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Journal articles on the topic 'Computational biology'

To see the other types of publications on this topic, follow the link: Computational biology.

Author: Grafiati

Published: 4 June 2021

Last updated: 29 July 2024

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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 (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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2

Wood, C. C. "The computational stance in biology." Philosophical Transactions of the Royal Society B: Biological Sciences 374, no. 1774 (April 22, 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 agreement among computer scientists about the essential elements that constitute computation, any doctrinaire position about the application of computational ideas seems misguided. Closely related to the concept of computation is the concept of information processing. Indeed, some influential computer scientists contend that there is no fundamental difference between the two concepts. I will argue that despite the lack of widely accepted, general definitions of information processing and computation: (1) information processing and computation are not fully equivalent and there is value in maintaining a distinction between them and (2) that such value is particularly evident in applications of information processing and computation to biology.This article is part of the theme issue ‘Liquid brains, solid brains: How distributed cognitive architectures process information’.

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3

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

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4

Mesirov, J. P., and D. K. Slonim. "Computational biology." Computing in Science & Engineering 1, no. 3 (May 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 (November 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 (March 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 (September 3, 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 (September 4, 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 (August 27, 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 (March 9, 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 (November 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 (March 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 (November 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 (January 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 (May 12, 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 (March 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 (November 17, 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 (March 28, 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 and hybrid analog–digital computational approaches; otherwise, even relatively simple synthetic computations in cells such as addition will exceed energy and molecular-count budgets. We present schematics for efficiently representing analog DNA–protein computation in cells. Analog electronic flow in subthreshold transistors and analog molecular flux in chemical reactions obey Boltzmann exponential laws of thermodynamics and are described by astoundingly similar logarithmic electrochemical potentials. Therefore, cytomorphic circuits can help to map circuit designs between electronic and biochemical domains. We review recent work that uses positive-feedback linearization circuits to architect wide-dynamic-range logarithmic analog computation in Escherichia coli using three transcription factors, nearly two orders of magnitude more efficient in parts than prior digital implementations.

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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 (January 27, 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 (March 6, 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 (July 3, 2015): 1–7. http://dx.doi.org/10.3233/isb-140461.

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25

Restrepo, Silvia, Andrés Pinzón, Luis Miguel Rodríguez-R, Roberto Sierra, Alejandro Grajales, Adriana Bernal, Emiliano Barreto, et al. "Computational Biology in Colombia." PLoS Computational Biology 5, no. 10 (October 30, 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 (November 14, 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 in a range of computational biology tasks. Here, we review some of these advances in the last 2 years.

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27

Laursen, Lucas. "Computational biology: Biological logic." Nature 462, no. 7272 (November 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 (December 7, 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 and topics covered in these seven papers are timely and important, and the authors have done an excellent job of presenting the material.

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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 (December 28, 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 (March 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 (January 12, 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 recent biological insights gained from applying bioinformatics methods to analyse and interpret ageing data in different organisms, tissues and conditions.

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33

Bader, David A., and Srinivas Aluru. "High-performance computational biology." Parallel Computing 34, no. 11 (November 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 (April 24, 2015): 435–52. http://dx.doi.org/10.1002/wrna.1286.

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36

Way, Gregory P., Casey S. Greene, Piero Carninci, Benilton S. Carvalho, Michiel de Hoon, Stacey D. Finley, Sara J. C. Gosline, et al. "A field guide to cultivating computational biology." PLOS Biology 19, no. 10 (October 7, 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 in order to pave the way for the next generation of discoveries, we need to improve recognition for computational biologists and better align pathways of career success with pathways of scientific progress. With 10 outlined steps, we call on all adjacent fields to move away from the traditional individual, single-discipline investigator research model and embrace multidisciplinary, data-driven, team science.

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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 (March 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 for relating consequences of the model to quantifiable observations. We also explore the contingency and creative nature of a scientific theory. Among the questions considered are: Can computational biology form the theoretical core of biology? What is the basis, if any, for choosing one particular model over another? And what is the role of computation in science, and in biology in particular? We examine how this broad epistemological framework applies to important statistical methodologies pertaining to computational biology, such as expression-based phenotype classification, gene regulatory networks, and clustering. We consider classification in detail, as the epistemological issues raised by classification are related to all computational-biology topics in which statistical prediction plays a key role. We pay particular attention to classifier-model validity and its relation to estimation rules.

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38

Toma, Milan, and Riccardo Concu. "Computational Biology: A New Frontier in Applied Biology." Biology 10, no. 5 (April 27, 2021): 374. http://dx.doi.org/10.3390/biology10050374.

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39

T.A, Deepak, Anulekha C.K, Suchindra Suchindra, Avinash Tejasvi, and Ms Mariyam Nadhira. "Role of Computational Biology in Oral Science." Bioscience & Engineering : An International Journal 11, no. 1 (January 29, 2024): 01–13. http://dx.doi.org/10.5121/bioej.2024.11101.

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DNA sequence Cigarette Smoking, Betel leaf chewing, and alcohol consumption are major cause of oral cancer in Asia. The difficulty in quitting, coupled with patients’ economic conditions affects the inability to get diagnosed early, driving death rate higher. There has been major advancement in molecular sciences, computational biology, and other fields today, but we are not still able to pinpoint the causes of oral cancer, also known as Squamous Cell Carcinoma (OSCC). Early detection leads to better survival rate, therefore, education on yearly check-ups plays a vital role. Computational analysis at the genomic (DNA sequence) can help patients with targeted cellular treatment and hopefully a cure. In this paper, we would look at computation tools used in detecting OSCC and various analysis. Analysis includes detecting abnormality in the cell and other molecular reactions which later morph into a cancerous cell. Later, we investigate all computational tools or techniques from local and global sequence alignment, protein structure, gene, functional structure analysis which help medical staff detect cancer, which in turn can help with oral cancer treatment, prognosis and hopefully a cure.

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40

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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41

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

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42

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

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43

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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44

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

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45

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

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46

Claverie, J. M. "From Bioinformatics to Computational Biology." Genome Research 10, no. 9 (September 1, 2000): 1277–79. http://dx.doi.org/10.1101/gr.155500.

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47

Bateman, Alex, Janet Kelso, Daniel Mietchen, Geoff Macintyre, Tomás Di Domenico, Thomas Abeel, Darren W. Logan, Predrag Radivojac, and Burkhard Rost. "ISCB Computational Biology Wikipedia Competition." PLoS Computational Biology 9, no. 9 (September 19, 2013): e1003242. http://dx.doi.org/10.1371/journal.pcbi.1003242.

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48

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

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49

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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50

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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