Relevant bibliographies by topics / Bioinformatics (technology)

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Bioinformatics (technology)'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "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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Dissertations / Theses on the topic "Bioinformatics (technology)"

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Benaim, Jalfon Carlos 1966. "Analysis of the bioinformatics industry." Thesis, Massachusetts Institute of Technology, 2001. http://hdl.handle.net/1721.1/8882.

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Thesis (S.M.M.O.T.)--Massachusetts Institute of Technology, Sloan School of Management, Management of Technology Program, 2001.<br>Includes bibliographical references (leaf 76).<br>The rise of the commercial genomic industry and the broadening application of genomic techniques in biology and medicine together with the growing availability of DNA sequence information have created a new industry: The Bioinformatics Industry. This thesis analyzes technologies, applications market and competitors in this industry and explores potential changes to the business models that are being used today. The technology and market information indicates that this is an industry in a very early stage. On the other hand, the business models being used are very similar to the ones used traditionally in the hardware and software industry: licensing, ASP (Application Service Provider), joint developments and hardware/software solutions. The actual market size is relatively small, estimated in no more than $300M. Only by implementing strategies of horizontal or vertical integration, a company in this industry might be able to boost revenues in the long term.<br>by Carlos Benaim Jalfon.<br>S.M.M.O.T.
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Fakhry, Charbel Bader. "Exploring Strategies to Integrate Disparate Bioinformatics Datasets." ScholarWorks, 2019. https://scholarworks.waldenu.edu/dissertations/7472.

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Distinct bioinformatics datasets make it challenging for bioinformatics specialists to locate the required datasets and unify their format for result extraction. The purpose of this single case study was to explore strategies to integrate distinct bioinformatics datasets. The technology acceptance model was used as the conceptual framework to understand the perceived usefulness and ease of use of integrating bioinformatics datasets. The population of this study included bioinformatics specialists of a research institution in Lebanon that has strategies to integrate distinct bioinformatics datasets. The data collection process included interviews with 6 bioinformatics specialists and reviewing 27 organizational documents relating to integrating bioinformatics datasets. Thematic analysis was used to identify codes and themes related to integrating distinct bioinformatics datasets. Key themes resulting from data analysis included a focus on integrating bioinformatics datasets, adding metadata with the submitted bioinformatics datasets, centralized bioinformatics database, resources, and bioinformatics tools. I showed throughout analyzing the findings of this study that specialists who promote standardizing techniques, adding metadata, and centralization may increase efficiency in integrating distinct bioinformatics datasets. Bioinformaticians, bioinformatics providers, the health care field, and society might benefit from this research. Improvement in bioinformatics affects poistevely the health-care field which has a positive social change. The results of this study might also lead to positive social change in research institutions, such as reduced workload, less frustration, reduction in costs, and increased efficiency while integrating distinct bioinformatics datasets.
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Cabral, Braulio J. "Exploring Factors Influencing Information Technology Portfolio Selection Process in Government-Funded Bioinformatics Projects." ScholarWorks, 2016. https://scholarworks.waldenu.edu/dissertations/2957.

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In 2012, the National Cancer Institute's (NCI) Board of Scientific Advisors (BSA) conducted a review of the Center for Biomedical Informatics and Information Technology's (CBIIT) bioinformatics program. The BSA suggested that the lack of a formal project selection process made it difficult to determine the alignment of projects with the mission of the organization. The problem addressed by this study was that CBIIT did not have an in-depth understanding of the project selection process and the factors influencing the process. The purpose of this study was to understand the project selection process at CBIIT. The research methodology was an exploratory case study. The data collection process included a phenomenological interview of 25 managers from program management, engineering, scientific computing, informatics program, and health sciences. The data analysis consisted of coding for themes, sensitizing, and heuristic coding, supported by a theoretical framework that included the technology acceptance model, the program evaluation theory, and decision theory. The analysis revealed the need for formal project portfolio governance, the lack of a predefined project selection process, and that the decision-making process was circumstantial. The study also revealed six major themes that affected the decision-making process: the CBIIT mission, the organizational culture, leadership, governance, funding, and organizational change. Finally, the study fills the gap in the literature regarding the project selection process for government-funded initiatives in information technologies. This study may contribute to positive social change by improving the project selection process at CBIIT, allowing for the effective use of public funds for cancer informatics researchers.
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Mooney, Marie R. "Automated Technology for Elucidating Meal Microstructures of Rat Feeding Behavior." University of Cincinnati / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1282579902.

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Mohan, Amrita. "A study of intrinsic disorder and its role in functional proteomics." [Bloomington, Ind.] : Indiana University, 2009. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3386707.

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Thesis (Ph.D.)--Indiana University, Dept. of Informatics, 2009.<br>Title from PDF t.p. (viewed on Jul 22, 2010). Source: Dissertation Abstracts International, Volume: 70-12, Section: B, page: 7298. Adviser: Predrag Radivojac.
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Sullivan, Matthew John. "Applying knowledge-based techniques and artificial intelligence to automated problem solving in science, technology and bioinformatics." Thesis, Queen's University Belfast, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.287431.

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Roach, Kenneth L. (Kenneth Lee) 1979. "A microwell array cytometry system for high throughput single cell biology and bioinformatics." Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/47850.

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Thesis (Ph. D.)--Harvard-MIT Division of Health Sciences and Technology, 2009.<br>Includes bibliographical references (p. 91-101).<br>Recent advances in systems biology and bioinformatics have highlighted that no cell population is truly uniform and that stochastic behavior is an inherent property of many biological systems. As a result, bulk measurements can be misleading even when particular care has been taken to isolate a single cell type, and measurements averaged over multiple cell populations in a tissue can be as misleading as the average height at an elementary school. Unfortunately, there are relatively few experimental systems available at present that can provide a combination of single cell resolution, large cell populations, and the ability to track individual cells over multiple time points. Those systems that do exist are often difficult to automate and require extensive user intervention simply to generate the raw data sets for later analysis. The goal of this thesis project was to develop a powerful, inexpensive, and easy-to-use system that meets the above requirements and can serve as a platform for single cell bioinformatics. Our current system design is composed of two basic parts: 1) a customizable PDMS device consisting of one or more microwell arrays, each with associated alignment and identification features, and 2) a suite of custom software tools for automated image processing and data analysis. The system has a number of significant advantages over competing technologies such as flow cytometry and standard image cytometry. Unlike flow cytometry, the cells are not in suspension, and individual cells can be tracked across multiple time points or examined before and after a treatment.<br>(cont.) Unlike most image cytometry approaches, the cells are arranged in a spatially defined pattern and physically separated from one another, greatly simplifying the required image analysis. The automated analysis tools require only a minimal amount of user intervention and can easily generate multi-channel fluorescence time courses for tens of thousands of individual cells in a single experiment. For visualization purposes, tools are provided to annotate the original fluorescence images or movies with the results of later analysis, and several quality control routines are available to identify improperly seeded wells or debris. The microwell array cytometry platform has allowed us to investigate a number of biological problems that would be difficult or impossible to tackle with standard techniques. Our earliest work focused on correlating pre-stress cell states with post-stress outcomes, with a major focus on the cryopreservation of primary hepatocytes. In particular, we wanted to know whether cell survival was dominated by extrinsic factors such as ice crystal nucleation, or intrinsic factors such as the energetic state of the cell. In one set of studies, we found that cells with a high initial mitochondrial content or mitochondrial membrane potential, as measured by Rh123 or JC-1 staining, were significantly less likely to survive the freezing process. This demonstrated that intrinsic cell factors do play a major role in cryopreservation survival, but perhaps more importantly demonstrated the power and versatility of the microwell system by tracking individual cells across a treatment as extreme as freezing the entire device. In another set of cryopreservation experiments, cells were transiently transfected with a GFP-tagged protective protein and the resulting cell population, with its range of expression levels, was used to generate dose response curves with single cell resolution for the protein's protective effect.<br>(cont.) More recently, our efforts have focused on generating single cell fluorescence time courses and using bioinformatics techniques such as hierarchical and k-means clustering to visualize the data and extract interesting features. More specifically, the behavior of primary hepatocytes under oxidative stress and protective metabolic manipulation was examined using a combination of mitochondrial and free radical sensitive dyes. The resulting time courses could not only be compared between the treatment groups, but a number of distinct response patterns could be identified within each treatment group. This variation in response patterns represent potentially important information that would be missed using bulk techniques or flow cytometry. In addition, membership in each response cluster was correlated between multiple dyes and with the initial state of each cell. Using a live / dead methodology, dose response curves, survival curves, and survival time distributions were also generated for each treatment condition and further subdivided based on the initial cell state and cluster assignments. We believe that our microwell array cytometry platform will have general utility for a wide range of questions related to cell population heterogeneity, biological stochasticity, and cell behavior under stress conditions. We have really just begun exploring rich data sets of this type, and with additional work there is a great potential for groundbreaking results in many areas of biology and bioinformatics. Though we have applied techniques from gene expression analysis, there are a number of significant differences between the type of data generated by gene chips and that generated in high-throughput single cell experiments. These differences also make single cell biology a fruitful area for the development of novel bioinformatics techniques and theories.<br>by Kenneth L. Roach.<br>Ph.D.
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Jungjit, Suwimol. "New multi-label correlation-based feature selection methods for multi-label classification and application in bioinformatics." Thesis, University of Kent, 2016. https://kar.kent.ac.uk/58873/.

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The very large dimensionality of real world datasets is a challenging problem for classification algorithms, since often many features are redundant or irrelevant for classification. In addition, a very large number of features leads to a high computational time for classification algorithms. Feature selection methods are used to deal with the large dimensionality of data by selecting a relevant feature subset according to an evaluation criterion. The vast majority of research on feature selection involves conventional single-label classification problems, where each instance is assigned a single class label; but there has been growing research on more complex multi-label classification problems, where each instance can be assigned multiple class labels. This thesis proposes three types of new Multi-Label Correlation-based Feature Selection (ML-CFS) methods, namely: (a) methods based on hill-climbing search, (b) methods that exploit biological knowledge (still using hill-climbing search), and (c) methods based on genetic algorithms as the search method. Firstly, we proposed three versions of ML-CFS methods based on hill climbing search. In essence, these ML-CFS versions extend the original CFS method by extending the merit function (which evaluates candidate feature subsets) to the multi-label classification scenario, as well as modifying the merit function in other ways. A conventional search strategy, hill-climbing, was used to explore the space of candidate solutions (candidate feature subsets) for those three versions of ML-CFS. These ML-CFS versions are described in detail in Chapter 4. Secondly, in order to try to improve the performance of ML-CFS in cancer-related microarray gene expression datasets, we proposed three versions of the ML-CFS method that exploit biological knowledge. These ML-CFS versions are also based on hill-climbing search, but the merit function was modified in a way that favours the selection of genes (features) involved in pre-defined cancer-related pathways, as discussed in detail in Chapter 5. Lastly, we proposed two more sophisticated versions of ML-CFS based on Genetic Algorithms (rather than hill-climbing) as the search method. The first version of GA-based ML-CFS is based on a conventional single-objective GA, where there is only one objective to be optimized; while the second version of GA-based ML-CFS performs lexicographic multi-objective optimization, where there are two objectives to be optimized, as discussed in detail in Chapter 6. In this thesis, all proposed ML-CFS methods for multi-label classification problems were evaluated by measuring the predictive accuracies obtained by two well-known multi-label classification algorithms when using the selected featuresม namely: the Multi-Label K-Nearest neighbours (ML-kNN) algorithm and the Multi-Label Back Propagation Multi-Label Learning Neural Network (BPMLL) algorithm. In general, the results obtained by the best version of the proposed ML-CFS methods, namely a GA-based ML-CFS method, were competitive with the results of other multi-label feature selection methods and baseline approaches. More precisely, one of our GA-based methods achieved the second best predictive accuracy out of all methods being compared (both with ML-kNN and BPMLL used as classifiers), but there was no statistically significant difference between that GA-based ML-CFS and the best method in terms of predictive accuracy. In addition, in the experiment with ML-kNN (the most accurate) method selects about twice as many features as our GA-based ML-CFS; whilst in the experiments with BPMLL the most accurate method was a baseline method that does not perform any feature selection, and runs the classifier once (with all original features) for each of the many class labels, which is a very computationally expensive baseline approach. In summary, one of the proposed GA-based ML-CFS methods managed to achieve substantial data reduction, (selecting a smaller subset of relevant features) without a significant decrease in predictive accuracy with respect to the most accurate method.
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Coutu-Nadeau, Charles. "Evaluating the usability of diabetes management iPad applications." Thesis, Weill Medical College of Cornell University, 2014. http://pqdtopen.proquest.com/#viewpdf?dispub=1526000.

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<p> <b>Background</b> Diabetes is a major cause of morbidity and mortality in the United States. In 2012, 29.1 million people were estimated to have the condition, with type 2 diabetes accounting for 95% of all cases [1]. It is currently one of the most costly conditions in the country [2] and forecasts as a heavier burden for the U.S. with the prevalence expected to significantly increase [3]. For those who live with the disease, it is possible to manage diabetes in order to prevent or delay the onset of complications [4]. However the self-management regimen is complex and impacts nearly every important aspect of one's life [5].</p><p> The ubiquitous nature of mobile technologies and powerful capabilities of smartphones and tablets has led to a significant increased interest in the development and use of mobile health. Diabetes management is an application area where mobile devices could enhance the quality of life for people living with chronic illnesses [6]&ndash;[8], and usability is key to the adoption of such technologies [9], [10]. Past work has evaluated the usability of diabetes management apps for Android, iOS and Blackberry smartphones [11]-[14] despite the fact that no established method to evaluate the usability of mobile apps has emerged [15]. To our knowledge, this study is the first to evaluate the usability of diabetes management apps on iPad.</p><p> <b>Methods</b> This study introduces a novel usability survey that is designed for mHealth and specific to the iOS operating system. The survey is built on previous usability findings [11]&ndash;[14], Nielsen heuristics [16] and the Apple iOS Human Interface Guidelines [17]. The new instrument was evaluated with three evaluators assessing ten iPad apps, selected because they were the most popular diabetes management apps on the Apple AppStore. A focus group was subsequently held to gather more insight on the usability of the apps and the survey itself. Statistical analysis using R and grounded theory were used to analyze the quantitative and qualitative results, respectively. </p><p> <b>Results</b> The survey identified OneTouch Reveal by LifeScan Inc. and TactioHealth by Tactic, Health Group as the most usable apps. GlucoMo by Artificial Life, Inc. and Diabetes in Check by Everyday Health, Inc. rated as the least usable apps. Setting up medication and editing blood glucose were the most problematic tasks. Some apps did not support all functions that were under review. Six main themes emerged from the focus group: the presentation of health information, aesthetic and minimalist design, flexibility and efficiency of data input, task feedback, intuitive design and app stability. These themes suggest important constructs of usability for mHealth apps.</p><p> <b>Discussion and Conclusion</b> Mobile health developers and researchers should focus on the tasks, heuristics and underlying issues that were identified as most problematic throughout the study. Additionally, research should further inquire on the potentially critical relation between the information available on app markets and the usability of apps. Several signs point to the potential of the usability survey that was developed but further adjustments and additional test iterations are warranted to validate its use as a reliable usability evaluation method.</p>
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Zhu, Rui. "ProteinChip SELDI-TOF MS technology to identify serum biomarkers for neuroblastoma and hepatitis B virus-induced hepatocellular carcinoma." Click to view the E-thesis via HKUTO, 2006. http://sunzi.lib.hku.hk/hkuto/record/B36392431.

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Books on the topic "Bioinformatics (technology)"

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Sharma, Kal Renganathan. Bioinformatics. McGraw-Hill, 2008.

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Claverie, Jean-Michel. Bioinformatics for dummies. 2nd ed. Wiley Pub., 2007.

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Cedric, Notredame, ed. Bioinformatics for dummies. 2nd ed. Wiley Pub., 2007.

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Cedric, Notredame, ed. Bioinformatics for dummies. Wiley Pub., 2003.

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Claverie, Jean-Michel. Bioinformatics for dummies. 2nd ed. Wiley Pub., 2007.

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Claverie, Jean-Michel. Bioinformatics For Dummies. John Wiley & Sons, Ltd., 2007.

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Tinku, Acharya, ed. Data mining: Multimedia, soft computing, and bioinformatics. John Wiley, 2003.

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BioADIT 2004 (1st 2004 Lausanne, Switzerland). Biologically inspired approaches to advanced information technology: First International Workshop, BioADIT 2004, Lausanne, Switzerland, January 29-30, 2004 : revised selected papers. Springer, 2004.

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Mitra, Sushmita. Data mining: Concepts and algorithms from multimedia to bioinformatics. Wiley-Interscience, 2003.

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Zomaya, Albert Y. Parallel Computing for Bioinformatics and Computational Biology. John Wiley & Sons, Ltd., 2006.

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Book chapters on the topic "Bioinformatics (technology)"

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Zanella, Paolo. "Technology Fallout in Bioinformatics." In From Physics to Daily Life. Wiley-VCH Verlag GmbH & Co. KGaA, 2014. http://dx.doi.org/10.1002/9783527687077.ch07.

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Liu, Qiong, and Liang Jiang. "Bioinformatics of Selenoproteins." In Advanced Topics in Science and Technology in China. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22236-8_9.

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Gonzalez-Garay, Manuel L. "Introduction to Isoform Sequencing Using Pacific Biosciences Technology (Iso-Seq)." In Translational Bioinformatics. Springer Netherlands, 2015. http://dx.doi.org/10.1007/978-94-017-7450-5_6.

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Paldus, Barbara A., and Mark D. Selker. "Bioinformatics and Single Use." In Single-Use Technology in Biopharmaceutical Manufacture. John Wiley & Sons, Inc., 2011. http://dx.doi.org/10.1002/9780470909997.ch7.

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Myllykangas, Samuel, Jason Buenrostro, and Hanlee P. Ji. "Overview of Sequencing Technology Platforms." In Bioinformatics for High Throughput Sequencing. Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-0782-9_2.

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Bansal, Gourja, Kiran Narta, and Manoj Ramesh Teltumbade. "Next-Generation Sequencing: Technology, Advancements, and Applications." In Bioinformatics: Sequences, Structures, Phylogeny. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1562-6_2.

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Ulag, Songul, Elif Ilhan, Burak Aksu, et al. "Patch-Based Technology for Corneal Microbial Keratitis." In Bioinformatics and Biomedical Engineering. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45385-5_18.

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Gajdoš, Ondřej, Ivana Juřičková, and Radka Otawova. "Health Technology Assessment Models Utilized in the Chronic Care Management." In Bioinformatics and Biomedical Engineering. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16483-0_6.

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Ahmed, Khaled Sayed, and Shereen M. El-Metwally. "Portable Low-Cost Heart Attack Detection System Using ZigBee Wireless Technology." In Bioinformatics and Biomedical Engineering. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16483-0_15.

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Ahumada-García, Roberto, Jorge González-Puelma, Diego Álvarez-Saravia, et al. "Identification of Immunoglobulin Gene Usage in Immune Repertoires Sequenced by Nanopore Technology." In Bioinformatics and Biomedical Engineering. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-17938-0_27.

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Conference papers on the topic "Bioinformatics (technology)"

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Olken, F. "Bioinformatics databases 1 [Advanced Technology Seminar 4]." In Proceedings 18th International Conference on Data Engineering. IEEE, 2002. http://dx.doi.org/10.1109/icde.2002.994782.

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Zhaoli. "Machine learning in bioinformatics." In 2011 International Conference on Computer Science and Network Technology (ICCSNT). IEEE, 2011. http://dx.doi.org/10.1109/iccsnt.2011.6182026.

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Raut, Shital A., S. R. Sathe, and Adarsh Raut. "Bioinformatics: Trends in gene expression analysis." In 2010 International Conference on Bioinformatics and Biomedical Technology. IEEE, 2010. http://dx.doi.org/10.1109/icbbt.2010.5479003.

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Zhang, Jinhe, Zhengyu Liu, Minxin Cai, et al. "Bioinformatics Analysis of Integrin aVß3." In 2015 7th International Conference on Information Technology in Medicine and Education (ITME). IEEE, 2015. http://dx.doi.org/10.1109/itme.2015.105.

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Tusch, Guenter, Paul Leidig, Greg Wolffe, David Elrod, and Carl Strebel. "Technology infrastructure supporting a medical & bioinformatics masters degree." In the 9th annual SIGCSE conference. ACM Press, 2004. http://dx.doi.org/10.1145/1007996.1008097.

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Gough, E. S., and M. D. Kane. "Evaluating Parallel Computing Systems in Bioinformatics." In Third International Conference on Information Technology: New Generations (ITNG'06). IEEE, 2006. http://dx.doi.org/10.1109/itng.2006.62.

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Polyakov, M. V., A. V. Khoperskov, V. V. Novochadov, A. A. Terpilovskiy, and K. P. Tiras. "Modeling of biological tissues based on 3D reconstruction technology." In Mathematical Biology and Bioinformatics. IMPB RAS - Branch of KIAM RAS, 2018. http://dx.doi.org/10.17537/icmbb18.79.

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Xu, Qing-Wei, and Yu Huang. "BiosFlow - A bioinformatics workflow platform based on semantic web technology." In 2009 International Conference on Future BioMedical Information Engineering (FBIE). IEEE, 2009. http://dx.doi.org/10.1109/fbie.2009.5405845.

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Wang, Xinyi, Zhen Huang, Cangshuai Wu, Feng Liu, and Congrui Wang. "Review of Bioinformatics Application Using Intel MIC." In 2019 IEEE 19th International Conference on Communication Technology (ICCT). IEEE, 2019. http://dx.doi.org/10.1109/icct46805.2019.8947018.

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Ayadi, N. Y., Raja Ben Messaoued, and H. Achour. "Towards semantic annotation of bioinformatics Web services." In 2013 International Conference on Computer Applications Technology (ICCAT 2013). IEEE, 2013. http://dx.doi.org/10.1109/iccat.2013.6522057.

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