Journal articles: 'Bioinformatics (technology)'

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Shulaev, V. "Metabolomics technology and bioinformatics." Briefings in Bioinformatics 7, no. 2 (2006): 128–39. http://dx.doi.org/10.1093/bib/bbl012.

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Ahn, Soyeon. "Introduction to bioinformatics: sequencing technology." Asia Pacific Allergy 1, no. 2 (2011): 93. http://dx.doi.org/10.5415/apallergy.2011.1.2.93.

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Karasavvas, K. A., R. Baldock, and A. Burger. "Bioinformatics integration and agent technology." Journal of Biomedical Informatics 37, no. 3 (2004): 205–19. http://dx.doi.org/10.1016/j.jbi.2004.04.003.

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Pop, Mihai, and Steven L. Salzberg. "Bioinformatics challenges of new sequencing technology." Trends in Genetics 24, no. 3 (2008): 142–49. http://dx.doi.org/10.1016/j.tig.2007.12.006.

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Azad, Rajeev K., and Vladimir Shulaev. "Metabolomics technology and bioinformatics for precision medicine." Briefings in Bioinformatics 20, no. 6 (2018): 1957–71. http://dx.doi.org/10.1093/bib/bbx170.

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Abstract Precision medicine is rapidly emerging as a strategy to tailor medical treatment to a small group or even individual patients based on their genetics, environment and lifestyle. Precision medicine relies heavily on developments in systems biology and omics disciplines, including metabolomics. Combination of metabolomics with sophisticated bioinformatics analysis and mathematical modeling has an extreme power to provide a metabolic snapshot of the patient over the course of disease and treatment or classifying patients into subpopulations and subgroups requiring individual medical treatment. Although a powerful approach, metabolomics have certain limitations in technology and bioinformatics. We will review various aspects of metabolomics technology and bioinformatics, from data generation, bioinformatics analysis, data fusion and mathematical modeling to data management, in the context of precision medicine.

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Counsell, Damian. "Meeting Review: 2002 O'Reilly Bioinformatics Technology Conference." Comparative and Functional Genomics 3, no. 3 (2002): 264–69. http://dx.doi.org/10.1002/cfg.170.

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At the end of January I travelled to the States to speak at and attend the first O’Reilly Bioinformatics Technology Conference [14]. It was a large, well-organized and diverse meeting with an interesting history. Although the meeting was not a typical academic conference, its style will, I am sure, become more typical of meetings in both biological and computational sciences.Speakers at the event included prominent bioinformatics researchers such as Ewan Birney, Terry Gaasterland and Lincoln Stein; authors and leaders in the open source programming community like Damian Conway and Nat Torkington; and representatives from several publishing companies including the Nature Publishing Group, Current Science Group and the President of O’Reilly himself, Tim O’Reilly. There were presentations, tutorials, debates, quizzes and even a ‘jam session’ for musical bioinformaticists.

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Chicurel, Marina. "Bioinformatics: Bringing it all together technology feature." Nature 419, no. 6908 (2002): 752–55. http://dx.doi.org/10.1038/419751a.

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Fernandez, Dennis, and Mary Chow. "Feature — Intellectual Property Strategy in Bioinformatics and Biochips." Asia-Pacific Biotech News 07, no. 02 (2003): 66–70. http://dx.doi.org/10.1142/s0219030303000181.

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Intellectual property rights are essential in today's technology-driven age. A strong intellectual property protection strategy is crucial in the bioinformatics and biochips technology spaces as monetary and temporal resources are tremendous in finding a blockbuster drug or gene therapy, as well as in deploying advanced biosensor and other medical systems. Current problems and intellectual property practice in the genomic space are presented and analyzed. Various strategy and solutions are proposed to guide bioinformatic and biochip companies in forming an aggressive strategy to protect one's intellectual property and competitive positioning.

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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 (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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Stephens, Susie, David LaVigna, Mike DiLascio, and Joanne Luciano. "Aggregation of bioinformatics data using Semantic Web technology." Journal of Web Semantics 4, no. 3 (2006): 216–21. http://dx.doi.org/10.1016/j.websem.2006.05.004.

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Kuenne, Christian, Ivo Grosse, Inge Matthies, et al. "Using Data Warehouse Technology in Crop Plant Bioinformatics." Journal of Integrative Bioinformatics 4, no. 1 (2007): 145–59. http://dx.doi.org/10.1515/jib-2007-88.

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Summary Plant-specific data is managed in heterogeneous formats and is dispersed geographically. Based on this data, efficient analyses require a materialised integration, often realised with data warehouse technology today. We describe the requirements, problems and solution strategies for domain-crossing integration as the fundament for analysing plant biological data based on three current case studies. First, we introduce a system for retrieval of markers and mapping positions based on clustering of ESTs. The second case study illustrates the steps for diversity studies after genotyping a collection of about 3,000 ryegrass accessions (Lolium spp.), whereas in the third example data of approximately 250 barley cultivars (Hordeum vulgare) were used for associating haplotype- and SNP-patterns with malting parameters. For all case studies, we integrate data from different domains - sequence and marker data as well as IPK Genebank data including passport and phenotypic information. Specific problems associated with plant biological data and possible solution strategies are shown.

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Fajriyah, Rohmatul. "Paper review: An overview on microarray technologies." Bulletin of Applied Mathematics and Mathematics Education 1, no. 1 (2021): 21. http://dx.doi.org/10.12928/bamme.v1i1.3854.

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Bioinformatics is a branch in Statistics which is still unpopular among statistics students in Indonesia. Bioinformatics research used microarray technology, because data is available through to microarray experiment on tissue sample at hand. Microarray technology has been widely used to provide data for bioinformatics research, since it was first introduced in late 1990, particularly in life sciences and biotechnology research. The emergence and development of the Covid-19 disease further reinforces the need to understand bioinformatics and its technology. There are two of the most advance platforms in microarray technology, namely, are the Affymetrix GeneChip and Illumina BeadArray. This paper aims to give an overview about microarray technology on the two platforms and the advantage of using them on bioinformatics research.

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Misoo, Kiyotaka. "A Pioneer of Bioinformatics Services." Asia-Pacific Biotech News 11, no. 15 (2007): 1046–47. http://dx.doi.org/10.1142/s0219030307001115.

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Gendel, Steven M. "Bioinformatics and Food Allergens." Journal of AOAC INTERNATIONAL 87, no. 6 (2004): 1417–22. http://dx.doi.org/10.1093/jaoac/87.6.1417.

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Abstract Bioinformatics can play an important role in developing improved technology for the detection and characterization of food allergens. However, the full realization of this potential will depend on the development of allergen-specific databases as well as improved methods for data mining within these databases. Examples of existing allergen databases and analysis tools are described, as are the most important issues that need to be addressed in the next stage of database development.

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Pereira, Rute, Jorge Oliveira, and Mário Sousa. "Bioinformatics and Computational Tools for Next-Generation Sequencing Analysis in Clinical Genetics." Journal of Clinical Medicine 9, no. 1 (2020): 132. http://dx.doi.org/10.3390/jcm9010132.

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Clinical genetics has an important role in the healthcare system to provide a definitive diagnosis for many rare syndromes. It also can have an influence over genetics prevention, disease prognosis and assisting the selection of the best options of care/treatment for patients. Next-generation sequencing (NGS) has transformed clinical genetics making possible to analyze hundreds of genes at an unprecedented speed and at a lower price when comparing to conventional Sanger sequencing. Despite the growing literature concerning NGS in a clinical setting, this review aims to fill the gap that exists among (bio)informaticians, molecular geneticists and clinicians, by presenting a general overview of the NGS technology and workflow. First, we will review the current NGS platforms, focusing on the two main platforms Illumina and Ion Torrent, and discussing the major strong points and weaknesses intrinsic to each platform. Next, the NGS analytical bioinformatic pipelines are dissected, giving some emphasis to the algorithms commonly used to generate process data and to analyze sequence variants. Finally, the main challenges around NGS bioinformatics are placed in perspective for future developments. Even with the huge achievements made in NGS technology and bioinformatics, further improvements in bioinformatic algorithms are still required to deal with complex and genetically heterogeneous disorders.

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Augen, Jeffrey. "Bioinformatics and information technology: reshaping the drug discovery process." Drug Discovery Today 7, no. 11 (2002): S39—S40. http://dx.doi.org/10.1016/s1359-6446(02)02187-6.

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Tusch, Guenter, Paul Leidig, Greg Wolffe, David Elrod, and Carl Strebel. "Technology infrastructure supporting a medical & bioinformatics masters degree." ACM SIGCSE Bulletin 36, no. 3 (2004): 264. http://dx.doi.org/10.1145/1026487.1008097.

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Malinowski, Michael J. "Technology Transfer in BioBanking: Credits, Debits, and Population Health Futures." Journal of Law, Medicine & Ethics 33, no. 1 (2005): 54–69. http://dx.doi.org/10.1111/j.1748-720x.2005.tb00210.x.

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Bioinformatics, the integration of information technology and biotechnology, is the primary means to make medical sense out of the map of the human genome, and bioinformatics capabilities continue to expand exponentially. Consequently, the demand for access to human biological samples and medical information has never been greater. This demand is giving rise to ambitious biobanking initiatives - meaning the organized collection of samples and medical information from human population.

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19

Elands, J. "Integrated Cheminformatics and Bioinformatics." Journal of the Association for Laboratory Automation 6, no. 4 (2001): 42–44. http://dx.doi.org/10.1016/s1535-5535(04)00142-x.

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Elands, Jack. "Integrated Cheminformatics and Bioinformatics." JALA: Journal of the Association for Laboratory Automation 6, no. 4 (2001): 42–44. http://dx.doi.org/10.1016/s1535-5535-04-00142-x.

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21

Kovarik, Dina N., Davis G. Patterson, Carolyn Cohen, et al. "Bioinformatics Education in High School: Implications for Promoting Science, Technology, Engineering, and Mathematics Careers." CBE—Life Sciences Education 12, no. 3 (2013): 441–59. http://dx.doi.org/10.1187/cbe.12-11-0193.

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We investigated the effects of our Bio-ITEST teacher professional development model and bioinformatics curricula on cognitive traits (awareness, engagement, self-efficacy, and relevance) in high school teachers and students that are known to accompany a developing interest in science, technology, engineering, and mathematics (STEM) careers. The program included best practices in adult education and diverse resources to empower teachers to integrate STEM career information into their classrooms. The introductory unit, Using Bioinformatics: Genetic Testing, uses bioinformatics to teach basic concepts in genetics and molecular biology, and the advanced unit, Using Bioinformatics: Genetic Research, utilizes bioinformatics to study evolution and support student research with DNA barcoding. Pre–post surveys demonstrated significant growth (n = 24) among teachers in their preparation to teach the curricula and infuse career awareness into their classes, and these gains were sustained through the end of the academic year. Introductory unit students (n = 289) showed significant gains in awareness, relevance, and self-efficacy. While these students did not show significant gains in engagement, advanced unit students (n = 41) showed gains in all four cognitive areas. Lessons learned during Bio-ITEST are explored in the context of recommendations for other programs that wish to increase student interest in STEM careers.

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Xu, Guang Yu, Li Ping An, Xiao Han, Huan Qi Wang, and Pei Ge Du. "Necessity of Bioinformatics in the Setting of Pharmacy." Applied Mechanics and Materials 678 (October 2014): 107–11. http://dx.doi.org/10.4028/www.scientific.net/amm.678.107.

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In the wake ofthe gradual improvement of people's living standards and the growing demand for health, new drug developments are becoming more and more needed. On the basis of the coming of post-genomic era and the development of computer technology, bioinformatics comes into the world as an efficient technology. In recent years, bioinformatics has been wildly used in novel drug design. Besides, bioinformatics has been merged into medical application, especially, together with the rapid development of molecular biology technology and the coming of post-genomicera; the research of drug resource is also facing a challenge of massive data. The application of cloud computing into the drug resource study has been an inevitable tendency. In this article, we discussed the importance and necessity of bioinformatics in the setting of pharmacy

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De Moor, Bart, Kathleen Marchal, Janick Mathys, and Yves Moreau. "Bioinformatics: Organisms from Venus, Technology from Jupiter, Algorithms from Mars." European Journal of Control 9, no. 2-3 (2003): 237–78. http://dx.doi.org/10.3166/ejc.9.237-278.

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Chen, Zhengxin. "Re-examination of a Bioinformatics Database Course: Engaging Blockchain Technology." Procedia Computer Science 162 (2019): 368–74. http://dx.doi.org/10.1016/j.procs.2019.11.297.

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Belyanina, Lubov A. "Using Blockchain Technology, Artificial and Natural Neural Network in Bioinformatics." International Journal of Applied Research in Bioinformatics 9, no. 2 (2019): 42–49. http://dx.doi.org/10.4018/ijarb.2019070104.

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Blockchain technology has come up with solutions for the issues faced in the current healthcare system. Stakeholders and researchers can share electronic health records to find the solution to diseases. This not only improves the health sector, but it also provides information about other diseases. To maintain the security and privacy of the patient, a healthcare data gateway storage platform was created based on the blockchain technique. To overcome the problem, this article proposes a dengue diagnosis method based on nucleotides in the gene sequence, it needs only skin cells, hair, or a nail which can be collected easily from the patients. The proposed method not only diagnoses dengue, but also classifies serotypes using wavelet coefficients of EIIP indicator sequences.

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Augen, Jeffrey. "Feature — Bioinformatics and Information Technology – Reshaping the Drug Discovery Process." Asia-Pacific Biotech News 07, no. 11 (2003): 624–26. http://dx.doi.org/10.1142/s0219030303001228.

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Rasskazova, Natalia, and Liudmila Ratushnaia. "Blockchain Technology Changing Traditional Methods of Applied Research in Bioinformatics." International Journal of Applied Research in Bioinformatics 9, no. 1 (2019): 66–74. http://dx.doi.org/10.4018/ijarb.2019010105.

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It is evident that traditional research methods are going to change. The research should be based on the possibility of quick access to information, interactivity and interaction of participants in search for information, elimination of temporal and spatial obstacles. Everything mentioned above requires the use of new research technologies, including blockchain technology. This technology allows solving the problems of collection and systematization of scientific data, access to it within the framework of project operation and implementation at any organization, and joint efforts of various structures. Data saved in blocks and stored on different servers can be accessed by different users. It reduces the cost of coordinating the actions of different users who want to obtain certain information. This article explores how blockchain technology is changing traditional methods of applied research in bioinformatics.

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NADEAU, THOMAS P., KELLI A. SULLIVAN, TOBY J. TEOREY, and EVA L. FELDMAN. "APPLYING DATABASE TECHNOLOGY TO CLINICAL AND BASIC RESEARCH BIOINFORMATICS PROJECTS." Journal of Integrative Neuroscience 02, no. 02 (2003): 201–17. http://dx.doi.org/10.1142/s0219635203000305.

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Rossi, Ernest, Kathryn Rossi, Garret Yount, Mauro Cozzolino, and Salvador Iannotti. "The Bioinformatics of Integrative Medical Insights: Proposals for an International Psycho-Social and Cultural Bioinformatics Project." Integrative Medicine Insights 1 (January 2006): 117863370600100. http://dx.doi.org/10.1177/117863370600100002.

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We propose the formation of an International Psycho-Social and Cultural Bioinformatics Project (IPCBP) to explore the research foundations of Integrative Medical Insights (IMI) on all levels from the molecular-genomic to the psychological, cultural, social, and spiritual. Just as The Human Genome Project identified the molecular foundations of modern medicine with the new technology of sequencing DNA during the past decade, the IPCBP would extend and integrate this neuroscience knowledge base with the technology of gene expression via DNA/proteomic microarray research and brain imaging in development, stress, healing, rehabilitation, and the psychotherapeutic facilitation of existentional wellness. We anticipate that the IPCBP will require a unique international collaboration of, academic institutions, researchers, and clinical practioners for the creation of a new neuroscience of mind-body communication, brain plasticity, memory, learning, and creative processing during optimal experiential states of art, beauty, and truth. We illustrate this emerging integration of bioinformatics with medicine with a videotape of the classical 4-stage creative process in a neuroscience approach to psychotherapy.

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McCubbin, Caroline. "Legal issues in bioinformatics." Journal of Commercial Biotechnology 9, no. 3 (2003): 249–65. http://dx.doi.org/10.1057/palgrave.jcb.3040034.

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Cheng, Gong, Quan Lu, Ling Ma, Guocai Zhang, Liang Xu, and Zongshan Zhou. "BGDMdocker: a Docker workflow for data mining and visualization of bacterial pan-genomes and biosynthetic gene clusters." PeerJ 5 (November 30, 2017): e3948. http://dx.doi.org/10.7717/peerj.3948.

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Recently, Docker technology has received increasing attention throughout the bioinformatics community. However, its implementation has not yet been mastered by most biologists; accordingly, its application in biological research has been limited. In order to popularize this technology in the field of bioinformatics and to promote the use of publicly available bioinformatics tools, such as Dockerfiles and Images from communities, government sources, and private owners in the Docker Hub Registry and other Docker-based resources, we introduce here a complete and accurate bioinformatics workflow based on Docker. The present workflow enables analysis and visualization of pan-genomes and biosynthetic gene clusters of bacteria. This provides a new solution for bioinformatics mining of big data from various publicly available biological databases. The present step-by-step guide creates an integrative workflow through a Dockerfile to allow researchers to build their own Image and run Container easily.

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Bryce, C. F. A. "Chambers Science and Technology Dictionary." Bioinformatics 5, no. 2 (1989): 179. http://dx.doi.org/10.1093/bioinformatics/5.2.179-a.

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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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Mircean, C., I. Shmulevich, D. Cogdell, et al. "Robust estimation of protein expression ratios with lysate microarray technology." Bioinformatics 21, no. 9 (2005): 1935–42. http://dx.doi.org/10.1093/bioinformatics/bti258.

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Назипова, Н. Н., and N. N. Nazipova. "Big Data in Bioinformatics." Mathematical Biology and Bioinformatics 12, no. 1 (2017): 102–19. http://dx.doi.org/10.17537/2017.12.102.

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Sequencing of the human genome began in 1994. It took 10 years of collaborative work of many research groups from different countries in order to provide a draft of the human DNA. Modern technologies allow sequencing of a whole genome in a few days. We discuss here the advances in modern bioinformatics related to the emergence of high-performance sequencing platforms, which not only contributed to the expansion of capabilities of biology and related sciences, but also gave rise to the phenomenon of Big Data in biology. The necessity for development of new technologies and methods for organization of storage, management, analysis and visualization of big data is substantiated. Modern bioinformatics is facing not only the problem of processing enormous volumes of heterogeneous data, but also a variety of methods of interpretation and presentation of the results, the simultaneous existence of various software tools and data formats. The ways of solving the arising challenges are discussed, in particular by using experiences from other areas of modern life, such as web and business intelligence. The former is the area of scientific research and development that explores the roles and makes use of artificial intelligence and information technology (IT) for new products, services and frameworks that are empowered by the World Wide Web; the latter is the domain of IT, which addresses the issues of decision-making. New database management systems, other than relational ones, will help solve the problem of storing huge data and providing an acceptable timescale for performing search queries. New programming technologies, such as generic programming and visual programming, are designed to solve the problem of the diversity of genomic data formats and to provide the ability to quickly create one's own scripts for data processing.

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Otto, Thomas D., Mandy Sanders, Matthew Berriman, and Chris Newbold. "Iterative Correction of Reference Nucleotides (iCORN) using second generation sequencing technology." Bioinformatics 26, no. 14 (2010): 1704–7. http://dx.doi.org/10.1093/bioinformatics/btq269.

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Firtina, Can, Jeremie S. Kim, Mohammed Alser, et al. "Apollo: a sequencing-technology-independent, scalable and accurate assembly polishing algorithm." Bioinformatics 36, no. 12 (2020): 3669–79. http://dx.doi.org/10.1093/bioinformatics/btaa179.

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Abstract Motivation Third-generation sequencing technologies can sequence long reads that contain as many as 2 million base pairs. These long reads are used to construct an assembly (i.e. the subject’s genome), which is further used in downstream genome analysis. Unfortunately, third-generation sequencing technologies have high sequencing error rates and a large proportion of base pairs in these long reads is incorrectly identified. These errors propagate to the assembly and affect the accuracy of genome analysis. Assembly polishing algorithms minimize such error propagation by polishing or fixing errors in the assembly by using information from alignments between reads and the assembly (i.e. read-to-assembly alignment information). However, current assembly polishing algorithms can only polish an assembly using reads from either a certain sequencing technology or a small assembly. Such technology-dependency and assembly-size dependency require researchers to (i) run multiple polishing algorithms and (ii) use small chunks of a large genome to use all available readsets and polish large genomes, respectively. Results We introduce Apollo, a universal assembly polishing algorithm that scales well to polish an assembly of any size (i.e. both large and small genomes) using reads from all sequencing technologies (i.e. second- and third-generation). Our goal is to provide a single algorithm that uses read sets from all available sequencing technologies to improve the accuracy of assembly polishing and that can polish large genomes. Apollo (i) models an assembly as a profile hidden Markov model (pHMM), (ii) uses read-to-assembly alignment to train the pHMM with the Forward–Backward algorithm and (iii) decodes the trained model with the Viterbi algorithm to produce a polished assembly. Our experiments with real readsets demonstrate that Apollo is the only algorithm that (i) uses reads from any sequencing technology within a single run and (ii) scales well to polish large assemblies without splitting the assembly into multiple parts. Availability and implementation Source code is available at https://github.com/CMU-SAFARI/Apollo. Supplementary information Supplementary data are available at Bioinformatics online.

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Mitra, Prabir Kumar. "Bioinformatics and Computer Technology-Trends in R&D and Industry." Review of Professional Management- A Journal of New Delhi Institute of Management 8, no. 2 (2010): 113. http://dx.doi.org/10.20968/rpm/2010/v8/i2/92826.

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Tusch, Guenter, Paul Leidig, Gregory Wolffe, David Elrod, and Carl Strebel. "Technology infrastructure in support of a medical & bioinformatics masters degree." ACM SIGCSE Bulletin 41, no. 3 (2009): 388. http://dx.doi.org/10.1145/1595496.1563030.

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Banerjee, Preeta M. "From information technology to bioinformatics: Evolution of technological capabilities in India." Technological Forecasting and Social Change 79, no. 4 (2012): 665–75. http://dx.doi.org/10.1016/j.techfore.2011.08.002.

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Valencia, Alfonso. "Bioinformatics and Computational Biology at the crossroads of post-genomic technology." Phytochemistry Reviews 1, no. 2 (2002): 209–14. http://dx.doi.org/10.1023/a:1022563518121.

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Wefer, Stephen H., and Keith Sheppard. "Bioinformatics in High School Biology Curricula: A Study of State Science Standards." CBE—Life Sciences Education 7, no. 1 (2008): 155–62. http://dx.doi.org/10.1187/cbe.07-05-0026.

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The proliferation of bioinformatics in modern biology marks a modern revolution in science that promises to influence science education at all levels. This study analyzed secondary school science standards of 49 U.S. states (Iowa has no science framework) and the District of Columbia for content related to bioinformatics. The bioinformatics content of each state's biology standards was analyzed and categorized into nine areas: Human Genome Project/genomics, forensics, evolution, classification, nucleotide variations, medicine, computer use, agriculture/food technology, and science technology and society/socioscientific issues. Findings indicated a generally low representation of bioinformatics-related content, which varied substantially across the different areas, with Human Genome Project/genomics and computer use being the lowest (8%), and evolution being the highest (64%) among states' science frameworks. This essay concludes with recommendations for reworking/rewording existing standards to facilitate the goal of promoting science literacy among secondary school students.

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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 (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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Nagata, H., H. Mizushima, and H. Tanaka. "Concept and prototype of protein-ligand docking simulator with force feedback technology." Bioinformatics 18, no. 1 (2002): 140–46. http://dx.doi.org/10.1093/bioinformatics/18.1.140.

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Magana, Alejandra J., Manaz Taleyarkhan, Daniela Rivera Alvarado, Michael Kane, John Springer, and Kari Clase. "A Survey of Scholarly Literature Describing the Field of Bioinformatics Education and Bioinformatics Educational Research." CBE—Life Sciences Education 13, no. 4 (2014): 607–23. http://dx.doi.org/10.1187/cbe.13-10-0193.

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Bioinformatics education can be broadly defined as the teaching and learning of the use of computer and information technology, along with mathematical and statistical analysis for gathering, storing, analyzing, interpreting, and integrating data to solve biological problems. The recent surge of genomics, proteomics, and structural biology in the potential advancement of research and development in complex biomedical systems has created a need for an educated workforce in bioinformatics. However, effectively integrating bioinformatics education through formal and informal educational settings has been a challenge due in part to its cross-disciplinary nature. In this article, we seek to provide an overview of the state of bioinformatics education. This article identifies: 1) current approaches of bioinformatics education at the undergraduate and graduate levels; 2) the most common concepts and skills being taught in bioinformatics education; 3) pedagogical approaches and methods of delivery for conveying bioinformatics concepts and skills; and 4) assessment results on the impact of these programs, approaches, and methods in students’ attitudes or learning. Based on these findings, it is our goal to describe the landscape of scholarly work in this area and, as a result, identify opportunities and challenges in bioinformatics education.

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Gauthier, Jeff, Antony T. Vincent, Steve J. Charette, and Nicolas Derome. "A brief history of bioinformatics." Briefings in Bioinformatics 20, no. 6 (2018): 1981–96. http://dx.doi.org/10.1093/bib/bby063.

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AbstractIt is easy for today’s students and researchers to believe that modern bioinformatics emerged recently to assist next-generation sequencing data analysis. However, the very beginnings of bioinformatics occurred more than 50 years ago, when desktop computers were still a hypothesis and DNA could not yet be sequenced. The foundations of bioinformatics were laid in the early 1960s with the application of computational methods to protein sequence analysis (notably, de novo sequence assembly, biological sequence databases and substitution models). Later on, DNA analysis also emerged due to parallel advances in (i) molecular biology methods, which allowed easier manipulation of DNA, as well as its sequencing, and (ii) computer science, which saw the rise of increasingly miniaturized and more powerful computers, as well as novel software better suited to handle bioinformatics tasks. In the 1990s through the 2000s, major improvements in sequencing technology, along with reduced costs, gave rise to an exponential increase of data. The arrival of ‘Big Data’ has laid out new challenges in terms of data mining and management, calling for more expertise from computer science into the field. Coupled with an ever-increasing amount of bioinformatics tools, biological Big Data had (and continues to have) profound implications on the predictive power and reproducibility of bioinformatics results. To overcome this issue, universities are now fully integrating this discipline into the curriculum of biology students. Recent subdisciplines such as synthetic biology, systems biology and whole-cell modeling have emerged from the ever-increasing complementarity between computer science and biology.

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Sumantri, Nur Imaniati, Nagisa Eremia Anju, and Siti Fauziyah Rahman. "Wiping out the covid-19 pandemic through bioinformatics: a review on database and web tools applications." Berkala Penelitian Hayati 26, no. 2 (2021): 60–65. http://dx.doi.org/10.23869/bphjbr.26.2.20212.

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The COVID-19 is an illness caused by SARS-CoV-2 that has ended up a widespread since March 2020 as declared by WHO. This condition gives a huge impact on miscellaneous sectors enforcing advance use of technology and information system, especially in scientific and medical community. Bioinformatics as a multidisciplinary method plays important role to overcome the COVID-19 outbreak in the early-stage through data exchange in virtual databases. There are a number of free access databases containing basic to complex information of SARS-CoV-2, such as genetic data sequence, epidemiology, evolutionary analysis, pharmacology, and so on. Bioinformatics allows us to analyze the data further to reveal new information applied in biomedical technology activities. Thus, bioinformatics helps the scientists, clinicians, and government learn the genomic characteristics of SARS-CoV-2, to trace the SARS-CoV-2 spread, and select and develop biomarker for reliable diagnostic tools, and design the drug and vaccine for SARS-CoV-2. This review aims to view insights on uses of bioinformatics methods and the databases related to SARS-CoV-2

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Shapovalova, V. V., S. P. Radko, K. G. Ptitsyn, et al. "Processing Oxford Nanopore Long Reads Using Amazon Web Services." Biomedical Chemistry: Research and Methods 3, no. 4 (2020): e00131. http://dx.doi.org/10.18097/bmcrm00131.

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Studies of genomes and transcriptomes are performed using sequencers that read the sequence of nucleotide residues of genomic DNA, RNA, or complementary DNA (cDNA). The analysis consists of an experimental part (obtaining primary data) and bioinformatic processing of primary data. The bioinformatics part is performed with different sets of input parameters. The selection of the optimal values of the parameters, as a rule, requires significant computing power. The article describes a protocol for processing transcriptome data by virtual computers provided by the cloud platform Amazon Web Services (AWS) using the example of the recently emerging technology of long DNA and RNA sequences (Oxford Nanopore Technology). As a result, a virtual machine and instructions for its use have been developed, thus allowing a wide range of molecular biologists to independently process the results obtained using the "Oxford nanopore".

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Fejes, A. P., G. Robertson, M. Bilenky, R. Varhol, M. Bainbridge, and S. J. M. Jones. "FindPeaks 3.1: a tool for identifying areas of enrichment from massively parallel short-read sequencing technology." Bioinformatics 24, no. 15 (2008): 1729–30. http://dx.doi.org/10.1093/bioinformatics/btn305.

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Yang, Lun, Langlai Xu, and Lin He. "A CitationRank algorithm inheriting Google technology designed to highlight genes responsible for serious adverse drug reaction." Bioinformatics 25, no. 17 (2009): 2244–50. http://dx.doi.org/10.1093/bioinformatics/btp369.

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Journal articles on the topic 'Bioinformatics (technology)'

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