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Journal articles on the topic 'Computational Molecular Biology'

Author: Grafiati

Published: 11 March 2023

Last updated: 31 July 2025

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1

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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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 (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 (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 (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 (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 (1999): 9–12. http://dx.doi.org/10.1145/340358.340374.

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7

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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8

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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9

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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10

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

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11

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

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12

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

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13

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

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14

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

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15

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

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16

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

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17

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

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18

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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19

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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20

Jiang, Tao, Paul Kearney, and Ming Li. "Some open problems in computational molecular biology." ACM SIGACT News 30, no. 3 (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 (2000): 194–201. http://dx.doi.org/10.1006/jagm.1999.1050.

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22

Krauze, Andra V., and Kevin Camphausen. "Molecular Biology in Treatment Decision Processes—Neuro-Oncology Edition." International Journal of Molecular Sciences 22, no. 24 (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 analy

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23

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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24

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 (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 anal

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25

D, Bhuvana. "Innovations in Molecular Biology-Cutting-Edge Breakthroughs in Molecular Genetics." Annals of Experimental and Molecular Biology 6, no. 1 (2024): 1–4. http://dx.doi.org/10.23880/aemb-16000121.

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The field of molecular biology has experienced significant breakthroughs in recent years, driven by cutting-edge technologies and innovative research strategies. This abstract provides a concise overview of some key advancement that has shaped the landscape of molecular biology. One prominent area of progress involves the CRISPR-Cas9 gene editing system, which has revolutionized genetic manipulation. Researchers have refined and expanded its applications, enabling precise modifications to the genome for therapeutic purposes, functional genomics, and the development of genetically modified orga

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26

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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27

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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28

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

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29

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

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30

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 (2012): 429–52. http://dx.doi.org/10.1146/annurev-biophys-042910-155245.

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31

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

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32

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

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33

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

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34

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 (2009): 228–39. http://dx.doi.org/10.2976/1.3092784.

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35

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 (1989): 1056–60. http://dx.doi.org/10.1109/5.30755.

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36

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

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37

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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38

Park, Heewon, and Satoru Miyano. "Computational Tactics for Precision Cancer Network Biology." International Journal of Molecular Sciences 23, no. 22 (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 vary

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39

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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40

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

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41

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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42

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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43

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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44

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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45

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

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46

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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47

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

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48

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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49

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

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50

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

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