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Journal articles on the topic 'Bioinformatics application'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Ellis, John, and David A. Morrison. "Application of bioinformatics to parasitology." International Journal for Parasitology 35, no. 5 (2005): 463–64. http://dx.doi.org/10.1016/j.ijpara.2005.02.011.

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2

Finak, G., N. Godin, M. Hallett, et al. "BIAS: Bioinformatics Integrated Application Software." Bioinformatics 21, no. 8 (2004): 1745–46. http://dx.doi.org/10.1093/bioinformatics/bti170.

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3

Li, Huixing, Yan Xue, and Xiancai Zeng. "Investigation of data mining technique and artificial intelligence algorithm in microflora bioinformatics." E3S Web of Conferences 267 (2021): 01040. http://dx.doi.org/10.1051/e3sconf/202126701040.

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Bioinformatics has gradually received widespread attention and has shown the characteristics of a large amount of calculation and high complexity. Therefore, it is required to adopt computer algorithms in bioinformatics to improve the efficiency of bioinformatics processing problems. Big data and artificial intelligence technologies have the characteristics of supporting bioinformatics and have achieved certain results in the field of bioinformatics. Introduced the application basis of big data and artificial intelligence in bioinformatics, analyzed data collection, preprocessing, data storage and management, data analysis, and mining technology. Furthermore, typical applications in bioinformatics are discussed in terms of gene expression data analysis, genome sequence information analysis, biological sequence difference and similarity analysis, genetic data analysis, and protein structure and function prediction. Finally, the bottlenecks and challenges in the application of big data and artificial intelligence in bioinformatics are discussed, and the application prospects of related technologies in bioinformatics have prospected.

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Barrett, Steven J. "Intelligent Bioinformatics: The Application of Artificial Intelligence Techniques to Bioinformatics Problems." Genetic Programming and Evolvable Machines 7, no. 3 (2006): 283–84. http://dx.doi.org/10.1007/s10710-006-7003-4.

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Du, Rui Fang, Jing Yu Li, Jian Li Liu, and Ji Zhao Zhao. "Application of Bioinformatics in Microbial Ecology." Advanced Materials Research 955-959 (June 2014): 276–80. http://dx.doi.org/10.4028/www.scientific.net/amr.955-959.276.

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The major goal of microbial ecology is to study the structure and function of complex microbial communities. Various bioinformatics software were employed to handle a large number of genomic information emerged by using high throughput sequencing. This paper summarizes application of bioinformatics in microbial ecology and their corresponding software used in α, β-diversity studies; and finally expounds the important roles in establishment of four synthesis databases.

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Sun, Y., S. Zhao, H. Yu, G. Gao, and J. Luo. "ABCGrid: Application for Bioinformatics Computing Grid." Bioinformatics 23, no. 9 (2007): 1175–77. http://dx.doi.org/10.1093/bioinformatics/btm086.

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7

Zhang, Yi. "Application Swarming Intelligence in Bioinformatics: Survey." Journal of Bioinformatics and Intelligent Control 1, no. 1 (2012): 27–39. http://dx.doi.org/10.1166/jbic.2012.1015.

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8

Lopes, Robson da Silva, Nathalia Maria Resende, Adenilda Cristina Honorio-França, and Eduardo Luzía França. "Application of Bioinformatics in Chronobiology Research." Scientific World Journal 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/153839.

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Bioinformatics and other well-established sciences, such as molecular biology, genetics, and biochemistry, provide a scientific approach for the analysis of data generated through “omics” projects that may be used in studies of chronobiology. The results of studies that apply these techniques demonstrate how they significantly aided the understanding of chronobiology. However, bioinformatics tools alone cannot eliminate the need for an understanding of the field of research or the data to be considered, nor can such tools replace analysts and researchers. It is often necessary to conduct an evaluation of the results of a data mining effort to determine the degree of reliability. To this end, familiarity with the field of investigation is necessary. It is evident that the knowledge that has been accumulated through chronobiology and the use of tools derived from bioinformatics has contributed to the recognition and understanding of the patterns and biological rhythms found in living organisms. The current work aims to develop new and important applications in the near future through chronobiology research.

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YANG, HOWARD H., and MAXWELL P. LEE. "Application of Bioinformatics in Cancer Epigenetics." Annals of the New York Academy of Sciences 1020, no. 1 (2004): 67–76. http://dx.doi.org/10.1196/annals.1310.008.

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Vassilev, D., J. Leunissen, A. Atanassov, A. Nenov, and G. Dimov. "Application of Bioinformatics in Plant Breeding." Biotechnology & Biotechnological Equipment 19, sup3 (2005): 139–52. http://dx.doi.org/10.1080/13102818.2005.10817293.

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Selby, Peter, Rafael Abbeloos, Jan Erik Backlund, et al. "BrAPI—an application programming interface for plant breeding applications." Bioinformatics 35, no. 20 (2019): 4147–55. http://dx.doi.org/10.1093/bioinformatics/btz190.

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Abstract Motivation Modern genomic breeding methods rely heavily on very large amounts of phenotyping and genotyping data, presenting new challenges in effective data management and integration. Recently, the size and complexity of datasets have increased significantly, with the result that data are often stored on multiple systems. As analyses of interest increasingly require aggregation of datasets from diverse sources, data exchange between disparate systems becomes a challenge. Results To facilitate interoperability among breeding applications, we present the public plant Breeding Application Programming Interface (BrAPI). BrAPI is a standardized web service API specification. The development of BrAPI is a collaborative, community-based initiative involving a growing global community of over a hundred participants representing several dozen institutions and companies. Development of such a standard is recognized as critical to a number of important large breeding system initiatives as a foundational technology. The focus of the first version of the API is on providing services for connecting systems and retrieving basic breeding data including germplasm, study, observation, and marker data. A number of BrAPI-enabled applications, termed BrAPPs, have been written, that take advantage of the emerging support of BrAPI by many databases. Availability and implementation More information on BrAPI, including links to the specification, test suites, BrAPPs, and sample implementations is available at https://brapi.org/. The BrAPI specification and the developer tools are provided as free and open source.

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Koch, Ina, Miguel Andrade-Navarro, Marcel H. Schulz, and Kathi Zarnack. "Bioinformatics in theory and application – highlights of the 36th German Conference on Bioinformatics." Biological Chemistry 402, no. 8 (2021): 869–70. http://dx.doi.org/10.1515/hsz-2021-0298.

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13

Mohialden, Yasmin Makki, Huda Abdulaali Abdulbaqi, Rafaa Ismael Yahya, Basim K. Abbas, and Saba Abdulbaqi Salman. "Cloud Platform Specification based on Bioinformatics Application." Indian Journal of Public Health Research & Development 9, no. 12 (2018): 1263. http://dx.doi.org/10.5958/0976-5506.2018.02025.9.

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Swindells, Mark, Mark Rae, Martyn Pearce, Stuart Moodie, Rob Miller, and Pat Leach. "Application of high-throughput computing in bioinformatics." Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences 360, no. 1795 (2002): 1179–89. http://dx.doi.org/10.1098/rsta.2002.0987.

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15

KHLEBODAROVA, TAMARA M., NINA V. TIKUNOVA, ALLA V. KACHKO, IRINA L. STEPANENKO, NIKOLAI L. PODKOLODNY, and NIKOLAI A. KOLCHANOV. "APPLICATION OF BIOINFORMATICS RESOURCES FOR GENOSENSOR DESIGN." Journal of Bioinformatics and Computational Biology 05, no. 02b (2007): 507–20. http://dx.doi.org/10.1142/s0219720007002813.

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Two novel databases, GenSensor and ConSensor, have been developed. GenSensor accumulates information on the sensitivities of the prokaryotic genes to external stimuli and may facilitate designing of novel genosensors; ConSensor contains data about the structure and efficiency of the available genosensor plasmid constructs. Using these databases, candidate genes for the design of novel multiple functional genosensors were searched, and the Escherichia coli dps gene was chosen as the candidate. The genetic construct derived from its promoter was developed and tested for its sensitivity to various stress agents: hydrogen peroxide (oxidative stress), phenol (protein and membrane damaging), and mitomycin C (DNA damaging). This genosensor was found to be sensitive to all stress conditions applied confirming its ability to serve as multi-functional genosensor. The GenSensor and ConSensor databases are available at .

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Wiemer, Jan C., and Alexander Prokudin. "Bioinformatics in proteomics: application, terminology, and pitfalls." Pathology - Research and Practice 200, no. 2 (2004): 173–78. http://dx.doi.org/10.1016/j.prp.2004.01.012.

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Toldo, Luca, and Friedrich Rippmann. "Integrated bioinformatics application for automated target discovery." Journal of the American Society for Information Science and Technology 56, no. 5 (2005): 483–92. http://dx.doi.org/10.1002/asi.20137.

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18

Chen, Yi-Ping Phoebe. "Guest Editorial: Application and Development of Bioinformatics." IEEE/ACM Transactions on Computational Biology and Bioinformatics 9, no. 5 (2012): 1265. http://dx.doi.org/10.1109/tcbb.2012.96.

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Alvarez V, Gloria Inés, Jorge Hernán Victoria M, Enrique Bravo M, and Pedro García G. "HyRPNI Algorithm and an Application to Bioinformatics." Revista de Ingeniería, no. 33 (January 2011): 44–52. http://dx.doi.org/10.16924/revinge.33.5.

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20

Hu, Rongdong, Guangming Liu, Jingfei Jiang, and Lixin Wang. "G2LC: Resources Autoscaling for Real Time Bioinformatics Applications in IaaS." Computational and Mathematical Methods in Medicine 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/549026.

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Cloud computing has started to change the way how bioinformatics research is being carried out. Researchers who have taken advantage of this technology can process larger amounts of data and speed up scientific discovery. The variability in data volume results in variable computing requirements. Therefore, bioinformatics researchers are pursuing more reliable and efficient methods for conducting sequencing analyses. This paper proposes an automated resource provisioning method, G2LC, for bioinformatics applications in IaaS. It enables application to output the results in a real time manner. Its main purpose is to guarantee applications performance, while improving resource utilization. Real sequence searching data of BLAST is used to evaluate the effectiveness of G2LC. Experimental results show that G2LC guarantees the application performance, while resource is saved up to 20.14%.

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21

Mulder, Nicola, Russell Schwartz, Michelle D. Brazas, et al. "The development and application of bioinformatics core competencies to improve bioinformatics training and education." PLOS Computational Biology 14, no. 2 (2018): e1005772. http://dx.doi.org/10.1371/journal.pcbi.1005772.

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22

You, Yuan, Hongmin Cai, and Jiazhou Chen. "Low Rank Representation and Its Application in Bioinformatics." Current Bioinformatics 13, no. 5 (2018): 508–17. http://dx.doi.org/10.2174/1574893612666171121155347.

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23

Kehinde, Hassan, and Kazeem Alagbe. "Residue Number System: An Important Application in Bioinformatics." International Journal of Computer Applications 179, no. 10 (2018): 28–33. http://dx.doi.org/10.5120/ijca2018916106.

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24

Song, Chang-xin. "Application Analysis of Feature Selection (FS) in Bioinformatics." Journal of Physics: Conference Series 1345 (November 2019): 052017. http://dx.doi.org/10.1088/1742-6596/1345/5/052017.

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25

Ahmed, Rabie, Munir Amin, and Mohammed Al-Shomrani. "A Web Mining Application to Classify Bioinformatics Datasets." Indian Journal of Science and Technology 11, no. 4 (2018): 1–10. http://dx.doi.org/10.17485/ijst/2018/v11i4/104043.

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26

Maurady, A. "Bioinformatics Application in Detection of Food-Borne Agents." Journal of Food Quality and Hazards Conrol 5, no. 4 (2018): 161. http://dx.doi.org/10.29252/jfqhc.5.4.8.

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27

Filntisi, Arianna, Nikitas Papangelopoulos, Elena Bencurova, et al. "State-of-the-Art Neural Networks Applications in Biology." International Journal of Systems Biology and Biomedical Technologies 2, no. 4 (2013): 63–85. http://dx.doi.org/10.4018/ijsbbt.2013100105.

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Artificial neural networks (ANNs) are a well-established computational method inspired by the structure and function of biological central nervous systems. Since their conception, ANNs have been utilized in a vast variety of applications due to their impressive information processing abilities. A vibrant field, ANNs have been utilized in bioinformatics, a general term for describing the combination of informatics, biology and medicine. This article is an effort to investigate recent advances in the area of bioinformatical applications of ANNs, with emphasis in disease diagnosis, genetics, proteomics, and chemoinformatics. The combination of neural networks and game theory in some of these application is also discussed.

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28

Rovin, Richard A., and Robert Winn. "Pokemon expression in malignant glioma: an application of bioinformatics methods." Neurosurgical Focus 19, no. 4 (2005): 1–2. http://dx.doi.org/10.3171/foc.2005.19.4.9.

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Object In this report the authors review the role of bioinformatics in the design of a research project in which the molecular genetics of malignant glioma were studied. A project to characterize Pokemon expression in malignant glioma was developed, refined, and implemented using bioinformatics methods. Methods Using the resources available from the National Center for Biotechnology Information, the messenger RNA (mRNA) sequence for Pokemon was determined. With this information and online primer design tools, novel primers were designed that would specifically amplify Pokemon mRNA by using reverse transcription–polymerase chain reaction assays. Conclusions The promise of bioinformatics is in the rapid and widespread dissemination and analysis of genomic information. This information is then used in research investigating the genetic basis of disease. In this paper the authors review the bioinformatics methods used in their study of Pokemon expression in malignant glioma.

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29

Kamble, Ashwini, and Rajesh Khairkar. "Basics of Bioinformatics in Biological Research." International Journal of Applied Sciences and Biotechnology 4, no. 4 (2017): 425–29. http://dx.doi.org/10.3126/ijasbt.v4i4.16252.

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The concept of laboratory rat is giving way to the computer mouse arose after the famous handshake between Clinton-Blair for the completion of the human genome in April 2003. Bioinformatics is defined as the application of computational techniques to understand and organize the information associated with biological macromolecules.There is availability of large databases of genomic information which has enabled research efforts for discovering methods for diagnosis and treatment of human diseases using DNA microarrays and proteomics experiments. But there are various problems while doing this like it’s always challenging to develop proper and sophisticated analysis method which can properly use genomic data bases considering its and heterogeneity of the data.The main purpose of this first paper is to explore and explain Bioinformatics in a more scientific way, and try highlighting applications of bioinformatics in the medical sector.Int J Appl Sci Biotechnol, Vol 4(4): 425-429

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30

Zur, H., and T. Tuller. "RFMapp: ribosome flow model application." Bioinformatics 28, no. 12 (2012): 1663–64. http://dx.doi.org/10.1093/bioinformatics/bts185.

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31

Dev, U., A. Sultana, D. Saha, and NK Mitra. "Application of fuzzy logic in medical data interpretation." Bangladesh Journal of Scientific and Industrial Research 49, no. 3 (2015): 137–46. http://dx.doi.org/10.3329/bjsir.v49i3.22127.

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This paper serves the purpose of presentation of a general view of the current applications of fuzzy logic in medicine and bioinformatics. Using fuzzy logic, we particularly review medical aspects. We then recall the geometrical interpretation of fuzzy sets as points in a fuzzy hypercube and present two concrete illustrations in medicine (drug addictions) and in bioinformatics (comparison of genomes). DOI: http://dx.doi.org/10.3329/bjsir.v49i3.22127 Bangladesh J. Sci. Ind. Res. 49(3), 137-146, 2014

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32

Blackwell, Bruce. "Bioinformatics and Oracle Extensibility." Asia-Pacific Biotech News 07, no. 03 (2003): 98–100. http://dx.doi.org/10.1142/s0219030303000260.

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The Oracle relational database management system, with object-oriented extensions and numerous application-driven enhancements, plays a critical role worldwide in managing the exploding volumes of bioinformatics data. There are many features of the Oracle product which support the bioinformatics community directly already and there are several features which could be exploited more thoroughly by users, service vendors, and Oracle itself to extend that level of support. This paper will present an overview of Oracle features which support storage of bioinformatics data and will discuss extensibility features which give the product room to grow. Some attention will be given to Oracle's own efforts to use that extensibility to exploit emerging standardization of many of the complex data and computation requirements of the life sciences.

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Cattley, S., and J. W. Arthur. "BioManager: the use of a bioinformatics web application as a teaching tool in undergraduate bioinformatics training." Briefings in Bioinformatics 8, no. 6 (2007): 457–65. http://dx.doi.org/10.1093/bib/bbm039.

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34

Potdar, Swapnil, Aleksandr Ianevski, John-Patrick Mpindi, et al. "Breeze: an integrated quality control and data analysis application for high-throughput drug screening." Bioinformatics 36, no. 11 (2020): 3602–4. http://dx.doi.org/10.1093/bioinformatics/btaa138.

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Abstract Summary High-throughput screening (HTS) enables systematic testing of thousands of chemical compounds for potential use as investigational and therapeutic agents. HTS experiments are often conducted in multi-well plates that inherently bear technical and experimental sources of error. Thus, HTS data processing requires the use of robust quality control procedures before analysis and interpretation. Here, we have implemented an open-source analysis application, Breeze, an integrated quality control and data analysis application for HTS data. Furthermore, Breeze enables a reliable way to identify individual drug sensitivity and resistance patterns in cell lines or patient-derived samples for functional precision medicine applications. The Breeze application provides a complete solution for data quality assessment, dose–response curve fitting and quantification of the drug responses along with interactive visualization of the results. Availability and implementation The Breeze application with video tutorial and technical documentation is accessible at https://breeze.fimm.fi; the R source code is publicly available at https://github.com/potdarswapnil/Breeze under GNU General Public License v3.0. Contact swapnil.potdar@helsinki.fi Supplementary information Supplementary data are available at Bioinformatics online.

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35

Guerrero, Ginés D., Baldomero Imbernón, Horacio Pérez-Sánchez, Francisco Sanz, José M. García, and José M. Cecilia. "A Performance/Cost Evaluation for a GPU-Based Drug Discovery Application on Volunteer Computing." BioMed Research International 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/474219.

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Bioinformatics is an interdisciplinary research field that develops tools for the analysis of large biological databases, and, thus, the use of high performance computing (HPC) platforms is mandatory for the generation of useful biological knowledge. The latest generation of graphics processing units (GPUs) has democratized the use of HPC as they push desktop computers to cluster-level performance. Many applications within this field have been developed to leverage these powerful and low-cost architectures. However, these applications still need to scale to larger GPU-based systems to enable remarkable advances in the fields of healthcare, drug discovery, genome research, etc. The inclusion of GPUs in HPC systems exacerbates power and temperature issues, increasing the total cost of ownership (TCO). This paper explores the benefits of volunteer computing to scale bioinformatics applications as an alternative to own large GPU-based local infrastructures. We use as a benchmark a GPU-based drug discovery application called BINDSURF that their computational requirements go beyond a single desktop machine. Volunteer computing is presented as a cheap and valid HPC system for those bioinformatics applications that need to process huge amounts of data and where the response time is not a critical factor.

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36

Cooper, Sam, Alexis R. Barr, Robert Glen, and Chris Bakal. "NucliTrack: an integrated nuclei tracking application." Bioinformatics 33, no. 20 (2017): 3320–22. http://dx.doi.org/10.1093/bioinformatics/btx404.

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Miller, David, J. "Emergent unsupervised clustering paradigms with potential application to bioinformatics." Frontiers in Bioscience 13, no. 13 (2008): 677. http://dx.doi.org/10.2741/2711.

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Kim, Ki-Bong. "Bioinformatics : Latest Application and Interdisciplinary Field of Computer Science." Journal of the Korea Academia-Industrial cooperation Society 11, no. 3 (2010): 971–77. http://dx.doi.org/10.5762/kais.2010.11.3.971.

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Baagyere, E. Y., K. O. Boateng, and K. A. Gbolagade. "Bioinformatics: An Important Application Area of Residue Number System." Journal of Engineering and Applied Sciences 6, no. 2 (2011): 174–79. http://dx.doi.org/10.3923/jeasci.2011.174.179.

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40

Emery, Laura R., and Sarah L. Morgan. "The application of project-based learning in bioinformatics training." PLOS Computational Biology 13, no. 8 (2017): e1005620. http://dx.doi.org/10.1371/journal.pcbi.1005620.

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41

Kuo, W. P. "Overview of Bioinformatics and its Application to Oral Genomics." Advances in Dental Research 17, no. 1 (2003): 89–94. http://dx.doi.org/10.1177/154407370301700121.

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The “informatics revolution” in both bioinformatics and dental informatics will eventually change the way we practice dentistry. This convergence will play a pivotal role in creating a bridge of opportunity by integrating scientific and clinical specialties to promote the advances in treatment, risk assessment, diagnosis, therapeutics, and oral health-care outcome. Bioinformatics has been an emerging field in the biomedical research community and has been gaining momentum in dental medicine. This area has created a steady stream of large and complex genomic data, which has transformed the way a clinical or basic science researcher approaches genomic research. This application to dental medicine, termed “oral genomics”, can aid in the molecular understanding of the genes and proteins, their interactions, pathways, and networks that are responsible for the development and progression of oral diseases and disorders. As the result of the Human Genome Project, new advances have prompted high-throughput technologies, such as DNA microarrays, which have become accepted tools in the biomedical research community. This manuscript reviews the two most commonly used microarray technologies, basic microarray data analysis, and the results from several ongoing oral cancer genomic studies.

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Abyzov, A., M. Errami, C. M. Leslin, and V. A. Ilyin. "Friend, an integrated analytical front-end application for bioinformatics." Bioinformatics 21, no. 18 (2005): 3677–78. http://dx.doi.org/10.1093/bioinformatics/bti602.

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Mamitsuka, Hiroshi, and Naoki Abe. "Active ensemble learning: Application to data mining and bioinformatics." Systems and Computers in Japan 38, no. 11 (2007): 100–108. http://dx.doi.org/10.1002/scj.10355.

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Qi, Dong. "Ngene: An Integrated Bioinformatics Platform for Analysis Application Sequence." Biotech Software & Internet Report 3, no. 1 (2002): 10–13. http://dx.doi.org/10.1089/152791602317250360.

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Wu, Duojiao, and Xiangdong Wang. "Application of clinical bioinformatics in lung cancer-specific biomarkers." Cancer and Metastasis Reviews 34, no. 2 (2015): 209–16. http://dx.doi.org/10.1007/s10555-015-9564-2.

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Swiercz, Aleksandra, Edmund K. Burke, Mateusz Cichenski, et al. "Unified encoding for hyper-heuristics with application to bioinformatics." Central European Journal of Operations Research 22, no. 3 (2013): 567–89. http://dx.doi.org/10.1007/s10100-013-0321-8.

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Wei, Leyi, Shixiang Wan, Jiasheng Guo, and Kelvin KL Wong. "A novel hierarchical selective ensemble classifier with bioinformatics application." Artificial Intelligence in Medicine 83 (November 2017): 82–90. http://dx.doi.org/10.1016/j.artmed.2017.02.005.

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Takenaka, M., and E. Imai. "Functional genomics in nephrology: construction and application of bioinformatics." Clinical and Experimental Nephrology 4, no. 4 (2000): 281–85. http://dx.doi.org/10.1007/s101570070002.

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Gorbalenya, Alexander E., Philippe Lieutaud, Mark R. Harris, et al. "Practical application of bioinformatics by the multidisciplinary VIZIER consortium." Antiviral Research 87, no. 2 (2010): 95–110. http://dx.doi.org/10.1016/j.antiviral.2010.02.005.

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Xu, Qian, and Yi Zhang. "Research on the Application of Computer Science in Bioinformatics." Journal of Physics: Conference Series 1915, no. 3 (2021): 032042. http://dx.doi.org/10.1088/1742-6596/1915/3/032042.

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