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Journal articles on the topic 'Bioinformatics applications'

Author: Grafiati
Published: 4 June 2021
Last updated: 9 February 2022

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1

Nedjah, Nadia, and Luiza de Macedo Mourelle. "Hardware for bioinformatics applications." Integration 46, no. 3 (2013): 219. http://dx.doi.org/10.1016/j.vlsi.2013.03.003.

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2

Sung, Wing-Kin. "Bioinformatics Applications in Genomics." Computer 45, no. 6 (2012): 57–63. http://dx.doi.org/10.1109/mc.2012.151.

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3

Parekh, Bhagavati. "Bioinformatics Applications in life Sciences: Concepts and Stance." Paripex - Indian Journal Of Research 3, no. 3 (2012): 72–74. http://dx.doi.org/10.15373/22501991/mar2014/78.

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4

Baiskhiyar, Divya, and Ravi Kumar. "Quantum Computing-Applications in Bioinformatics." International Journal of Computer Applications 177, no. 12 (2019): 26–28. http://dx.doi.org/10.5120/ijca2019919527.

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5

Ekanayake, J., T. Gunarathne, and J. Qiu. "Cloud Technologies for Bioinformatics Applications." IEEE Transactions on Parallel and Distributed Systems 22, no. 6 (2011): 998–1011. http://dx.doi.org/10.1109/tpds.2010.178.

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6

Stromback, Lena, and Juliana Freire. "XML Management for Bioinformatics Applications." Computing in Science & Engineering 13, no. 5 (2011): 12–23. http://dx.doi.org/10.1109/mcse.2010.100.

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7

Brenner, Chad. "Applications of Bioinformatics in Cancer." Cancers 11, no. 11 (2019): 1630. http://dx.doi.org/10.3390/cancers11111630.

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8

Baker, P. G., C. A. Goble, S. Bechhofer, N. W. Paton, R. Stevens, and A. Brass. "An ontology for bioinformatics applications." Bioinformatics 15, no. 6 (1999): 510–20. http://dx.doi.org/10.1093/bioinformatics/15.6.510.

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9

UMAR, ASAD. "Applications of Bioinformatics in Cancer Detection: A Lexicon of Bioinformatics Terms." Annals of the New York Academy of Sciences 1020, no. 1 (2004): 263–76. http://dx.doi.org/10.1196/annals.1310.021.

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10

Knudsen, Thomas, and Amar Singh. "Comparative bioinformatics—Applications for developmental toxicology." Toxicology Letters 172 (October 2007): S14—S15. http://dx.doi.org/10.1016/j.toxlet.2007.05.059.

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11

Khatri, P., M. M. Sarwal, and A. J. Butte. "Applications of Translational Bioinformatics in Transplantation." Clinical Pharmacology & Therapeutics 90, no. 2 (2011): 323–27. http://dx.doi.org/10.1038/clpt.2011.120.

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12

Trelles, O. "On the parallelisation of bioinformatics applications." Briefings in Bioinformatics 2, no. 2 (2001): 181–94. http://dx.doi.org/10.1093/bib/2.2.181.

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13

Antonio, B. A., N. Namiki, T. Matsumoto, and T. Sasaki. "Rice Bioinformatics: From Sequences to Applications." Asia-Pacific Biotech News 06, no. 24 (2002): 914–20. http://dx.doi.org/10.1142/s0219030302001908.

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Rice genomics data are rapidly expanding in number and complexity. This is further intensified as the international collaboration aimed at sequencing the entire genome accelerates the release of sequence data to the public domain. An overview of available resources for processing, elucidating and propagating genomic information in rice is described here. As the genome sequencing nears its completion, the future challenges in rice bioinformatics lies in developing an informatics infrastructure that would facilitate integration across various data types and sources, and eventually lead to the development of viable strategies for improvement of rice and other major cereal crops.
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14

Butte, Atul J. "Translational bioinformatics applications in genome medicine." Genome Medicine 1, no. 6 (2009): 64. http://dx.doi.org/10.1186/gm64.

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15

Yukinawa, N., T. Takenouchi, S. Oba, and S. Ishii. "Combining multiple decisions: applications to bioinformatics." Journal of Physics: Conference Series 95 (January 1, 2008): 012018. http://dx.doi.org/10.1088/1742-6596/95/1/012018.

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16

Chalasani, Suresh, and Robert Barber. "Architectures for Java‐based bioinformatics applications." Industrial Management & Data Systems 104, no. 7 (2004): 578–88. http://dx.doi.org/10.1108/02635570410550241.

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17

Pinho, Jorge, João Luis Sobral, and Miguel Rocha. "Parallel evolutionary computation in bioinformatics applications." Computer Methods and Programs in Biomedicine 110, no. 2 (2013): 183–91. http://dx.doi.org/10.1016/j.cmpb.2012.10.001.

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18

Pant, Dhruv Chander, and O. P. Gupta. "Performance Analysis of Parallelized Bioinformatics Applications." Asian Journal of Computer Science and Technology 7, no. 2 (2018): 70–74. http://dx.doi.org/10.51983/ajcst-2018.7.2.1881.

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The main challenges bioinformatics applications facing today are to manage, analyze and process a huge volume of genome data. This type of analysis and processing is very difficult using general purpose computer systems. So the need of distributed computing, cloud computing and high performance computing in bioinformatics applications arises. Now distributed computers, cloud computers and multi-core processors are available at very low cost to deal with bulk amount of genome data. Along with these technological developments in distributed computing, many efforts are being done by the scientists and bioinformaticians to parallelize and implement the algorithms to take the maximum advantage of the additional computational power. In this paper a few bioinformatics algorithms have been discussed. The parallelized implementations of these algorithms have been explained. The performance of these parallelized algorithms has been also analyzed. It has been also observed that in parallel implementations of the various bioinformatics algorithms, impact of communication subsystems with respect to the job sizes should also be analyzed.
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19

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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20

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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21

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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22

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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23

Tabassum Khan, Nida. "The Emerging Role of Bioinformatics in Biotechnology." Journal of Biotechnology and Biomedical Science 1, no. 3 (2018): 13–24. http://dx.doi.org/10.14302/issn.2576-6694.jbbs-18-2173.

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Bioinformatic tools is widely used to manage the enormous genomic and proteomic data involving DNA/protein sequences management, drug designing, homology modelling, motif/domain prediction ,docking, annotation and dynamic simulation etc. Bioinformatics offers a wide range of applications in numerous disciplines such as genomics. Proteomics, comparative genomics, nutrigenomics, microbial genome, biodefense, forensics etc. Thus it offers promising future to accelerate scientific research in biotechnology
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24

Sirangelo, Tiziana Maria, and Grazia Calabro. "Soil Metagegomics: Approaches, Bioinformatics Tools and Applications." Scholars Journal of Agriculture and Veterinary Sciences 7, no. 6 (2020): 125–32. http://dx.doi.org/10.36347/sjavs.2020.v07i06.003.

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25

Servant, N., P. Hupé, M. Kamal, C. LeTourneau, and E. Barillot. "SP002 Bioinformatics analysis for real-time applications." European Journal of Cancer 49 (November 2013): S1. http://dx.doi.org/10.1016/s0959-8049(13)70080-7.

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26

Rhee, Seung Yon, Julie Dickerson, and Dong Xu. "BIOINFORMATICS AND ITS APPLICATIONS IN PLANT BIOLOGY." Annual Review of Plant Biology 57, no. 1 (2006): 335–60. http://dx.doi.org/10.1146/annurev.arplant.56.032604.144103.

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27

Mesa, Oscar, Hermans Díaz, Pedro Masoliver, and Carlos Clar. "Bioinformatics in phylogeography: analitical methods and applications." BMC Bioinformatics 6, Suppl 3 (2005): P22. http://dx.doi.org/10.1186/1471-2105-6-s3-p22.

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28

Sousa, Sílvia A., Jorge H. Leitão, Raul C. Martins, João M. Sanches, Jasjit S. Suri, and Alejandro Giorgetti. "Bioinformatics Applications in Life Sciences and Technologies." BioMed Research International 2016 (2016): 1–2. http://dx.doi.org/10.1155/2016/3603827.

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29

Schmidt, Bertil, Lin Feng, Amey Laud, and Yusdi Santoso. "Development of distributed bioinformatics applications with GMP." Concurrency and Computation: Practice and Experience 16, no. 9 (2004): 945–59. http://dx.doi.org/10.1002/cpe.815.

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30

Ji, Y., C. Wu, P. Liu, J. Wang, and K. R. Coombes. "Applications of beta-mixture models in bioinformatics." Bioinformatics 21, no. 9 (2005): 2118–22. http://dx.doi.org/10.1093/bioinformatics/bti318.

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31

Andrews, Bincy P. "Paralyzing Bioinformatics Applications Using Conducive Hadoop Cluster." IOSR Journal of Computer Engineering 14, no. 6 (2013): 89–93. http://dx.doi.org/10.9790/0661-1468993.

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32

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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33

Joppich, Markus, and Ralf Zimmer. "From command-line bioinformatics to bioGUI." PeerJ 7 (November 21, 2019): e8111. http://dx.doi.org/10.7717/peerj.8111.

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Bioinformatics is a highly interdisciplinary field providing (bioinformatics) applications for scientists from many disciplines. Installing and starting applications on the command-line (CL) is inconvenient and/or inefficient for many scientists. Nonetheless, most methods are implemented with a command-line interface only. Providing a graphical user interface (GUI) for bioinformatics applications is one step toward routinely making CL-only applications available to more scientists and, thus, toward a more effective interdisciplinary work. With our bioGUI framework we address two main problems of using CL bioinformatics applications: First, many tools work on UNIX-systems only, while many scientists use Microsoft Windows. Second, scientists refrain from using CL tools which, however, could well support them in their research. With bioGUI install modules and templates, installing and using CL tools is made possible for most scientists—even on Windows, due to bioGUI’s support for Windows Subsystem for Linux. In addition, bioGUI templates can easily be created, making the bioGUI framework highly rewarding for developers. From the bioGUI repository it is possible to download, install and use bioinformatics tools with just a few clicks.
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34

Aravindhan, G., G. Ramesh Kumar, R. Sathish Kumar, and K. Subha. "AJAX Interface: A Breakthrough in Bioinformatics Web Applications." Proteomics Insights 2 (January 2009): PRI.S2261. http://dx.doi.org/10.4137/pri.s2261.

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Bioinformatics applications are generally multi-server dependants and will have to communicate several information repositories to carry out any analyses. These applications remain computationally intensive and time consuming as they engage lots of data transfer. Hence they face a major bottleneck when ported as web applications. Browser based web applications normally feature the classical request-response approach. If the response becomes late, as it is expected to happen in the case of long running Bioinformatics programs, Apache will get tired and a request timeout error might occur. Alternate approaches like “Client-Pull” models that involve polling strategy with the unpredictable amount of page refreshes, only tend to intensify the network traffic. Hence a technology that is intelligent enough to support the varied exhaustive Bioinformatics processes becomes highly essential. In this review, we propose how AJAX can afford a laconic framework within the Bioinformatics applications to completely reduce the page refresh nuisance and provide a better user experience.
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35

Johansson, Ingvar. "Bioinformatics and biological reality." Journal of Biomedical Informatics 39, no. 3 (2006): 274–87. http://dx.doi.org/10.1016/j.jbi.2005.08.005.

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36

Kumar Pandey, Binay, Sanjay Kumar Pandey, and Digvijay Pandey. "A Survey of Bioinformatics Applications on Parallel Architectures." International Journal of Computer Applications 23, no. 4 (2011): 21–25. http://dx.doi.org/10.5120/2877-3744.

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37

Singh, Jaswant, and S. S. Sengar. "Applications Of Bioinformatics In Animal Science-A Review." Biotech Today : An International Journal of Biological Sciences 6, no. 2 (2016): 20. http://dx.doi.org/10.5958/2322-0996.2016.00020.x.

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38

Papangelopoulos, Nikitas, Dimitrios Vlachakis, Arianna Filntisi, et al. "State-of-the-Art GPGPU Applications in Bioinformatics." International Journal of Systems Biology and Biomedical Technologies 2, no. 4 (2013): 24–48. http://dx.doi.org/10.4018/ijsbbt.2013100103.

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The exponential growth of available biological data in recent years coupled with their increasing complexity has made their analysis a computationally challenging process. Traditional central processing unist (CPUs) are reaching their limit in processing power and are not designed primarily for multithreaded applications. Graphics processing units (GPUs) on the other hand are affordable, scalable computer powerhouses that, thanks to the ever increasing demand for higher quality graphics, have yet to reach their limit. Typically high-end CPUs have 8-16 cores, whereas GPUs can have more than 2,500 cores. GPUs are also, by design, highly parallel, multicore and multithreaded, able of handling thousands of threads doing the same calculation on different subsets of a large data set. This ability is what makes them perfectly suited for biological analysis tasks. Lately this potential has been realized by many bioinformatics researches and a huge variety of tools and algorithms have been ported to GPUs, or designed from the ground up to maximize the usage of available cores. Here, we present a comprehensive review of available bioinformatics tools ranging from sequence and image analysis to protein structure prediction and systems biology that use NVIDIA Compute Unified Device Architecture (CUDA) general-purpose computing on graphics processing units (GPGPU) programming language.
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39

Xiao, Xuan, Pu Wang, and Kuo-Chen Chou. "Cellular Automata and Its Applications in Protein Bioinformatics." Current Protein & Peptide Science 12, no. 6 (2011): 508–19. http://dx.doi.org/10.2174/138920311796957720.

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40

Borkar, Pradnya, and Vijaya Bhute. "A STUDY OF BIOINFORMATICS APPLICATIONS AND MULTICORE ARCHITECTURE." International Journal of Engineering Applied Sciences and Technology 4, no. 2 (2019): 88–92. http://dx.doi.org/10.33564/ijeast.2019.v04i02.015.

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41

Mr. A. Harish, chander. "ADVANCEMENTS IN APPLICATIONS OF MICROBIOLOGY AND BIOINFORMATICS INPHARMACOLOGY." International Journal of pharma and Bio Sciences 10, no. 1 (2020): 1–222. http://dx.doi.org/10.22376/ijpbs/ijlpr/sp08/jan/2020.1-222.

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42

Walsh, Ian, Roisin O’Flaherty, and Pauline M. Rudd. "Bioinformatics applications to aid high-throughput glycan profiling." Perspectives in Science 11 (January 2017): 31–39. http://dx.doi.org/10.1016/j.pisc.2016.01.013.

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43

Tucker, Allan, and Daniel Duplisea. "Bioinformatics tools in predictive ecology: applications to fisheries." Philosophical Transactions of the Royal Society B: Biological Sciences 367, no. 1586 (2012): 279–90. http://dx.doi.org/10.1098/rstb.2011.0184.

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There has been a huge effort in the advancement of analytical techniques for molecular biological data over the past decade. This has led to many novel algorithms that are specialized to deal with data associated with biological phenomena, such as gene expression and protein interactions. In contrast, ecological data analysis has remained focused to some degree on off-the-shelf statistical techniques though this is starting to change with the adoption of state-of-the-art methods, where few assumptions can be made about the data and a more explorative approach is required, for example, through the use of Bayesian networks. In this paper, some novel bioinformatics tools for microarray data are discussed along with their ‘crossover potential’ with an application to fisheries data. In particular, a focus is made on the development of models that identify functionally equivalent species in different fish communities with the aim of predicting functional collapse.
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44

Araújo, Aletéia, Michel Rosa, Breno Moura, Guilherme Vergara, Maristela Holanda, and Maria Emília Walter. "BioNimbuZ: a federated cloud platform for bioinformatics applications." International Journal of Data Mining and Bioinformatics 18, no. 2 (2017): 144. http://dx.doi.org/10.1504/ijdmb.2017.086460.

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45

Walter, Maria Emília, Maristela Holanda, Guilherme Vergara, Michel Rosa, Aletéia Araújo, and Breno Moura. "BioNimbuZ: a federated cloud platform for bioinformatics applications." International Journal of Data Mining and Bioinformatics 18, no. 2 (2017): 144. http://dx.doi.org/10.1504/ijdmb.2017.10007505.

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46

Liu, Xiaoqing, Jun Wu, Feiyang Gu, Jie Wang, and Zengyou He. "Discriminative pattern mining and its applications in bioinformatics." Briefings in Bioinformatics 16, no. 5 (2014): 884–900. http://dx.doi.org/10.1093/bib/bbu042.

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47

Laishram, Minerva. "Survey on Various Applications of Hadoop in Bioinformatics." International Journal of Computer Applications 129, no. 6 (2015): 20–22. http://dx.doi.org/10.5120/ijca2015906929.

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48

Goh, Wilson Wen Bin, and Limsoon Wong. "Advanced bioinformatics methods for practical applications in proteomics." Briefings in Bioinformatics 20, no. 1 (2017): 347–55. http://dx.doi.org/10.1093/bib/bbx128.

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49

Thomas, Minta, Anneleen Daemen, and Bart De Moor. "Maximum Likelihood Estimation of GEVD: Applications in Bioinformatics." IEEE/ACM Transactions on Computational Biology and Bioinformatics 11, no. 4 (2014): 673–80. http://dx.doi.org/10.1109/tcbb.2014.2304292.

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50

EZZIANE, Z. "Applications of artificial intelligence in bioinformatics: A review." Expert Systems with Applications 30, no. 1 (2006): 2–10. http://dx.doi.org/10.1016/j.eswa.2005.09.042.

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