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Artykuły w czasopismach na temat „Computational biology”

Autor: Grafiati

Data publikacji: 4 czerwca 2021

Data aktualizacji: 25 lipca 2025

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1

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 (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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2

Wood, C. C. "The computational stance in biology." Philosophical Transactions of the Royal Society B: Biological Sciences 374, no. 1774 (2019): 20180380. http://dx.doi.org/10.1098/rstb.2018.0380.

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The goal of this article is to call attention to, and to express caution about, the extensive use of computation as an explanatory concept in contemporary biology. Inspired by Dennett's ‘intentional stance’ in the philosophy of mind, I suggest that a ‘computational stance’ can be a productive approach to evaluating the value of computational concepts in biology. Such an approach allows the value of computational ideas to be assessed without being diverted by arguments about whether a particular biological system is ‘actually computing’ or not. Because there is sufficient difference of agreemen

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3

Lederman, Lynne. "Computational Biology." BioTechniques 40, no. 3 (2006): 263–65. http://dx.doi.org/10.2144/06403tn01.

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Mesirov, J. P., and D. K. Slonim. "Computational biology." Computing in Science & Engineering 1, no. 3 (1999): 16–17. http://dx.doi.org/10.1109/mcise.1999.764211.

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5

Surridge, Christopher. "Computational biology." Nature 420, no. 6912 (2002): 205. http://dx.doi.org/10.1038/nature01253x.

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6

Kingsbury, David T. "Computational biology." ACM Computing Surveys 28, no. 1 (1996): 101–3. http://dx.doi.org/10.1145/234313.234358.

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7

Ray, L. B., L. D. Chong, and N. R. Gough. "Computational Biology." Science Signaling 2002, no. 148 (2002): eg10-eg10. http://dx.doi.org/10.1126/stke.2002.148.eg10.

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8

Schwarz, Karlheinz, Rainer Breitling, and Christian Allen. "Computation: A New Open Access Journal of Computational Chemistry, Computational Biology and Computational Engineering." Computation 1, no. 2 (2013): 27–30. http://dx.doi.org/10.3390/computation1020027.

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9

Alt, Wolfgang, Andreas Deutsch, and Luigi Preziosi. "Computational Cell Biology: Second Theme Issue on “Computational Biology”." Journal of Mathematical Biology 58, no. 1-2 (2008): 1–5. http://dx.doi.org/10.1007/s00285-008-0207-x.

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10

Markowetz, Florian. "All biology is computational biology." PLOS Biology 15, no. 3 (2017): e2002050. http://dx.doi.org/10.1371/journal.pbio.2002050.

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11

Baumbach, Jan. "Integrative computational biology." Integr. Biol. 4, no. 7 (2012): 713–14. http://dx.doi.org/10.1039/c2ib90016e.

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12

Kitano, Hiroaki. "Computational systems biology." Nature 420, no. 6912 (2002): 206–10. http://dx.doi.org/10.1038/nature01254.

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13

Bourne, Philip E., and Steven E. Brenner. "Developing Computational Biology." PLoS Computational Biology 3, no. 9 (2007): e157. http://dx.doi.org/10.1371/journal.pcbi.0030157.

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14

Sneyd, J. "Computational Cell Biology." Mathematical Medicine and Biology 20, no. 1 (2003): 131–33. http://dx.doi.org/10.1093/imammb/20.1.131.

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15

Zhao, Xing-Ming, Weidong Tian, Rui Jiang, and Jun Wan. "Computational Systems Biology." Scientific World Journal 2013 (2013): 1–2. http://dx.doi.org/10.1155/2013/350358.

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16

Knudsen, Thomas B. "Computational systems biology." Reproductive Toxicology 19, no. 1 (2004): 1–2. http://dx.doi.org/10.1016/j.reprotox.2004.07.001.

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17

Schnell, S. "Computational Cell Biology." Briefings in Bioinformatics 4, no. 1 (2003): 87–89. http://dx.doi.org/10.1093/bib/4.1.87.

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18

Mellman, Ira, and Tom Misteli. "Computational cell biology." Journal of Cell Biology 161, no. 3 (2003): 463–64. http://dx.doi.org/10.1083/jcb.200303202.

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19

Wong, Wing Hung. "Computational Molecular Biology." Journal of the American Statistical Association 95, no. 449 (2000): 322–26. http://dx.doi.org/10.1080/01621459.2000.10473934.

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20

Ma, Buyong, and Ruth Nussinov. "From computational quantum chemistry to computational biology: experiments and computations are (full) partners." Physical Biology 1, no. 4 (2004): P23—P26. http://dx.doi.org/10.1088/1478-3967/1/4/p01.

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21

Sarpeshkar, R. "Analog synthetic biology." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 372, no. 2012 (2014): 20130110. http://dx.doi.org/10.1098/rsta.2013.0110.

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We analyse the pros and cons of analog versus digital computation in living cells. Our analysis is based on fundamental laws of noise in gene and protein expression, which set limits on the energy, time, space, molecular count and part-count resources needed to compute at a given level of precision. We conclude that analog computation is significantly more efficient in its use of resources than deterministic digital computation even at relatively high levels of precision in the cell. Based on this analysis, we conclude that synthetic biology must use analog, collective analog, probabilistic an

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22

Chelly Dagdia, Zaineb, Pavel Avdeyev, and Md Shamsuzzoha Bayzid. "Biological computation and computational biology: survey, challenges, and discussion." Artificial Intelligence Review 54, no. 6 (2021): 4169–235. http://dx.doi.org/10.1007/s10462-020-09951-1.

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23

Gupta, Pushpendra K. "GUEST EDITORIAL: COMPUTATIONAL BIOLOGY." International Journal for Computational Biology 3, no. 1 (2014): 1. http://dx.doi.org/10.34040/ijcb.3.1.2014.03.

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24

Bray, Dennis. "Limits of computational biology." In Silico Biology 12, no. 1,2 (2015): 1–7. http://dx.doi.org/10.3233/isb-140461.

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25

Restrepo, Silvia, Andrés Pinzón, Luis Miguel Rodríguez-R, et al. "Computational Biology in Colombia." PLoS Computational Biology 5, no. 10 (2009): e1000535. http://dx.doi.org/10.1371/journal.pcbi.1000535.

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26

Jones, William, Kaur Alasoo, Dmytro Fishman, and Leopold Parts. "Computational biology: deep learning." Emerging Topics in Life Sciences 1, no. 3 (2017): 257–74. http://dx.doi.org/10.1042/etls20160025.

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Deep learning is the trendiest tool in a computational biologist's toolbox. This exciting class of methods, based on artificial neural networks, quickly became popular due to its competitive performance in prediction problems. In pioneering early work, applying simple network architectures to abundant data already provided gains over traditional counterparts in functional genomics, image analysis, and medical diagnostics. Now, ideas for constructing and training networks and even off-the-shelf models have been adapted from the rapidly developing machine learning subfield to improve performance

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27

Laursen, Lucas. "Computational biology: Biological logic." Nature 462, no. 7272 (2009): 408–10. http://dx.doi.org/10.1038/462408a.

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28

Vega-Rodríguez, Miguel A., and Álvaro Rubio-Largo. "Parallelism in computational biology." International Journal of High Performance Computing Applications 32, no. 3 (2016): 317–20. http://dx.doi.org/10.1177/1094342016677599.

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Computational biology allows and encourages the application of many different parallelism-based technologies. This special issue brings together high-quality state-of-the-art contributions about parallelism-based technologies in computational biology, from different points of view or perspectives, that is, from diverse high-performance computing applications. The special issue collects considerably extended and improved versions of the best papers, accepted and presented in PBio 2015 (the Third International Workshop on Parallelism in Bioinformatics, and part of IEEE ISPA 2015 ). The domains a

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29

Neshich, Goran. "Computational Biology in Brazil." PLoS Computational Biology 3, no. 10 (2007): e185. http://dx.doi.org/10.1371/journal.pcbi.0030185.

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30

Bassi, Sebastian, Virginia González, and Gustavo Parisi. "Computational Biology in Argentina." PLoS Computational Biology 3, no. 12 (2007): e257. http://dx.doi.org/10.1371/journal.pcbi.0030257.

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31

Konopka, Andrzej K. "Topics in computational biology." Computers & Chemistry 20, no. 1 (1996): v—vii. http://dx.doi.org/10.1016/s0097-8485(96)80002-7.

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32

Wieser, Daniela, Irene Papatheodorou, Matthias Ziehm, and Janet M. Thornton. "Computational biology for ageing." Philosophical Transactions of the Royal Society B: Biological Sciences 366, no. 1561 (2011): 51–63. http://dx.doi.org/10.1098/rstb.2010.0286.

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High-throughput genomic and proteomic technologies have generated a wealth of publicly available data on ageing. Easy access to these data, and their computational analysis, is of great importance in order to pinpoint the causes and effects of ageing. Here, we provide a description of the existing databases and computational tools on ageing that are available for researchers. We also describe the computational approaches to data interpretation in the field of ageing including gene expression, comparative and pathway analyses, and highlight the challenges for future developments. We review rece

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33

Bader, David A., and Srinivas Aluru. "High-performance computational biology." Parallel Computing 34, no. 11 (2008): 613–15. http://dx.doi.org/10.1016/j.parco.2008.10.001.

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34

You, Lingchong. "Toward Computational Systems Biology." Cell Biochemistry and Biophysics 40, no. 2 (2004): 167–84. http://dx.doi.org/10.1385/cbb:40:2:167.

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35

Li, Yue, and Zhaolei Zhang. "Computational Biology in microRNA." Wiley Interdisciplinary Reviews: RNA 6, no. 4 (2015): 435–52. http://dx.doi.org/10.1002/wrna.1286.

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36

Way, Gregory P., Casey S. Greene, Piero Carninci, et al. "A field guide to cultivating computational biology." PLOS Biology 19, no. 10 (2021): e3001419. http://dx.doi.org/10.1371/journal.pbio.3001419.

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Evolving in sync with the computation revolution over the past 30 years, computational biology has emerged as a mature scientific field. While the field has made major contributions toward improving scientific knowledge and human health, individual computational biology practitioners at various institutions often languish in career development. As optimistic biologists passionate about the future of our field, we propose solutions for both eager and reluctant individual scientists, institutions, publishers, funding agencies, and educators to fully embrace computational biology. We believe that

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37

DOUGHERTY, EDWARD R., and ULISSES BRAGA-NETO. "EPISTEMOLOGY OF COMPUTATIONAL BIOLOGY: MATHEMATICAL MODELS AND EXPERIMENTAL PREDICTION AS THE BASIS OF THEIR VALIDITY." Journal of Biological Systems 14, no. 01 (2006): 65–90. http://dx.doi.org/10.1142/s0218339006001726.

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Knowing the roles of mathematics and computation in experimental science is important for computational biology because these roles determine to a great extent how research in this field should be pursued and how it should relate to biology in general. The present paper examines the epistemology of computational biology from the perspective of modern science, the underlying principle of which is that a scientific theory must have two parts: (1) a structural model, which is a mathematical construct that aims to represent a selected portion of physical reality and (2) a well-defined procedure fo

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38

Toma, Milan, and Riccardo Concu. "Computational Biology: A New Frontier in Applied Biology." Biology 10, no. 5 (2021): 374. http://dx.doi.org/10.3390/biology10050374.

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39

Yilancioglu, Kaan. "Systems Biology and Computational Neuroscience." Journal of Neurobehavioral Sciences 1, no. 3 (2014): 99. http://dx.doi.org/10.5455/jnbs.1415621109.

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40

Abbott, Steve, Alexander V. Panfilov, and Arun V. Holden. "Computational Biology of the Heart." Mathematical Gazette 82, no. 493 (1998): 157. http://dx.doi.org/10.2307/3620195.

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41

Crasto, Chiquito. "Computational Biology of Olfactory Receptors." Current Bioinformatics 4, no. 1 (2009): 8–15. http://dx.doi.org/10.2174/157489309787158143.

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42

Blazewicz, Jacek, and Marta Kasprzak. "Complexity Issues in Computational Biology." Fundamenta Informaticae 118, no. 4 (2012): 385–401. http://dx.doi.org/10.3233/fi-2012-721.

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43

Mak, H. Craig. "Trends in computational biology—2010." Nature Biotechnology 29, no. 1 (2011): 45. http://dx.doi.org/10.1038/nbt.1747.

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44

Noble, Denis. "The rise of computational biology." Nature Reviews Molecular Cell Biology 3, no. 6 (2002): 459–63. http://dx.doi.org/10.1038/nrm810.

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45

Claverie, J. M. "From Bioinformatics to Computational Biology." Genome Research 10, no. 9 (2000): 1277–79. http://dx.doi.org/10.1101/gr.155500.

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46

Bateman, Alex, Janet Kelso, Daniel Mietchen, et al. "ISCB Computational Biology Wikipedia Competition." PLoS Computational Biology 9, no. 9 (2013): e1003242. http://dx.doi.org/10.1371/journal.pcbi.1003242.

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47

Chan, Cliburn. "Big data in computational biology." XRDS: Crossroads, The ACM Magazine for Students 19, no. 1 (2012): 64–68. http://dx.doi.org/10.1145/2331042.2331061.

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48

Loewe, Laurence, and Jane Hillston. "Computational models in systems biology." Genome Biology 9, no. 12 (2008): 328. http://dx.doi.org/10.1186/gb-2008-9-12-328.

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Datta, Susmita, and Somnath Datta. "Computational biology touches all bases." Genome Biology 10, no. 2 (2009): 303. http://dx.doi.org/10.1186/gb-2009-10-2-303.

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Mazza, Tommaso. "High Performance Computational Systems Biology." IEEE/ACM Transactions on Computational Biology and Bioinformatics 9, no. 3 (2012): 641–42. http://dx.doi.org/10.1109/tcbb.2012.42.

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