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Journal articles on the topic 'Computational Molecular Biology'

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Published: 11 March 2023

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1

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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2

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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3

Lloyd, A. "Computational Methods in Molecular Biology." Briefings in Bioinformatics 1, no. 3 (January 1, 2000): 315–16. http://dx.doi.org/10.1093/bib/1.3.315.

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4

Martin, D. "Computational Molecular Biology: An Introduction." Briefings in Bioinformatics 2, no. 2 (January 1, 2001): 204–6. http://dx.doi.org/10.1093/bib/2.2.204.

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5

Brutlag, Douglas L. "Genomics and computational molecular biology." Current Opinion in Microbiology 1, no. 3 (June 1998): 340–45. http://dx.doi.org/10.1016/s1369-5274(98)80039-8.

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6

Hunter, Lawrence. "Progress in computational molecular biology." ACM SIGBIO Newsletter 19, no. 3 (December 1999): 9–12. http://dx.doi.org/10.1145/340358.340374.

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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

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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9

Casadio, Rita, Boris Lenhard, and Michael J. E. Sternberg. "Computational Resources for Molecular Biology 2021." Journal of Molecular Biology 433, no. 11 (May 2021): 166962. http://dx.doi.org/10.1016/j.jmb.2021.166962.

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10

Gentleman, Robert. "Current Topics in Computational Molecular Biology." Journal of the American Statistical Association 99, no. 466 (June 2004): 560. http://dx.doi.org/10.1198/jasa.2004.s328.

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11

Fickett, James. "Computational Molecular Biology: An Algorithmic Approach." Computers & Chemistry 25, no. 4 (July 2001): 423–24. http://dx.doi.org/10.1016/s0097-8485(01)00076-6.

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12

Martin, D. "Computational Molecular Biology: An Algorithmic Approach." Briefings in Bioinformatics 2, no. 3 (January 1, 2001): 303–5. http://dx.doi.org/10.1093/bib/2.3.303.

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13

Keele, J. W., and J. E. Wray. "Software agents in molecular computational biology." Briefings in Bioinformatics 6, no. 4 (January 1, 2005): 370–79. http://dx.doi.org/10.1093/bib/6.4.370.

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14

Karp, Richard, Ming Li, Pavel Pevzner, and Ron Shamir. "Special issue on computational molecular biology." Journal of Computer and System Sciences 73, no. 7 (November 2007): 1023. http://dx.doi.org/10.1016/j.jcss.2007.03.010.

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15

Karp, Richard M. "Heuristic algorithms in computational molecular biology." Journal of Computer and System Sciences 77, no. 1 (January 2011): 122–28. http://dx.doi.org/10.1016/j.jcss.2010.06.009.

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16

Cai, Yudong, Julio Vera González, Zengrong Liu, and Tao Huang. "Computational Systems Biology Methods in Molecular Biology, Chemistry Biology, Molecular Biomedicine, and Biopharmacy." BioMed Research International 2014 (2014): 1–2. http://dx.doi.org/10.1155/2014/746814.

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17

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

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18

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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19

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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20

Jiang, Tao, Paul Kearney, and Ming Li. "Some open problems in computational molecular biology." ACM SIGACT News 30, no. 3 (September 1999): 43–49. http://dx.doi.org/10.1145/333623.333626.

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21

Jiang, Tao, Paul Kearney, and Ming Li. "Some Open Problems in Computational Molecular Biology." Journal of Algorithms 34, no. 1 (January 2000): 194–201. http://dx.doi.org/10.1006/jagm.1999.1050.

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22

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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23

Krauze, Andra V., and Kevin Camphausen. "Molecular Biology in Treatment Decision Processes—Neuro-Oncology Edition." International Journal of Molecular Sciences 22, no. 24 (December 10, 2021): 13278. http://dx.doi.org/10.3390/ijms222413278.

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Computational approaches including machine learning, deep learning, and artificial intelligence are growing in importance in all medical specialties as large data repositories are increasingly being optimised. Radiation oncology as a discipline is at the forefront of large-scale data acquisition and well positioned towards both the production and analysis of large-scale oncologic data with the potential for clinically driven endpoints and advancement of patient outcomes. Neuro-oncology is comprised of malignancies that often carry poor prognosis and significant neurological sequelae. The analysis of radiation therapy mediated treatment and the potential for computationally mediated analyses may lead to more precise therapy by employing large scale data. We analysed the state of the literature pertaining to large scale data, computational analysis, and the advancement of molecular biomarkers in neuro-oncology with emphasis on radiation oncology. We aimed to connect existing and evolving approaches to realistic avenues for clinical implementation focusing on low grade gliomas (LGG), high grade gliomas (HGG), management of the elderly patient with HGG, rare central nervous system tumors, craniospinal irradiation, and re-irradiation to examine how computational analysis and molecular science may synergistically drive advances in personalised radiation therapy (RT) and optimise patient outcomes.
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24

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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25

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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26

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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27

Bafna, Vineet. "Preface: Research in Computational Molecular Biology (RECOMB 2011)." Journal of Computational Biology 18, no. 11 (November 2011): 1369. http://dx.doi.org/10.1089/cmb.2011.009p.

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28

Sun, Fengzhu. "Preface: Research in Computational Molecular Biology (RECOMB 2013)." Journal of Computational Biology 20, no. 10 (October 2013): 713. http://dx.doi.org/10.1089/cmb.2013.020p.

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29

Dror, Ron O., Robert M. Dirks, J. P. Grossman, Huafeng Xu, and David E. Shaw. "Biomolecular Simulation: A Computational Microscope for Molecular Biology." Annual Review of Biophysics 41, no. 1 (June 9, 2012): 429–52. http://dx.doi.org/10.1146/annurev-biophys-042910-155245.

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30

Kohlbacher, O., and H. P. Lenhof. "BALL--rapid software prototyping in computational molecular biology." Bioinformatics 16, no. 9 (September 1, 2000): 815–24. http://dx.doi.org/10.1093/bioinformatics/16.9.815.

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31

Lenhard, Boris, and Michael J. E. Sternberg. "Computational Resources for Molecular Biology: Special Issue 2020." Journal of Molecular Biology 432, no. 11 (May 2020): 3361–63. http://dx.doi.org/10.1016/j.jmb.2020.04.010.

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32

Barron, Sarah, Matthew Witten, and Gongxian Liu. "A bibliography on computational molecular biology and genetics." Mathematical and Computer Modelling 16, no. 6-7 (June 1992): 245–319. http://dx.doi.org/10.1016/0895-7177(92)90166-i.

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33

Lupieri, Paola, Chuong Ha Hung Nguyen, Zhaleh Ghaemi Bafghi, Alejandro Giorgetti, and Paolo Carloni. "Computational molecular biology approaches to ligand‐target interactions." HFSP Journal 3, no. 4 (August 2009): 228–39. http://dx.doi.org/10.2976/1.3092784.

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34

Lipton, R. J., T. G. Marr, and J. D. Welsh. "Computational approaches to discovering semantics in molecular biology." Proceedings of the IEEE 77, no. 7 (July 1989): 1056–60. http://dx.doi.org/10.1109/5.30755.

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35

Rojo-Domínguez, Arturo. "Srinivas Aluru (ed): Handbook of Computational Molecular Biology." Bulletin of Mathematical Biology 69, no. 8 (April 24, 2007): 2775–76. http://dx.doi.org/10.1007/s11538-007-9217-x.

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36

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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37

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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38

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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39

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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40

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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41

Lilburn, T. G. "Computational aspects of systematic biology." Briefings in Bioinformatics 7, no. 2 (March 7, 2006): 186–95. http://dx.doi.org/10.1093/bib/bbl005.

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42

Park, Heewon, and Satoru Miyano. "Computational Tactics for Precision Cancer Network Biology." International Journal of Molecular Sciences 23, no. 22 (November 19, 2022): 14398. http://dx.doi.org/10.3390/ijms232214398.

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Network biology has garnered tremendous attention in understanding complex systems of cancer, because the mechanisms underlying cancer involve the perturbations in the specific function of molecular networks, rather than a disorder of a single gene. In this article, we review the various computational tactics for gene regulatory network analysis, focused especially on personalized anti-cancer therapy. This paper covers three major topics: (1) cell line’s (or patient’s) cancer characteristics specific gene regulatory network estimation, which enables us to reveal molecular interplays under varying conditions of cancer characteristics of cell lines (or patient); (2) computational approaches to interpret the multitudinous and massive networks; (3) network-based application to uncover molecular mechanisms of cancer and related marker identification. We expect that this review will help readers understand personalized computational network biology that plays a significant role in precision cancer medicine.
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43

Sindi, S. "Handbook of Computational Molecular Biology. * Edited by Srinivas Aluru." Briefings in Bioinformatics 8, no. 3 (May 25, 2007): 201–3. http://dx.doi.org/10.1093/bib/bbm002.

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44

Felsenstein, Joe. "Computational molecular biology: Sources and methods for sequence analysis." Trends in Genetics 5 (1989): 419. http://dx.doi.org/10.1016/0168-9525(89)90203-5.

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45

Konopka, AndrzejK. "Computational molecular biology: From sequence research to software development." Computers & Chemistry 17, no. 2 (June 1993): v—vi. http://dx.doi.org/10.1016/0097-8485(93)85001-s.

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46

Kornelyuk, A. I. "COMPUTATIONAL GRID TECHNOLOGIES AND THEIR APPLICATIONS IN MOLECULAR BIOLOGY." Visnik Nacional'noi' academii' nauk Ukrai'ni 10 (October 20, 2018): 44–51. http://dx.doi.org/10.15407/visn2018.10.044.

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47

Cushing, Judy Bayard. "Metadata and Semantics: A Computational Challenge for Molecular Biology." OMICS: A Journal of Integrative Biology 7, no. 1 (January 2003): 23–24. http://dx.doi.org/10.1089/153623103322006535.

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48

Salzberg, Steven L. "Computational Molecular Biology: An Algorithmic Approach. Pavel A. Pevzner." Quarterly Review of Biology 76, no. 4 (December 2001): 485–86. http://dx.doi.org/10.1086/420567.

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49

Gelfand, M. S. "Second Moscow Conference on Computational Molecular Biology MCCMB’05." Biophysics 51, no. 4 (August 2006): 675–76. http://dx.doi.org/10.1134/s0006350906040269.

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50

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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