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Journal articles on the topic 'Computational biology, bioinformatics'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 August 2024

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1

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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2

Fatumo, Segun A., Moses P. Adoga, Opeolu O. Ojo, Olugbenga Oluwagbemi, Tolulope Adeoye, Itunuoluwa Ewejobi, Marion Adebiyi, Ezekiel Adebiyi, Clement Bewaji, and Oyekanmi Nashiru. "Computational Biology and Bioinformatics in Nigeria." PLoS Computational Biology 10, no. 4 (April 24, 2014): e1003516. http://dx.doi.org/10.1371/journal.pcbi.1003516.

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3

Pearson, W. R. "Training for bioinformatics and computational biology." Bioinformatics 17, no. 9 (September 1, 2001): 761–62. http://dx.doi.org/10.1093/bioinformatics/17.9.761.

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4

Cho, Sung-Bae, Jonathan H. Chan, and Kyu-Baek Hwang. "Preface: Computational Systems-Biology and Bioinformatics." Procedia Computer Science 23 (2013): 1–4. http://dx.doi.org/10.1016/j.procs.2013.10.002.

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5

Pegg, S. "Dictionary of Bioinformatics and Computational Biology." Briefings in Bioinformatics 6, no. 2 (January 1, 2005): 211–12. http://dx.doi.org/10.1093/bib/6.2.211.

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6

Chan, Jonathan H., Asawin Meechai, and Chee Keong Kwoh. "Preface: Computational Systems-Biology and Bioinformatics." Procedia Computer Science 11 (2012): 1–3. http://dx.doi.org/10.1016/j.procs.2012.09.001.

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7

Talbi, El-Ghazali, and Albert Zomaya. "Grids in bioinformatics and computational biology." Journal of Parallel and Distributed Computing 66, no. 12 (December 2006): 1481. http://dx.doi.org/10.1016/j.jpdc.2006.09.001.

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8

Bujnicki, Janusz M., and Jerzy Tiuryn. "Bioinformatics and Computational Biology in Poland." PLoS Computational Biology 9, no. 5 (May 2, 2013): e1003048. http://dx.doi.org/10.1371/journal.pcbi.1003048.

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9

Eldəniz qızı Əhmədova, Gülnarə. "Inclusion of bioinformatics in biological sciences." NATURE AND SCIENCE 22, no. 7 (July 17, 2022): 82–86. http://dx.doi.org/10.36719/2707-1146/22/82-86.

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Bioinformatika hesablama və biologiya elmlərinin birləşməsi kimi müəyyən edilə bilər. Proteomika və genomika tədqiqatları nəticəsində yaranan məlumatların daşqını emal etmək və təhlil etmək üçün aktuallıq bioinformatikanın önəm və əhəmiyyət qazanmasına səbəb oldu. Bununla belə, onun multidissiplinar təbiəti həm biologiya, həm də hesablama sahəsində hazırlanmış mütəxəssisə unikal tələbat yaratmışdır. İcmalda bioinformatika sahəsini təşkil edən komponentlər və bioinformatika təhsili olan fərdlərin yetişdirilməsi üçün tələb olunan fərqli təhsil meyarları təsvir edilib. Məqalə həm də Malayziyada bioinformatikaya giriş və onun haqqında ümumi məlumat verəcəkdir. Malayziyada mövcud bioinformatika ssenarisi onun inkişafını ölçmək və gələcək bioinformatika təhsili strategiyalarını planlaşdırmaq üçün araşdırıldı. Müqayisə üçün biz digər ölkələrin təhsildə istifadə etdiyi metod və strategiyaları araşdırdıq ki, bioinformatikanın tətbiqini daha da təkmilləşdirmək üçün dərslər alınsın. Hesab olunur ki, akademiyadan, sənayedən dəqiq və kifayət qədər idarəetmə gələcəkdə keyfiyyətli bioinformatiklər yetişdirməyə imkan verəcək. Açar sözlər: bioinformatika, hesablama biologiyası, təhsil, biologiya elmi, bioinformatikanın tədrisi Gulnara Eldeniz Ahmadova Inclusion of bioinformatics in biological sciences Abstract Bioinformatics can be defined as the combination of computational and biological sciences. The urgency to process and analyze the flood of data resulting from proteomics and genomics research has led bioinformatics to gain prominence and importance. However, its multidisciplinary nature has created a unique need for a specialist trained in both biology and computing. In this review, we have described the components that make up the field of bioinformatics and the different educational criteria required to produce individuals with bioinformatics training. This article will also provide an introduction and overview of bioinformatics in Malaysia. The current bioinformatics scenario in Malaysia was examined to gauge its development and plan future bioinformatics education strategies. For comparison, we examined the methods and strategies used in education by other countries, so that lessons can be learned to further improve the application of bioinformatics. It is believed that accurate and sufficient management from academia and industry will enable to produce quality bioinformaticians in the future. Keywords: bioinformatics, computational biology, education, biological science, teaching bioinformatics
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10

Gomathy, Dr CK, Mr D. Surya Manohar, and Vasavi Rajesh. "PROTEIN DATABASE IN COMPUTATIONAL BIOLOGY." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 11 (November 1, 2023): 1–11. http://dx.doi.org/10.55041/ijsrem26770.

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Protein data sets have turns into a Critical piece of computational science. Colossal measure of information for protein structure capability and especially arrangement are being created. Looking through information base is an initial step of study to track down new protein. We present the rudiments of protein underlying bioinformatics. Protein performs most fundamental natural and compound capability in a cell. They additionally assume significant part in primary, enzymatic, transport and administrative capabilities. not entirely settled by their design. This survey covers some fundamental of protein structure and related data sets and it is further developed subject of protein underlying bioinformatics. This work gives to investigate the capability of protein information bases on web. Keywords: Protein database, bioinformatics, protein structure, protein sequences
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11

Wang, May Dongmei. "In the Spotlight: Bioinformatics, Computational Biology and Systems Biology." IEEE Reviews in Biomedical Engineering 4 (2011): 3–5. http://dx.doi.org/10.1109/rbme.2011.2177935.

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12

Saidel, Gerald M., and Jie Liang. "ABME Special Issue: Systems Biology, Bioinformatics, and Computational Biology." Annals of Biomedical Engineering 35, no. 6 (May 8, 2007): 861–62. http://dx.doi.org/10.1007/s10439-007-9325-7.

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13

Zheng, H., K. C. Wiese, and F. Azuaje. "Special Section on Bioinformatics and Computational Biology." IEEE Transactions on NanoBioscience 4, no. 3 (September 2005): 205–6. http://dx.doi.org/10.1109/tnb.2005.853643.

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14

Semple, C. A. M. "Meet the parents: bioinformatics and computational biology." Heredity 91, no. 6 (November 25, 2003): 542–43. http://dx.doi.org/10.1038/sj.hdy.6800366.

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15

Handl, Julia, Douglas B. Kell, and Joshua Knowles. "Multiobjective Optimization in Bioinformatics and Computational Biology." IEEE/ACM Transactions on Computational Biology and Bioinformatics 4, no. 2 (April 2007): 279–92. http://dx.doi.org/10.1109/tcbb.2007.070203.

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16

Chen, Luonan, and Michael K. Ng. "Guest Editorial: Bioinformatics and Computational Systems Biology." IEEE/ACM Transactions on Computational Biology and Bioinformatics 9, no. 4 (July 2012): 945–46. http://dx.doi.org/10.1109/tcbb.2012.76.

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17

Merelli, E., G. Armano, N. Cannata, F. Corradini, M. d'Inverno, A. Doms, P. Lord, et al. "Agents in bioinformatics, computational and systems biology." Briefings in Bioinformatics 8, no. 1 (May 26, 2006): 45–59. http://dx.doi.org/10.1093/bib/bbl014.

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18

Pickeral, Oxana K., and Mark S. Boguski. "The Bioinformatics Bookshelf: Teach Yourself Computational Biology?" Cell 96, no. 4 (February 1999): 451–55. http://dx.doi.org/10.1016/s0092-8674(00)80632-7.

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19

Bateman, A., and A. Valencia. "Structural genomics meets computational biology." Bioinformatics 22, no. 19 (October 1, 2006): 2319. http://dx.doi.org/10.1093/bioinformatics/btl426.

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20

Cremona, Marzia A., Hongyan Xu, Kateryna D. Makova, Matthew Reimherr, Francesca Chiaromonte, and Pedro Madrigal. "Functional data analysis for computational biology." Bioinformatics 35, no. 17 (January 22, 2019): 3211–13. http://dx.doi.org/10.1093/bioinformatics/btz045.

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21

Bodlaender, H. L., R. G. Downey, M. R. Fellows, M. T. Hallett, and H. T. Wareham. "Parameterized complexity analysis in computational biology." Bioinformatics 11, no. 1 (1995): 49–57. http://dx.doi.org/10.1093/bioinformatics/11.1.49.

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22

Dress, Andreas, Michal Linial, Olga Troyanskaya, and Martin Vingron. "ISCB/SPRINGER series in computational biology." Bioinformatics 29, no. 24 (November 8, 2013): 3246–47. http://dx.doi.org/10.1093/bioinformatics/btt630.

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23

Dress, A., M. Linial, O. Troyanskaya, and M. Vingron. "ISCB/SPRINGER series in computational biology." Bioinformatics 30, no. 1 (December 18, 2013): 146–47. http://dx.doi.org/10.1093/bioinformatics/btt670.

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24

Fogg, C. N., and D. E. Kovats. "International Society for Computational Biology Honors Goncalo Abecasis with Top Bioinformatics/Computational Biology Award for 2013." Bioinformatics 29, no. 12 (May 9, 2013): 1586–87. http://dx.doi.org/10.1093/bioinformatics/btt251.

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25

Mahanayak, Bhaskar. "Meaning, Concept and Application of Bioinformatics." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 06 (June 26, 2024): 1–5. http://dx.doi.org/10.55041/ijsrem36102.

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Bioinformatics, originating from the term coined in 1979 by Paulien Hogeweg and Ben Hesper, is a burgeoning interdisciplinary field at the nexus of biology, computer science, and information technology. It encompasses the collection, classification, storage, and analysis of vast biological datasets using computational methods, aiming to unravel the complexities of biological systems. This abstract provides a comprehensive overview of bioinformatics, emphasizing its definition, scope, branches, aims, research areas, databases, and applications, particularly highlighting its role in drug discovery through structural bioinformatics. Bioinformatics integrates diverse branches such as computational biology, genomics, proteomics, structural biology, systems biology, pharmacogenomics, and bioprogramming. Each branch applies mathematical modeling, molecular interactions analysis, or genomic data interpretation to advance understanding in biology and medicine. Key aims include applying advanced computational technologies to biological problems, presenting complex data clearly, and providing robust statistical tools for genomic analysis. The field also fosters collaborations across academic, commercial, and government sectors to leverage resources and expertise. Research areas span computational evolutionary analysis, genome annotation, gene expression analysis, cancer mutation studies, and comparative genomics, illuminating disease mechanisms and evolutionary relationships. Bioinformatics databases play a crucial role by storing and organizing biological data. Primary repositories like GenBank and the Protein Data Bank (PDB), secondary databases such as SWISS-Prot, and specialized databases support research and clinical applications by providing access to genomic, proteomic, and structural information. In drug discovery, structural bioinformatics techniques like homology modeling, molecular docking, and simulations predict protein structures, analyze interactions, and aid in designing therapies. These computational methods accelerate drug development, optimize drug-target interactions, and reduce costs associated with bringing new drugs to market. Overall, bioinformatics drives innovation in biological sciences and healthcare by leveraging computational methods to interpret biological data. As technology advances, bioinformatics will continue to play a pivotal role in addressing global health challenges, advancing personalized medicine, and facilitating groundbreaking discoveries in biology and medicine. The evolving field of bioinformatics promises continued growth and impact, shaping the future of biological research and its applications in improving human health and understanding the natural world. Keywords: Bioinformatics, Computational biology, Structural bioinformatics, Drug discovery, Genomics.
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26

Ragan, Mark A., Tim Littlejohn, and Bruce Ross. "Genome-Scale Computational Biology and Bioinformatics in Australia." PLoS Computational Biology 4, no. 8 (August 29, 2008): e1000068. http://dx.doi.org/10.1371/journal.pcbi.1000068.

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27

Lathia, Milan. "Computing in Bioinformatics and Computational Biology: A Collection." IEEE Distributed Systems Online 8, no. 3 (March 2007): 4. http://dx.doi.org/10.1109/mdso.2007.12.

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28

Sander, C. "The Journal Bioinformatics, key medium for computational biology." Bioinformatics 18, no. 1 (January 1, 2002): 1–2. http://dx.doi.org/10.1093/bioinformatics/18.1.1.

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29

Wilkinson, D. J. "Bayesian methods in bioinformatics and computational systems biology." Briefings in Bioinformatics 8, no. 2 (December 19, 2006): 109–16. http://dx.doi.org/10.1093/bib/bbm007.

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30

Qin, Hong. "Teaching computational thinking through bioinformatics to biology students." ACM SIGCSE Bulletin 41, no. 1 (March 4, 2009): 188–91. http://dx.doi.org/10.1145/1539024.1508932.

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31

Hu, Xiaohua. "Guest Editorial Bioinformatics and Computational Biology Special Section." IEEE Transactions on NanoBioscience 22, no. 4 (October 2023): 704. http://dx.doi.org/10.1109/tnb.2023.3316485.

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32

Hậu, Lê Đức, and Lê Hoàng Sơn. "Introduction to the Special Issue on Bioinformatics and Computational Biology." Journal of Research and Development on Information and Communication Technology 2019, no. 2 (December 31, 2019): 73–74. http://dx.doi.org/10.32913/mic-ict-research.v2019.n2.917.

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The Special Issue on Bioinformatics and Computational Biology in the Journal of Research and Development on Information and Communication Technology (ICT Research) aims at bringing together researchers in Vietnam for exchange of new developments in all areas of bioinformatics and computational biology.
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33

Fogg, Christiana N. "ISMB 2016 offers outstanding science, networking, and celebration." F1000Research 5 (June 14, 2016): 1371. http://dx.doi.org/10.12688/f1000research.8640.1.

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The annual international conference on Intelligent Systems for Molecular Biology (ISMB) is the major meeting of the International Society for Computational Biology (ISCB). Over the past 23 years the ISMB conference has grown to become the world's largest bioinformatics/computational biology conference. ISMB 2016 will be the year's most important computational biology event globally. The conferences provide a multidisciplinary forum for disseminating the latest developments in bioinformatics/computational biology. ISMB brings together scientists from computer science, molecular biology, mathematics, statistics and related fields. Its principal focus is on the development and application of advanced computational methods for biological problems. ISMB 2016 offers the strongest scientific program and the broadest scope of any international bioinformatics/computational biology conference. Building on past successes, the conference is designed to cater to variety of disciplines within the bioinformatics/computational biology community. ISMB 2016 takes place July 8 - 12 at the Swan and Dolphin Hotel in Orlando, Florida, United States. For two days preceding the conference, additional opportunities including Satellite Meetings, Student Council Symposium, and a selection of Special Interest Group Meetings and Applied Knowledge Exchange Sessions (AKES) are all offered to enable registered participants to learn more on the latest methods and tools within specialty research areas.
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34

Bhaskar Mahanayak. "Meaning, concept and application of bioinformatics." World Journal of Advanced Engineering Technology and Sciences 12, no. 2 (July 30, 2024): 053–56. http://dx.doi.org/10.30574/wjaets.2024.12.2.0269.

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Bioinformatics, originating from the term coined in 1979 by Paulien Hogeweg and Ben Hesper, is a burgeoning interdisciplinary field at the nexus of biology, computer science, and information technology. It encompasses the collection, classification, storage, and analysis of vast biological datasets using computational methods, aiming to unravel the complexities of biological systems. This abstract provides a comprehensive overview of bioinformatics, emphasizing its definition, scope, branches, aims, research areas, databases, and applications, particularly highlighting its role in drug discovery through structural bioinformatics. Bioinformatics integrates diverse branches such as computational biology, genomics, proteomics, structural biology, systems biology, pharmacogenomics, and bioprogramming. Each branch applies mathematical modeling, molecular interactions analysis, or genomic data interpretation to advance understanding in biology and medicine. Key aims include applying advanced computational technologies to biological problems, presenting complex data clearly, and providing robust statistical tools for genomic analysis. The field also fosters collaborations across academic, commercial, and government sectors to leverage resources and expertise. Research areas span computational evolutionary analysis, genome annotation, gene expression analysis, cancer mutation studies, and comparative genomics, illuminating disease mechanisms and evolutionary relationships. Bioinformatics databases play a crucial role by storing and organizing biological data. Primary repositories like GenBank and the Protein Data Bank (PDB), secondary databases such as SWISS-Prot, and specialized databases support research and clinical applications by providing access to genomic, proteomic, and structural information. In drug discovery, structural bioinformatics techniques like homology modeling, molecular docking, and simulations predict protein structures, analyze interactions, and aid in designing therapies. These computational methods accelerate drug development, optimize drug-target interactions, and reduce costs associated with bringing new drugs to market. Overall, bioinformatics drives innovation in biological sciences and healthcare by leveraging computational methods to interpret biological data. As technology advances, bioinformatics will continue to play a pivotal role in addressing global health challenges, advancing personalized medicine, and facilitating groundbreaking discoveries in biology and medicine. The evolving field of bioinformatics promises continued growth and impact, shaping the future of biological research and its applications in improving human health and understanding the natural world.
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35

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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36

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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37

Likić, Vladimir A., Malcolm J. McConville, Trevor Lithgow, and Antony Bacic. "Systems Biology: The Next Frontier for Bioinformatics." Advances in Bioinformatics 2010 (February 9, 2010): 1–10. http://dx.doi.org/10.1155/2010/268925.

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Biochemical systems biology augments more traditional disciplines, such as genomics, biochemistry and molecular biology, by championing (i) mathematical and computational modeling; (ii) the application of traditional engineering practices in the analysis of biochemical systems; and in the past decade increasingly (iii) the use of near-comprehensive data sets derived from ‘omics platform technologies, in particular “downstream” technologies relative to genome sequencing, including transcriptomics, proteomics and metabolomics. The future progress in understanding biological principles will increasingly depend on the development of temporal and spatial analytical techniques that will provide high-resolution data for systems analyses. To date, particularly successful were strategies involving (a) quantitative measurements of cellular components at the mRNA, protein and metabolite levels, as well as in vivo metabolic reaction rates, (b) development of mathematical models that integrate biochemical knowledge with the information generated by high-throughput experiments, and (c) applications to microbial organisms. The inevitable role bioinformatics plays in modern systems biology puts mathematical and computational sciences as an equal partner to analytical and experimental biology. Furthermore, mathematical and computational models are expected to become increasingly prevalent representations of our knowledge about specific biochemical systems.
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38

Setty, Y. "Multi-scale computational modeling of developmental biology." Bioinformatics 28, no. 15 (May 24, 2012): 2022–28. http://dx.doi.org/10.1093/bioinformatics/bts307.

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39

O’Neill, Kieran, Vivek Rai, and Alastair M. Kilpatrick. "The International Society for Computational Biology and WikiProject Computational Biology: celebrating 10 years of collaboration towards open access." Bioinformatics 33, no. 15 (June 19, 2017): 2429–30. http://dx.doi.org/10.1093/bioinformatics/btx388.

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40

Corns, Steven. "IEEE CIBCB 2014 - Computational Intelligence in Bioinformatics and Computational Biology [Conference Reports]." IEEE Computational Intelligence Magazine 9, no. 4 (November 2014): 10–11. http://dx.doi.org/10.1109/mci.2014.2350921.

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41

Srinivasan, N. "Computational Biology and Bioinformatics: A tinge of Indian spice." Bioinformation 1, no. 1 (January 1, 2006): 105–9. http://dx.doi.org/10.6026/97320630001105.

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42

Davis, J. Wade. "Bioinformatics and Computational Biology Solutions Using R and Bioconductor." Journal of the American Statistical Association 102, no. 477 (March 2007): 388–89. http://dx.doi.org/10.1198/jasa.2007.s179.

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43

Payne, Joshua L., Nicholas A. Sinnott-Armstrong, and Jason H. Moore. "Exploiting graphics processing units for computational biology and bioinformatics." Interdisciplinary Sciences: Computational Life Sciences 2, no. 3 (July 25, 2010): 213–20. http://dx.doi.org/10.1007/s12539-010-0002-4.

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44

Bertolazzi, Paola, Jacek Blazewicz, and Metin Turkay. "Operations Research Models for Computational Biology, Bioinformatics and Medicine." Journal of Mathematical Modelling and Algorithms 9, no. 3 (June 30, 2010): 209–11. http://dx.doi.org/10.1007/s10852-010-9135-z.

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45

Pan, Yi, and Zhipeng Cai. "Guest editorial: Special issue on bioinformatics and computational biology." Tsinghua Science and Technology 17, no. 6 (December 2012): 607–8. http://dx.doi.org/10.1109/tst.2012.6374361.

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46

Pan, Yi. "Guest Editorial: Special issue on bioinformatics and computational biology." Tsinghua Science and Technology 18, no. 5 (October 2013): 429–30. http://dx.doi.org/10.1109/tst.2013.6616515.

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47

Hauth, Amy M., and Gertraud Burger. "Methodology for Constructing Problem Definitions in Bioinformatics." Bioinformatics and Biology Insights 2 (January 2008): BBI.S706. http://dx.doi.org/10.4137/bbi.s706.

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Motivation A recurrent criticism is that certain bioinformatics tools do not account for crucial biology and therefore fail answering the targeted biological question. We posit that the single most important reason for such shortcomings is an inaccurate formulation of the computational problem. Results Our paper describes how to define a bioinformatics problem so that it captures both the underlying biology and the computational constraints for a particular problem. The proposed model delineates comprehensively the biological problem and conducts an item-by-item bioinformatics transformation resulting in a germane computational problem. This methodology not only facilitates interdisciplinary information flow but also accommodates emerging knowledge and technologies.
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48

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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49

Giancarlo, R., D. Scaturro, and F. Utro. "Textual data compression in computational biology: a synopsis." Bioinformatics 25, no. 13 (February 27, 2009): 1575–86. http://dx.doi.org/10.1093/bioinformatics/btp117.

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50

Capella-Gutierrez, Salvador, Eva Alloza, Edurne Gallastegui, Ioannis Kavakiotis, Jen Harrow, Alberto Langtry, and Alfonso Valencia. "ECCB2020: the 19th European Conference on Computational Biology." Bioinformatics 36, Supplement_2 (December 2020): i569—i572. http://dx.doi.org/10.1093/bioinformatics/btaa979.

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