 2 (April 1992): 209–11. http://dx.doi.org/10.1016/0959-4388(92)90014-c.

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41

Hung, Che-Lun, and Chi-Chun Chen. "Computational Approaches for Drug Discovery." Drug Development Research 75, no. 6 (September 2014): 412–18. http://dx.doi.org/10.1002/ddr.21222.

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42

Khot, Varada, Marc Strous, and Alyse K. Hawley. "Computational approaches in viral ecology." Computational and Structural Biotechnology Journal 18 (2020): 1605–12. http://dx.doi.org/10.1016/j.csbj.2020.06.019.

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43

Qiu, Xueting, Venkata R. Duvvuri, and Justin Bahl. "Computational Approaches and Challenges to Developing Universal Influenza Vaccines." Vaccines 7, no. 2 (May 28, 2019): 45. http://dx.doi.org/10.3390/vaccines7020045.

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The traditional design of effective vaccines for rapidly-evolving pathogens, such as influenza A virus, has failed to provide broad spectrum and long-lasting protection. With low cost whole genome sequencing technology and powerful computing capabilities, novel computational approaches have demonstrated the potential to facilitate the design of a universal influenza vaccine. However, few studies have integrated computational optimization in the design and discovery of new vaccines. Understanding the potential of computational vaccine design is necessary before these approaches can be implemented on a broad scale. This review summarizes some promising computational approaches under current development, including computationally optimized broadly reactive antigens with consensus sequences, phylogenetic model-based ancestral sequence reconstruction, and immunomics to compute conserved cross-reactive T-cell epitopes. Interactions between virus-host-environment determine the evolvability of the influenza population. We propose that with the development of novel technologies that allow the integration of data sources such as protein structural modeling, host antibody repertoire analysis and advanced phylodynamic modeling, computational approaches will be crucial for the development of a long-lasting universal influenza vaccine. Taken together, computational approaches are powerful and promising tools for the development of a universal influenza vaccine with durable and broad protection.

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44

Sadiku, Matthew N. O., Yonghui Wang, Suxia Cui, and Sarhan M. Musa. "COMPUTATIONAL BIOLOGY." International Journal of Advanced Research in Computer Science and Software Engineering 8, no. 6 (June 30, 2018): 66. http://dx.doi.org/10.23956/ijarcsse.v8i6.616.

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Computation is an integral part of a larger revolution that will affect how science is conducted. Computational biology is an important emerging field of biology which is uniquely enabled by computation. It involves using computers to model biological problems and interpret data, especially problems in evolutionary and molecular biology. The application of computational tools to all areas of biology is producing excitements and insights into biological problems too complex for conventional approaches. This paper provides a brief introduction on computational biology.

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45

Balaz, Igor, Sabine Hauert, and Andrew Adamatzky. "Editorial: Computational approaches in cancer modelling." Biosystems 204 (June 2021): 104385. http://dx.doi.org/10.1016/j.biosystems.2021.104385.

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46

Lee, Kyoungrim, and Joo-Woon Lee. "Computational Approaches to Protein-Protein Docking." Current Proteomics 5, no. 1 (April 1, 2008): 10–19. http://dx.doi.org/10.2174/157016408783955083.

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47

Meng, Xuan-Yu, Mihaly Mezei, and Meng Cui. "Computational Approaches for Modeling GPCR Dimerization." Current Pharmaceutical Biotechnology 15, no. 10 (November 7, 2014): 996–1006. http://dx.doi.org/10.2174/1389201015666141013102515.

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48

Stylianou, Antonis. "Computational approaches to study elbow biomechanics." Annals of Joint 6 (January 2021): 11. http://dx.doi.org/10.21037/aoj.2020.04.01.

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49

Montero-Chacón, Francisco, José Sanz-Herrera, and Manuel Doblaré. "Computational Multiscale Solvers for Continuum Approaches." Materials 12, no. 5 (February 26, 2019): 691. http://dx.doi.org/10.3390/ma12050691.

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Computational multiscale analyses are currently ubiquitous in science and technology. Different problems of interest—e.g., mechanical, fluid, thermal, or electromagnetic—involving a domain with two or more clearly distinguished spatial or temporal scales, are candidates to be solved by using this technique. Moreover, the predictable capability and potential of multiscale analysis may result in an interesting tool for the development of new concept materials, with desired macroscopic or apparent properties through the design of their microstructure, which is now even more possible with the combination of nanotechnology and additive manufacturing. Indeed, the information in terms of field variables at a finer scale is available by solving its associated localization problem. In this work, a review on the algorithmic treatment of multiscale analyses of several problems with a technological interest is presented. The paper collects both classical and modern techniques of multiscale simulation such as those based on the proper generalized decomposition (PGD) approach. Moreover, an overview of available software for the implementation of such numerical schemes is also carried out. The availability and usefulness of this technique in the design of complex microstructural systems are highlighted along the text. In this review, the fine, and hence the coarse scale, are associated with continuum variables so atomistic approaches and coarse-graining transfer techniques are out of the scope of this paper.

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50

Gervas, Pablo. "Computational Approaches to Storytelling and Creativity." AI Magazine 30, no. 3 (July 7, 2009): 49. http://dx.doi.org/10.1609/aimag.v30i3.2250.

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This paper deals with computational approaches to storytelling, or the production of stories by computers, with a particular attention on the way human creativity is modelled or emulated, also in computational terms. Features relevant to creativity and to stories are analysed, and existing systems are reviewed under the light of that analysis.The extent to which they implement the key features proposed in recent models of computational creativity is discussed. Limitations, avenues of future research and expected trends are outlined.

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