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Journal articles on the topic 'Bioinformatics'

Author: Grafiati
Published: 4 June 2021
Last updated: 19 April 2025

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1

Bottomley, S. "Bioinformatics: guide for evaluating bioinformatic software." Drug Discovery Today 4, no. 5 (May 1, 1999): 240–43. http://dx.doi.org/10.1016/s1359-6446(99)01352-5.

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2

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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3

Kangueane, Pandjassarame. "Biotechnology, Bioinformatics and BIOINFORMATION in an autobiography." Bioinformation 16, no. 1 (January 31, 2020): 39–50. http://dx.doi.org/10.6026/97320630016039.

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4

Kangueane, Pandjassarame. "Biotechnology, Bioinformatics and BIOINFORMATION in an autobiography." Bioinformation 16, no. 1 (January 31, 2020): 39–50. http://dx.doi.org/10.6026/97320630016050.

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5

Shams, Shakila, M. Abdus Sobhan, Farruk Ahmed, and Ali Shihab Sabbir. "Bioinformatics." International Journal of Science and Research (IJSR) 11, no. 7 (July 5, 2022): 1073–75. http://dx.doi.org/10.21275/sr22706073504.

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6

Lee, Byung-Wook, In-Sun Chu, Nam-Shin Kim, Jin-Hyuk Lee, Seon-Yong Kim, Wan-Kyu Kim, and Sang-Hyuk Lee. "Bioinformatics Resources of the Korean Bioinformation Center (KOBIC)." Genomics & Informatics 8, no. 4 (December 31, 2010): 165–69. http://dx.doi.org/10.5808/gi.2010.8.4.165.

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7

Palsson, Bernhard O. "Bioinformatics: What lies beyond bioinformatics?" Nature Biotechnology 15, no. 1 (January 1997): 3–4. http://dx.doi.org/10.1038/nbt0197-3.

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8

Santos, Sílvia Regina Cavani Jorge. "Bioinformatics." Brazilian Journal of Pharmaceutical Sciences 47, no. 1 (March 2011): 193. http://dx.doi.org/10.1590/s1984-82502011000100024.

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9

Brokaw, Stephen. "Bioinformatics:." Journal of Pharmaceutical Marketing & Management 16, no. 4 (October 13, 2005): 65–80. http://dx.doi.org/10.1300/j058v16n04_04.

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10

Persidis, Aris. "Bioinformatics." Nature Biotechnology 17, no. 8 (August 1999): 828–30. http://dx.doi.org/10.1038/11793.

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11

Doom, T., M. Raymerand, and D. Krane. "Bioinformatics." IEEE Potentials 23, no. 1 (February 2004): 24–27. http://dx.doi.org/10.1109/mp.2004.1266936.

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12

Scott, L. Ridgway. "Bioinformatics." Perspectives in Biology and Medicine 47, no. 1 (2004): 135–39. http://dx.doi.org/10.1353/pbm.2004.0015.

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13

Hofestädt, R. "Bioinformatics." Yearbook of Medical Informatics 11, no. 01 (August 2002): 581–83. http://dx.doi.org/10.1055/s-0038-1638127.

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14

WATKINS, KAREN J. "BIOINFORMATICS." Chemical & Engineering News Archive 79, no. 8 (February 19, 2001): 29–45. http://dx.doi.org/10.1021/cen-v079n008.p029.

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15

Bottomley, Steve. "Bioinformatics." Drug Discovery Today 3, no. 9 (September 1998): 426–28. http://dx.doi.org/10.1016/s1359-6446(98)01225-2.

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16

Bottomley, Steve, and Tim Littlejohn. "Bioinformatics." Drug Discovery Today 3, no. 11 (November 1998): 525–27. http://dx.doi.org/10.1016/s1359-6446(98)01255-0.

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17

Bottomley, Steve. "Bioinformatics." Drug Discovery Today 4, no. 1 (January 1999): 42–44. http://dx.doi.org/10.1016/s1359-6446(98)01285-9.

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18

Bottomley, Steve. "Bioinformatics." Drug Discovery Today 4, no. 10 (October 1999): 482–84. http://dx.doi.org/10.1016/s1359-6446(99)01398-7.

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19

Bottomley, Steve. "Bioinformatics." Drug Discovery Today 5, no. 6 (June 2000): 262–64. http://dx.doi.org/10.1016/s1359-6446(00)01504-x.

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20

Gattiker, James R., Jason T. L. Wang, and Paul P. Wang. "Bioinformatics." Information Sciences 139, no. 1-2 (November 2001): 1–2. http://dx.doi.org/10.1016/s0020-0255(01)00154-2.

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21

Barber, Stuart. "Bioinformatics." Journal of Applied Statistics 36, no. 2 (December 16, 2008): 235–36. http://dx.doi.org/10.1080/02664760802340275.

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22

Kaminski, Naftali. "Bioinformatics." American Journal of Respiratory Cell and Molecular Biology 23, no. 6 (December 2000): 705–11. http://dx.doi.org/10.1165/ajrcmb.23.6.4291.

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23

Boguski, Mark S. "Bioinformatics." Current Opinion in Genetics & Development 4, no. 3 (June 1994): 383–88. http://dx.doi.org/10.1016/0959-437x(94)90025-6.

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24

Moore, Jason H. "Bioinformatics." Journal of Cellular Physiology 213, no. 2 (2007): 365–69. http://dx.doi.org/10.1002/jcp.21218.

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25

Benoît, Gerald. "Bioinformatics." Annual Review of Information Science and Technology 39, no. 1 (October 18, 2006): 179–218. http://dx.doi.org/10.1002/aris.1440390112.

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26

Forman, Michele R., Sarah M. Greene, Nancy E. Avis, Stephen H. Taplin, Paul Courtney, Peter A. Schad, Bradford W. Hesse, and Deborah M. Winn. "Bioinformatics." American Journal of Preventive Medicine 38, no. 6 (June 2010): 646–51. http://dx.doi.org/10.1016/j.amepre.2010.03.002.

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27

Pagel, Mark. "Evolution, Bioinformatics and Evolutionary Bioinformatics Online." Evolutionary Bioinformatics 2 (January 2006): 117693430600200. http://dx.doi.org/10.1177/117693430600200006.

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28

Mshvidobadze, Tinatin. "Bioinformatics as Emerging Tool and Pipeline Frameworks." Science Progress and Research 1, no. 4 (October 23, 2021): 411–15. http://dx.doi.org/10.52152/spr/2021.162.

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In this article, we will discuss the areas of origin of bioinformatics in the human health care system. Due to the growing network of biological information databases such as human genomes, transcriptomics and proteomics, bioinformatics has become the approach of choosing forensic sciences. High-throughput bioinformatic analyses increasingly rely on pipeline frameworks to process sequence and metadata. Here we survey and compare the design philosophies of several current pipeline frameworks.
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29

SoRelle, Jeffrey A., Megan Wachsmann, and Brandi L. Cantarel. "Assembling and Validating Bioinformatic Pipelines for Next-Generation Sequencing Clinical Assays." Archives of Pathology & Laboratory Medicine 144, no. 9 (February 11, 2020): 1118–30. http://dx.doi.org/10.5858/arpa.2019-0476-ra.

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Context.— Clinical next-generation sequencing (NGS) is being rapidly adopted, but analysis and interpretation of large data sets prompt new challenges for a clinical laboratory setting. Clinical NGS results rely heavily on the bioinformatics pipeline for identifying genetic variation in complex samples. The choice of bioinformatics algorithms, genome assembly, and genetic annotation databases are important for determining genetic alterations associated with disease. The analysis methods are often tuned to the assay to maximize accuracy. Once a pipeline has been developed, it must be validated to determine accuracy and reproducibility for samples similar to real-world cases. In silico proficiency testing or institutional data exchange will ensure consistency among clinical laboratories. Objective.— To provide molecular pathologists a step-by-step guide to bioinformatics analysis and validation design in order to navigate the regulatory and validation standards of implementing a bioinformatic pipeline as a part of a new clinical NGS assay. Data Sources.— This guide uses published studies on genomic analysis, bioinformatics methods, and methods comparison studies to inform the reader on what resources, including open source software tools and databases, are available for genetic variant detection and interpretation. Conclusions.— This review covers 4 key concepts: (1) bioinformatic analysis design for detecting genetic variation, (2) the resources for assessing genetic effects, (3) analysis validation assessment experiments and data sets, including a diverse set of samples to mimic real-world challenges that assess accuracy and reproducibility, and (4) if concordance between clinical laboratories will be improved by proficiency testing designed to test bioinformatic pipelines.
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30

Moreews, François, Olivier Sallou, Hervé Ménager, Yvan Le bras, Cyril Monjeaud, Christophe Blanchet, and Olivier Collin. "BioShaDock: a community driven bioinformatics shared Docker-based tools registry." F1000Research 4 (December 14, 2015): 1443. http://dx.doi.org/10.12688/f1000research.7536.1.

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Linux container technologies, as represented by Docker, provide an alternative to complex and time-consuming installation processes needed for scientific software. The ease of deployment and the process isolation they enable, as well as the reproducibility they permit across environments and versions, are among the qualities that make them interesting candidates for the construction of bioinformatic infrastructures, at any scale from single workstations to high throughput computing architectures. The Docker Hub is a public registry which can be used to distribute bioinformatic software as Docker images. However, its lack of curation and its genericity make it difficult for a bioinformatics user to find the most appropriate images needed. BioShaDock is a bioinformatics-focused Docker registry, which provides a local and fully controlled environment to build and publish bioinformatic software as portable Docker images. It provides a number of improvements over the base Docker registry on authentication and permissions management, that enable its integration in existing bioinformatic infrastructures such as computing platforms. The metadata associated with the registered images are domain-centric, including for instance concepts defined in the EDAM ontology, a shared and structured vocabulary of commonly used terms in bioinformatics. The registry also includes user defined tags to facilitate its discovery, as well as a link to the tool description in the ELIXIR registry if it already exists. If it does not, the BioShaDock registry will synchronize with the registry to create a new description in the Elixir registry, based on the BioShaDock entry metadata. This link will help users get more information on the tool such as its EDAM operations, input and output types. This allows integration with the ELIXIR Tools and Data Services Registry, thus providing the appropriate visibility of such images to the bioinformatics community.
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31

Brown Epstein, Helen-Ann. "Successful Support of Bioinformatics and Translational Bioinformatics." Journal of Hospital Librarianship 12, no. 3 (July 2012): 266–71. http://dx.doi.org/10.1080/15323269.2012.692272.

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32

Irham, Lalu Muhammad, Danang Prasetyaning Amukti, Wirawan Adikusuma, Dilpreet Singh, Rockie Chong, Mohammad Basyuni, Satriya Pranata, Isom Hilmi, and Sabiah Khairi. "Applied of bioinformatics in drug discovery and drug development: Bioinformatic analysis 1996-2024." BIO Web of Conferences 148 (2024): 01003. https://doi.org/10.1051/bioconf/202414801003.

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Drug discovery and drug development were two complex process to find new drugs. Advance science of medicine after human genome project were established accelerating the development of new field called bionformatics. Currently, bioinformatics integrated multidisciplinary studies including molecular biology, mathematics and information engineering. This study utilized the Biblioshiny and VosViewer databases as well as the Scopus database to evalute the study related to the bioinformatics in Drug Discovery and Drug Development. Our study were analyzed the scopus data which were retrieved from 1996-2024. We highlighted that 1581 research articles which were publised in 701 journals. Our findings showed that the annual grow up of the research related study was increased annually with the peak of study in 2023. Besides, top five most relevant sources of study was PlosOne (32 documents), international journal of molecular sciences (30 documents), BMS Bioinformatics (29 documents), Bioinfromatics (24 documents), and Frontiers in Genetics (19 documents). In conclusion, through the integration of the use of Vosviewer, biblioshiny and Scopus database software, our findings show a positive trend regarding research on the application of bioinformatics in drug discovery and drug development.
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33

Abed, Raghad, and Yusra Al-Najjar. "Bioinformatics Storing Databases." Technium BioChemMed 2, no. 4 (December 12, 2021): 96–105. http://dx.doi.org/10.47577/biochemmed.v2i4.5335.

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An exceptional branch of data that requires huge databases has been shown lately from genome sequencing projects which is a field that employs computational approaches to answer biological questions. With this huge sequence of information that is available for researchers, bioinformatics plays a big role in studying basic medical-biological problems. The challenge that faces bioinformatical scientists is to help in discovering genes and designing molecular models, site-directed mutagenesis, and other experiments that reveal the unknown relationships concerning the structure and function of genes and proteins. This become a big challenge especially with the huge amount of data that is generated using the human genome and other systematic sequencing efforts up till now. Bioinformatics solves biological problems depending on available data. It is concerned with creating databases and predicting the outcome of lab experiments.
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34

Brazas, M. D., J. T. Yamada, and B. F. F. Ouellette. "Evolution in bioinformatic resources: 2009 update on the Bioinformatics Links Directory." Nucleic Acids Research 37, Web Server (June 15, 2009): W3—W5. http://dx.doi.org/10.1093/nar/gkp531.

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35

Khaldi, Nora. "Bioinformatics approaches for identifying new therapeutic bioactive peptides in food." Functional Foods in Health and Disease 2, no. 10 (October 15, 2012): 325. http://dx.doi.org/10.31989/ffhd.v2i10.80.

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The traditional methods for mining foods for bioactive peptides are tedious and long. Similar to the drug industry, the length of time to identify and deliver a commercial health ingredient that reduces disease symptoms can take anything between 5 to 10 years. Reducing this time and effort is crucial in order to create new commercially viable products with clear and important health benefits. In the past few years, bioinformatics, the science that brings together fast computational biology, and efficient genome mining, is appearing as the long awaited solution to this problem. By quickly mining food genomes for characteristics of certain food therapeutic ingredients, researchers can potentially find new ones in a matter of a few weeks. Yet, surprisingly, very little success has been achieved so far using bioinformatics in mining for food bioactives. The absence of food specific bioinformatic mining tools, the slow integration of both experimental mining and bioinformatics, and the important difference between different experimental platforms are some of the reasons for the slow progress of bioinformatics in the field of functional food and more specifically in bioactive peptide discovery. In this paper I discuss some methods that could be easily translated, using a rational peptide bioinformatics design, to food bioactive peptide mining. I highlight the need for an integrated food peptide database. I also discuss how to better integrate experimental work with bioinformatics in order to improve the mining of food for bioactive peptides, therefore achieving a higher success rates.Keywords: bioactive peptides, bioinformatics, mining food, therapeutic properties, food proteins, functional food.
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36

Fraser, Lewis, Shaolin Liang, Simon Shiu, and Julian Tanner. "Aptamer Bioinformatics." International Journal of Molecular Sciences 18, no. 12 (November 24, 2017): 2516. http://dx.doi.org/10.3390/ijms18122516.

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37

Jain, Eric. "Practical Bioinformatics." Pharmacogenomics 4, no. 2 (March 2003): 119–21. http://dx.doi.org/10.1517/phgs.4.2.119.22634.

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38

Peach, H. F., P. W. M. Johnson, S. Johnson, L. K. Jones, M. Jones, R. Sharpe, E. Shaw, et al. "Computational / bioinformatics." Annals of Oncology 23, suppl 5 (June 1, 2012): v38—v40. http://dx.doi.org/10.1093/annonc/mds164.

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39

Jacobson, A. "Bioinformatics booming." Computing in Science & Engineering 4, no. 4 (July 2002): 11. http://dx.doi.org/10.1109/mcise.2002.1014974.

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40

Tennant, Michele R. "Bioinformatics librarian." Reference Services Review 33, no. 1 (March 2005): 12–19. http://dx.doi.org/10.1108/00907320410519333.

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41

Alfred, Jane. "Bioinformatics lessons." Nature Reviews Genetics 2, no. 10 (October 2001): 736. http://dx.doi.org/10.1038/35093531.

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42

Valverde, Jose R. "Bioinformatics Algorithms." EMBnet.journal 20 (July 16, 2014): 781. http://dx.doi.org/10.14806/ej.20.0.781.

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43

Stafford, Ned. "Bioinformatics awarded." Genome Biology 5 (2004): spotlight—20040629–01. http://dx.doi.org/10.1186/gb-spotlight-20040629-01.

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44

Greaves, Sarah. "Virtual bioinformatics." Nature Reviews Genetics 5, no. 4 (April 2004): 246. http://dx.doi.org/10.1038/nrg1329.

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45

Youngs, Robin, and Edward Fisher. "Introducing bioinformatics." Journal of Laryngology & Otology 128, no. 10 (October 2014): 847. http://dx.doi.org/10.1017/s0022215114002618.

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46

Voolstra, C. "Microarray Bioinformatics." Briefings in Functional Genomics and Proteomics 3, no. 3 (January 1, 2004): 289–90. http://dx.doi.org/10.1093/bfgp/3.3.289.

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47

Samudrala, R. "Structural Bioinformatics." Briefings in Bioinformatics 4, no. 3 (January 1, 2003): 299–301. http://dx.doi.org/10.1093/bib/4.3.299.

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48

Mostafa, Javed. "Whither Bioinformatics?" Obstetrics & Gynecology 123, no. 6 (June 2014): 1151–52. http://dx.doi.org/10.1097/aog.0000000000000312.

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49

Salter, Hugh. "Teaching bioinformatics." Biochemical Education 26, no. 1 (January 1998): 3–10. http://dx.doi.org/10.1016/s0307-4412(97)00163-5.

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50

de Bono, Stephanie. "Bioinformatics boost." Trends in Biochemical Sciences 26, no. 7 (July 2001): 413. http://dx.doi.org/10.1016/s0968-0004(01)01914-4.

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