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Journal articles on the topic 'Bioinformatics, Medicine'

Author: Grafiati
Published: 4 June 2021
Last updated: 5 February 2022

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1

Borodin, E. A. "PERSONIFIED MEDICINE AND BIOINFORMATICS." Amur Medical Journal, no. 4 (2018): 77–80. http://dx.doi.org/10.22448/amj.2018.4.77-80.

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2

Kim, Ju Han. "Bioinformatics and genomic medicine." Genetics in Medicine 4 (December 2002): 62S—65S. http://dx.doi.org/10.1097/00125817-200211001-00013.

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3

Anand, Sneh, and Jayashree Santhosh. "Bioinformatics in Clinical Medicine." IETE Technical Review 18, no. 4 (2001): 253–61. http://dx.doi.org/10.1080/02564602.2001.11416971.

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4

Fernald, Guy Haskin, Emidio Capriotti, Roxana Daneshjou, Konrad J. Karczewski, and Russ B. Altman. "Bioinformatics challenges for personalized medicine." Bioinformatics 27, no. 13 (2011): 1741–48. http://dx.doi.org/10.1093/bioinformatics/btr295.

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5

Fernald, G. H., E. Capriotti, R. Daneshjou, K. J. Karczewski, and R. B. Altman. "Bioinformatics challenges for personalized medicine." Bioinformatics 27, no. 16 (2011): 2323. http://dx.doi.org/10.1093/bioinformatics/btr408.

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6

Chang, I.-Shou, Ping-Chiang Lyu, Jenn-Kang Hwang, H. Sunny Sun, and Chao A. Hsiung. "Taiwan Bioinformatics Institute and the GMBD Bioinformatics Core." Asia-Pacific Biotech News 10, no. 24 (2006): 1432–34. http://dx.doi.org/10.1142/s0219030306002175.

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7

Bayat, A. "Science, medicine, and the future: Bioinformatics." BMJ 324, no. 7344 (2002): 1018–22. http://dx.doi.org/10.1136/bmj.324.7344.1018.

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8

Torres, Angela, and Juan J. Nieto. "Fuzzy Logic in Medicine and Bioinformatics." Journal of Biomedicine and Biotechnology 2006 (2006): 1–7. http://dx.doi.org/10.1155/jbb/2006/91908.

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The purpose of this paper is to present a general view of the current applications of fuzzy logic in medicine and bioinformatics. We particularly review the medical literature using fuzzy logic. We then recall the geometrical interpretation of fuzzy sets as points in a fuzzy hypercube and present two concrete illustrations in medicine (drug addictions) and in bioinformatics (comparison of genomes).
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9

Gu, P., and H. Chen. "Modern bioinformatics meets traditional Chinese medicine." Briefings in Bioinformatics 15, no. 6 (2013): 984–1003. http://dx.doi.org/10.1093/bib/bbt063.

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10

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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Shen, Bairong, Hong-Bin Shen, Tianhai Tian, Qiang Lü, and Guang Hu. "Translational Bioinformatics and Computational Systems Medicine." Computational and Mathematical Methods in Medicine 2013 (2013): 1–2. http://dx.doi.org/10.1155/2013/375641.

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12

Quan, Yuan, Zhong-Yi Wang, Min Xiong, Zheng-Tao Xiao, and Hong-Yu Zhang. "Dissecting Traditional Chinese Medicines by Omics and Bioinformatics." Natural Product Communications 9, no. 9 (2014): 1934578X1400900. http://dx.doi.org/10.1177/1934578x1400900942.

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Traditional Chinese medicines (TCM) are a rich source of potential leads for drug development. However, there are fundamental differences between traditional Chinese medical concepts and modern pharmacology, which greatly hinder the modern development of TCM. To address this challenge, new techniques associated with genomics, transcriptomics, proteomics, metabolomics and bioinformatics have been used to dissect the pharmacological mechanisms of TCM. This review article provides an overview of the current research in this area, and illustrates the potential of omic and bioinformatic methods in TCM-based drug discovery.
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13

Liang, Zhaohui, Xiangji Huang, Byeongsang Oh, and Josiah Poon. "Bioinformatics/Medical Informatics in Traditional Medicine and Integrative Medicine." Scientific World Journal 2015 (2015): 1–2. http://dx.doi.org/10.1155/2015/460490.

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14

Gorbachenko, Vladimir. "Digital Model for Diagnosis of Postoperative Complications in Medicine Using Bioinformatics." International Journal of Applied Research in Bioinformatics 9, no. 2 (2019): 1–23. http://dx.doi.org/10.4018/ijarb.2019070101.

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Digital models are needed in medicine using bioinformatics for diagnosis and prediction. Such models are especially needed in personalized medicine using bioinformatics. In this area, it is necessary to evaluate and predict the patient's condition from a priori knowledge obtained from other patients. Therefore, a new direction appeared - predictive medicine using bioinformatics. Predictive medicine, or “in silico medicine” is the use of computer modelling and intelligent technologies in the diagnosis, treatment and prevention of diseases. Using predictive medicine, the doctor can determine the likelihood of the development of certain diseases and choose the optimal treatment using bioinformatics. Predictive medicine begins to be applied in surgery. The prognosis in surgery consists in the preoperative evaluation of various surgical interventions and in the evaluation of possible outcomes of surgical interventions.
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15

Maojo, V., J. A. Mitchell, and L. J. Frey. "Section 7: Bioinformatics: Bioinformatics Linkage of Heterogeneous Clinical and Genomic Information in Support of Personalized Medicine." Yearbook of Medical Informatics 16, no. 01 (2007): 98–105. http://dx.doi.org/10.1055/s-0038-1638533.

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SummaryBiomedical Informatics as a whole faces a difficult epistemological task, since there is no foundation to explain the complexities of modeling clinical medicine and the many relationships between genotype, phenotype, and environment. This paper discusses current efforts to investigate such relationships, intended to lead to better diagnostic and therapeutic procedures and the development of treatments that could make personalized medicine a reality.To achieve this goal there are a number of issues to overcome. Primary are the rapidly growing numbers of heterogeneous data sources which must be integrated to support personalized medicine. Solutions involving the use of domain driven information models of heterogeneous data sources are described in conjunction with controlled ontologies and terminologies. A number of such applications are discussed.Researchers have realized that many dimensions of biology and medicine aim to understand and model the informational mechanisms that support more precise clinical diagnostic, prognostic and therapeutic procedures. As long as data grows exponentially, novel Biomedical Informatics approaches and tools are needed to manage the data. Although researchers are typically able to manage this information within specific, usually narrow contexts of clinical investigation, novel approaches for both training and clinical usage must be developed.After some preliminary overoptimistic expectations, it seems clear now that genetics alone cannot transform medicine. In order to achieve this, heterogeneous clinical and genomic data source must be integrated in scientifically meaningful and productive systems. This will include hypothesis-driven scientific research systems along with well understood information systems to support such research. These in turn will enable the faster advancement of personalized medicine.
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16

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

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17

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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18

Han, Ke, Miao Wang, Lei Zhang, and Chunyu Wang. "Application of Molecular Methods in the Identification of Ingredients in Chinese Herbal Medicines." Molecules 23, no. 10 (2018): 2728. http://dx.doi.org/10.3390/molecules23102728.

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There are several kinds of Chinese herbal medicines originating from diverse sources. However, the rapid taxonomic identification of large quantities of Chinese herbal medicines is difficult using traditional methods, and the process of identification itself is prone to error. Therefore, the traditional methods of Chinese herbal medicine identification must meet higher standards of accuracy. With the rapid development of bioinformatics, methods relying on bioinformatics strategies offer advantages with respect to the speed and accuracy of the identification of Chinese herbal medicine ingredients. This article reviews the applicability and limitations of biochip and DNA barcoding technology in the identification of Chinese herbal medicines. Furthermore, the future development of the two technologies of interest is discussed.
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19

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

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20

Kulikowski, C. W., and C. A. Kulikowski. "Biomedical and Health Informatics in Translational Medicine." Methods of Information in Medicine 48, no. 01 (2009): 4–10. http://dx.doi.org/10.3414/me9135.

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Summary Objectives: To discuss translational medicine advances challenging biomedical and health informatics. Methods: Reviewing material presented at the Heidelberg 35th Anniversary Workshop, summarizing results from the 1st AMIA Summit on Translational Bioinformatics and discussing the opportunities, difficulties, and ethical dilemmas confronting researchers, practitioners, and healthcare managers in transitional bioinformatics. Results: The first results in translational medicine are appearing in the biomedical literature. All rely on bioinformatics methods for analysis. Conclusions: Translational medicine introduces new problems of interpretation and application to healthcare. Applying results to complex human-machine systems raises ethical issues, which are augmented in healthcare informatics. Bridging biological, medical, and informatics knowledge requires new epistemological approaches.
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21

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

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22

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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23

Suh, K. Stephen, Sreeja Sarojini, Maher Youssif, et al. "Tissue Banking, Bioinformatics, and Electronic Medical Records: The Front-End Requirements for Personalized Medicine." Journal of Oncology 2013 (2013): 1–12. http://dx.doi.org/10.1155/2013/368751.

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Personalized medicine promises patient-tailored treatments that enhance patient care and decrease overall treatment costs by focusing on genetics and “-omics” data obtained from patient biospecimens and records to guide therapy choices that generate good clinical outcomes. The approach relies on diagnostic and prognostic use of novel biomarkers discovered through combinations of tissue banking, bioinformatics, and electronic medical records (EMRs). The analytical power of bioinformatic platforms combined with patient clinical data from EMRs can reveal potential biomarkers and clinical phenotypes that allow researchers to develop experimental strategies using selected patient biospecimens stored in tissue banks. For cancer, high-quality biospecimens collected at diagnosis, first relapse, and various treatment stages provide crucial resources for study designs. To enlarge biospecimen collections, patient education regarding the value of specimen donation is vital. One approach for increasing consent is to offer publically available illustrations and game-like engagements demonstrating how wider sample availability facilitates development of novel therapies. The critical value of tissue bank samples, bioinformatics, and EMR in the early stages of the biomarker discovery process for personalized medicine is often overlooked. The data obtained also require cross-disciplinary collaborations to translate experimental results into clinical practice and diagnostic and prognostic use in personalized medicine.
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24

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

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25

Overby, Casey Lynnette, and Peter Tarczy-Hornoch. "Personalized medicine: challenges and opportunities for translational bioinformatics." Personalized Medicine 10, no. 5 (2013): 453–62. http://dx.doi.org/10.2217/pme.13.30.

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26

Star, Kremena. "Bioinformatics at the Albert Einstein College of Medicine." Einstein Journal of Biology and Medicine 20, no. 2 (2016): 87. http://dx.doi.org/10.23861/ejbm200420432.

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27

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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28

Carter, Tonia C., and Max M. He. "Challenges of Identifying Clinically Actionable Genetic Variants for Precision Medicine." Journal of Healthcare Engineering 2016 (2016): 1–14. http://dx.doi.org/10.1155/2016/3617572.

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Advances in genomic medicine have the potential to change the way we treat human disease, but translating these advances into reality for improving healthcare outcomes depends essentially on our ability to discover disease- and/or drug-associated clinically actionable genetic mutations. Integration and manipulation of diverse genomic data and comprehensive electronic health records (EHRs) on a big data infrastructure can provide an efficient and effective way to identify clinically actionable genetic variants for personalized treatments and reduce healthcare costs. We review bioinformatics processing of next-generation sequencing (NGS) data, bioinformatics infrastructures for implementing precision medicine, and bioinformatics approaches for identifying clinically actionable genetic variants using high-throughput NGS data and EHRs.
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29

Captur, Gabriella, Rodney H. Stables, Dennis Kehoe, John Deanfield, and James C. Moon. "Why democratise bioinformatics?" BMJ Innovations 2, no. 4 (2016): 166–71. http://dx.doi.org/10.1136/bmjinnov-2016-000129.

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30

Luxon, B. A. "Bioinformatics for Dummies." JAMA: The Journal of the American Medical Association 290, no. 24 (2003): 3255. http://dx.doi.org/10.1001/jama.290.24.3255-a.

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31

Imani, Farzin. "Bioinformatics in Otolaryngology." Otolaryngologic Clinics of North America 38, no. 2 (2005): 321–32. http://dx.doi.org/10.1016/j.otc.2004.10.007.

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32

Matsuura, Masaaki. "Bioinformatics and Statistics — Omics Data Analysis for Personalized Medicine —." Japanese Journal of Biometrics 32, Special_Issue (2011): S51—S64. http://dx.doi.org/10.5691/jjb.32.s51.

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33

Zou, Quan. "Data Mining and Network Analytics in Bioinformatics and Medicine." Current Proteomics 15, no. 5 (2018): 343. http://dx.doi.org/10.2174/157016461505180924103528.

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34

Wu, Duojiao, Catherine M. Rice, and Xiangdong Wang. "Cancer bioinformatics: A new approach to systems clinical medicine." BMC Bioinformatics 13, no. 1 (2012): 71. http://dx.doi.org/10.1186/1471-2105-13-71.

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35

Wood, Alexander, Kayvan Najarian, and Delaram Kahrobaei. "Homomorphic Encryption for Machine Learning in Medicine and Bioinformatics." ACM Computing Surveys 53, no. 4 (2020): 1–35. http://dx.doi.org/10.1145/3394658.

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36

Bertolazzi, Paola, Jacek Blazewicz, and Metin Turkay. "Operations Research Models for Computational Biology, Bioinformatics and Medicine." Journal of Mathematical Modelling and Algorithms 9, no. 3 (2010): 209–11. http://dx.doi.org/10.1007/s10852-010-9135-z.

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37

Duan, Yibing. "Present Situation and Forecast of Bioinformatics in the Field of New Medicine Research and Development." E3S Web of Conferences 213 (2020): 03027. http://dx.doi.org/10.1051/e3sconf/202021303027.

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In the last several centuries, biology has accumulated a large number of data, which are disorganized and hard to be used repeatedly. Bioinformatics, synthesized informatics, statistics and some other subjects, makes them orderly and much more valuable. In drug discovery, Bioinformatics takes the place of some conventional ways because of low cast and high throughput. This article introduces the current situation and application of Bioinformatics in drug discovery and looks forward to the future, hoping to provide a Reference for the development of new drugs.
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38

Bromham, Lindell. "Putting the ‘bio’ into bioinformatics." Biology Letters 5, no. 3 (2009): 391–93. http://dx.doi.org/10.1098/rsbl.2009.0227.

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Bioinformatic analyses have grown rapidly in sophistication and efficiency to accommodate the vast increase in available data. One of the major challenges has been to incorporate the growing appreciation of the complexity of molecular evolution into new analytical methods. As the reliance on molecular data in biology and medicine increases, we need to be confident that these methods adequately reflect the underlying processes of genome change. This special issue focuses on the way that patterns and processes of molecular evolution are influenced by features of populations of whole organisms, such as selection pressure, population size and life history. The advantage of this approach to molecular evolution is that it views genomic change not simply as a biochemical or stochastic process, but as the result of a complex series of interactions that shape the kinds of genomic changes that can and do happen.
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39

Esteban, David J., Melissa Da Silva, and Chris Upton. "New bioinformatics tools for viral genome analyses at Viral Bioinformatics – Canada." Pharmacogenomics 6, no. 3 (2005): 271–80. http://dx.doi.org/10.1517/14622416.6.3.271.

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40

Simmons, M. K., P. Subramanian, J. M. Temkin, and B. D. Sarachan. "Combining Medical Informatics and Bioinformatics toward Tools for Personalized Medicine." Methods of Information in Medicine 42, no. 02 (2003): 111–15. http://dx.doi.org/10.1055/s-0038-1634320.

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Summary Objectives: Key bioinformatics and medical informatics research areas need to be identified to advance knowledge and understanding of disease risk factors and molecular disease pathology in the 21st century toward new diagnoses, prognoses, and treatments. Methods: Three high-impact informatics areas are identified: predictive medicine (to identify significant correlations within clinical data using statistical and artificial intelligence methods), along with pathway informatics and cellular simulations (that combine biological knowledge with advanced informatics to elucidate molecular disease pathology). Results: Initial predictive models have been developed for a pilot study in Huntington’s disease. An initial bioinformatics platform has been developed for the reconstruction and analysis of pathways, and work has begun on pathway simulation. Conclusions: A bioinformatics research program has been established at GE Global Research Center as an important technology toward next generation medical diagnostics. We anticipate that 21st century medical research will be a combination of informatics tools with traditional biology wet lab research, and that this will translate to increased use of informatics techniques in the clinic.
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41

Bottomly, Daniel, Shannon K. McWeeney, and Beth Wilmot. "HitWalker2: visual analytics for precision medicine and beyond." Bioinformatics 32, no. 8 (2015): 1253–55. http://dx.doi.org/10.1093/bioinformatics/btv739.

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42

Molidor, R. "New trends in bioinformatics: from genome sequence to personalized medicine." Experimental Gerontology 38, no. 10 (2003): 1031–36. http://dx.doi.org/10.1016/s0531-5565(03)00168-2.

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43

Spang, Rainer. "Diagnostic signatures from microarrays: a bioinformatics concept for personalized medicine." BIOSILICO 1, no. 2 (2003): 64–68. http://dx.doi.org/10.1016/s1478-5382(03)02329-1.

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44

Rossi, E. "The Bioinformatics of Psychosocial Genomics in Alternative and Complementary Medicine." Complementary Medicine Research 10, no. 3 (2003): 143–50. http://dx.doi.org/10.1159/000072212.

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45

Van Neste, Leander, and Wim Van Criekinge. "We are all individuals… bioinformatics in the personalized medicine era." Cellular Oncology 38, no. 1 (2014): 29–37. http://dx.doi.org/10.1007/s13402-014-0195-3.

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46

Qin, Zhaohui S. "Special collection of bioinformatics in the era of precision medicine." Quantitative Biology 5, no. 4 (2017): 277–79. http://dx.doi.org/10.1007/s40484-017-0128-z.

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47

Robison, Wade. "Bioinformatics and Privacy." Ethics in Biology, Engineering and Medicine 1, no. 1 (2010): 9–17. http://dx.doi.org/10.1615/ethicsbiologyengmed.v1.i1.30.

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48

Ammenwerth, E., R. Brandner, B. Brigl, et al. "Towards Clinical Bioinformatics: Advancing Genomic Medicine with Informatics Methods and Tools." Methods of Information in Medicine 43, no. 03 (2004): 302–7. http://dx.doi.org/10.1055/s-0038-1633872.

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Summary Objectives: To summarize the challenges facing clinical applications in the light of growing research results in genomic medicine and bioinformatics. Methods: Analysis of the contents of the Yearbook of Medical Informatics 2004 of the International Medical Informatics Association (IMIA). Results: The Yearbook of Medical Informatics 2004 includes 32 articles selected from 22 peer-reviewed scientific journals. A special section on clinical bio-informatics highlights recent developments in this field. Several guest editors review the promises and limitations of available methods and resources from biomedical informatics that are relevant to clinical medicine. Integrated data and knowledge resources are generally regarded to be central and key issues for clinical bioinformatics. Further review papers deal with public health implications of bioinformatics, knowledge management and trends in health care education. The Yearbook includes for the first time a section on the history of medical informatics, where the significant impact of the Reisensburg protocol 1973 on international health and medical informatics education is examined. Conclusions: Close collaboration between bio-informatics and medical informatics researchers can contribute to new insights in genomic medicine and contribute towards the more efficient and effective use of genomic data to advance clinical care.
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49

Jang, Hyunchul, Jinhyun Kim, Sang-Kyun Kim, et al. "Ontology for medicinal materials based on traditional Korean medicine." Bioinformatics 26, no. 18 (2010): 2359–60. http://dx.doi.org/10.1093/bioinformatics/btq424.

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50

Thorn, Caroline F., Teri E. Klein, and Russ B. Altman. "Pharmacogenomics and bioinformatics: PharmGKB." Pharmacogenomics 11, no. 4 (2010): 501–5. http://dx.doi.org/10.2217/pgs.10.15.

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