Journal articles: 'Medical Bioinformatics'

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Cheng, Phil F. "Medical bioinformatics in melanoma." Current Opinion in Oncology 30, no. 2 (2018): 113–17. http://dx.doi.org/10.1097/cco.0000000000000428.

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Haines, Stephen J. "Bioinformatics in medical practice." Neurosurgical Focus 19, no. 4 (2005): 1. http://dx.doi.org/10.3171/foc.2005.19.4.1.

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Orlov, Yuriy L., Ancha V. Baranova, and Tatiana V. Tatarinova. "Bioinformatics Methods in Medical Genetics and Genomics." International Journal of Molecular Sciences 21, no. 17 (2020): 6224. http://dx.doi.org/10.3390/ijms21176224.

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Medical genomics relies on next-gen sequencing methods to decipher underlying molecular mechanisms of gene expression. This special issue collects materials originally presented at the “Centenary of Human Population Genetics” Conference-2019, in Moscow. Here we present some recent developments in computational methods tested on actual medical genetics problems dissected through genomics, transcriptomics and proteomics data analysis, gene networks, protein–protein interactions and biomedical literature mining. We have selected materials based on systems biology approaches, database mining. These methods and algorithms were discussed at the Digital Medical Forum-2019, organized by I.M. Sechenov First Moscow State Medical University presenting bioinformatics approaches for the drug targets discovery in cancer, its computational support, and digitalization of medical research, as well as at “Systems Biology and Bioinformatics”-2019 (SBB-2019) Young Scientists School in Novosibirsk, Russia. Selected recent advancements discussed at these events in the medical genomics and genetics areas are based on novel bioinformatics tools.

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Elkin, Peter L. "Primer on Medical Genomics Part V: Bioinformatics." Mayo Clinic Proceedings 78, no. 1 (2003): 57–64. http://dx.doi.org/10.4065/78.1.57.

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Yan, Benedict, and Tin W. Tan. "Integrating translational bioinformatics into the medical curriculum." International Journal of Medical Education 5 (July 16, 2014): 132–34. http://dx.doi.org/10.5116/ijme.53ae.bc97.

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Majumder, Manojit. "Bioinformatics: a promising field for Medical Biochemists." Bangladesh Journal of Medical Biochemistry 7, no. 2 (2015): 39–40. http://dx.doi.org/10.3329/bjmb.v7i2.22410.

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Lu, Shen, and Richard S. Segall. "Linkage in medical records and bioinformatics data." International Journal of Information and Decision Sciences 5, no. 2 (2013): 169. http://dx.doi.org/10.1504/ijids.2013.053803.

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Bansard, J. Y., D. Rebholz-Schuhmann, G. Cameron, et al. "Medical Informatics and Bioinformatics: A Bibliometric Study." IEEE Transactions on Information Technology in Biomedicine 11, no. 3 (2007): 237–43. http://dx.doi.org/10.1109/titb.2007.894795.

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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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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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Land, Walker H., William Ford, Jin-Woo Park, et al. "Partial Least Squares (PLS) Applied to Medical Bioinformatics." Procedia Computer Science 6 (2011): 273–78. http://dx.doi.org/10.1016/j.procs.2011.08.051.

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Altman, R. B. "Towards Clinical Bioinformatics: Redux 2015." Yearbook of Medical Informatics 25, S 01 (2016): S6—S7. http://dx.doi.org/10.15265/iys-2016-s007.

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SummaryIn 2004, medical informatics as a scientific community recognized an emerging field of “clinical bioinformatics” that included work bringing bioinformatics data and knowledge into the clinic. In the intervening decade, “translational biomedical informatics” has emerged as the umbrella term for the work that brings together biological entities and clinical entities. The major challenges continue: understanding the clinical significance of basic ‘omics’ (and other) measurements, and communicating this to increasingly empowered patients/consumers who often have access to this information outside usual medical channels. It has become clear that basic molecular information must be combined with environmental and lifestyle data to fully define, predict, and manage health status..

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

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

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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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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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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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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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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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Maojo, Victor, Ilias Iakovidis, Fernando Martin-Sanchez, Jose Crespo, and Casimir Kulikowski. "Medical Informatics and Bioinformatics: European Efforts to Facilitate Synergy." Journal of Biomedical Informatics 34, no. 6 (2001): 423–27. http://dx.doi.org/10.1006/jbin.2002.1042.

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Morokhovets, H. Yu, and Yu V. Lysanets. "METHODS OF EFFECTIVE TEACHING BIOINFORMATICS IN MEDICAL ACADEMIC SETTING." Medical and Ecological Problems 22, no. 3-4 (2018): 18–21. http://dx.doi.org/10.31718/mep.2018.22.3-4.05.

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Higher medical education is a prerequisite of the present, requiring re-consideration of teaching methods and forms, as well as principles and approaches in accordance with the latest advances in science and technology. The priority direction in the transformation of higher medical education is training the competent specialists, capable of responding to the challenges of time, ready for self-improvement and continuous development. Training of specialists at the third educational and scientific level of higher education today requires forms and methods of training, aimed at the practical use of the knowledge gained.

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Rodriguez-Esteban, R., and W. T. Loging. "Quantifying the complexity of medical research." Bioinformatics 29, no. 22 (2013): 2918–24. http://dx.doi.org/10.1093/bioinformatics/btt505.

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Wiemer, J., F. Schubert, M. Granzow, et al. "Informatics United." Methods of Information in Medicine 42, no. 02 (2003): 126–33. http://dx.doi.org/10.1055/s-0038-1634323.

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Summary Objectives: Medical informatics, neuroinformatics and bioinformatics provide a wide spectrum of research. Here, we show the great potential of synergies between these research areas on the basis of four exemplary studies where techniques are transferred from one of the disciplines to the other. Methods: Reviewing and analyzing exemplary and specific projects at the intersection of medical informatics, neuroinformatics, and bioinformatics from our experience in an interdisciplinary research group. Results: Synergy emerges when techniques and solutions from medical informatics, bioinformatics, or neuroinformatics are successfully applied in one of the other disciplines. Synergy was found in 1. the modeling of neurophysiological systems for medical therapy development, 2. the use of image processing techniques from medical computer vision for the analysis of the dynamics of cell nuclei, and 3. the application of neuroinformatics tools for data mining in bioinformatics and as classifiers in clinical oncology. Conclusions: Each of the three different disciplines have delivered technologies that are readily applicable in the other disciplines. The mutual transfer of knowledge and techniques proved to increase efficiency and accuracy in a manifold of applications. In particular, we expect that clinical decision support systems based on techniques derived from neuro- and bioinformatics have the potential to improve medical diagnostics and will finally lead to a personalized delivery of healthcare.

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Lebo, Matthew S., Limin Hao, Chiao-Feng Lin, and Arti Singh. "Bioinformatics in Clinical Genomic Sequencing." Clinics in Laboratory Medicine 40, no. 2 (2020): 163–87. http://dx.doi.org/10.1016/j.cll.2020.02.003.

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Sartor, Maureen A., Alex Ade, Zach Wright, et al. "Metab2MeSH: annotating compounds with medical subject headings." Bioinformatics 28, no. 10 (2012): 1408–10. http://dx.doi.org/10.1093/bioinformatics/bts156.

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

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Counsell, Damian. "Workshop—Predicting the Structure of Biological Molecules." Comparative and Functional Genomics 5, no. 6-7 (2004): 480–90. http://dx.doi.org/10.1002/cfg.414.

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This April, in Cambridge (UK), principal investigators from the Mathematical Biology Group of the Medical Research Council's National Institute of Medical Research organized a workshop in structural bioinformatics at the Centre for Mathematical Sciences. Bioinformatics researchers of several nationalities from labs around the country presented and discussed their computational work in biomolecular structure prediction and analysis, and in protein evolution. The meeting was intensive and lively and gave attendees an overview of the healthy state of protein bioinformatics in the UK.

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Chattopadhyay, Ansuman, Carrie L. Iwema, Barbara A. Epstein, Adrian V. Lee, and Arthur S. Levine. "Molecular Biology Information Service: an innovative medical library-based bioinformatics support service for biomedical researchers." Briefings in Bioinformatics 21, no. 3 (2019): 876–84. http://dx.doi.org/10.1093/bib/bbz035.

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Abstract Biomedical researchers are increasingly reliant on obtaining bioinformatics training in order to conduct their research. Here we present a model that academic institutions may follow to provide such training for their researchers, based on the Molecular Biology Information Service (MBIS) of the Health Sciences Library System, University of Pittsburgh (Pitt). The MBIS runs a four-facet service with the following goals: (1) identify, procure and implement commercially licensed bioinformatics software, (2) teach hands-on workshops using bioinformatics tools to solve research questions, (3) provide in-person and email consultations on software/databases and (4) maintain a web portal providing overall guidance on the access and use of bioinformatics resources and MBIS-created webtools. This paper describes these facets of MBIS activities from 2006 to 2018, including outcomes from a survey measuring attitudes of Pitt researchers about MBIS service and performance.

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Moore, Alyssa C., Jonathan S. Winkjer, and Tsai-Tien Tseng. "Bioinformatics Resources for MicroRNA Discovery." Biomarker Insights 10s4 (January 2015): BMI.S29513. http://dx.doi.org/10.4137/bmi.s29513.

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Biomarker identification is often associated with the diagnosis and evaluation of various diseases. Recently, the role of microRNA (miRNA) has been implicated in the development of diseases, particularly cancer. With the advent of next-generation sequencing, the amount of data on miRNA has increased tremendously in the last decade, requiring new bioinformatics approaches for processing and storing new information. New strategies have been developed in mining these sequencing datasets to allow better understanding toward the actions of miRNAs. As a result, many databases have also been established to disseminate these findings. This review focuses on several curated databases of miRNAs and their targets from both predicted and validated sources.

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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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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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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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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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Sen, Pranab Kumar. "Burden of bioinformatics in medical research: Statistical perspectives and controversies." Journal of Statistical Planning and Inference 138, no. 2 (2008): 450–63. http://dx.doi.org/10.1016/j.jspi.2007.06.004.

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Kulikowski, C., and V. Maojo. "Medical Informatics and Bioinformatics: Integration or Evolution through Scientific Crises?" Methods of Information in Medicine 45, no. 05 (2006): 474–82. http://dx.doi.org/10.1055/s-0038-1634107.

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Summary Objectives: To contribute a new perspective on recent investigations into the scientific foundations of medical informatics (MI) and bioinformatics (BI). To support efforts that could generate synergies and new research directions. Methods: MI and BI are compared and contrasted from a philosophy of science perspective. Historical examples from MI and BI are analyzed based on contrasting viewpoints about the evolution of scientific disciplines. Results: Our analysis suggests that the scientific approaches of MI and BI involve different assumptions and foundations, which, together with largely non-overlapping communities of researchers for the two disciplines, have led to different courses of development. We indicate how their respective application domains, medicine, and biology may have contributed to these differences in development. Conclusions: An analysis from the point of view of the philosophy of science is characteristic of established scientific disciplines. From a Kuhnian perspective, both disciplines may be entering a period of scientific crisis, where their foundations are questioned and where new ideas (or paradigm shifts) and a progressive research programme are needed to advance them scientifically. We discuss research directions and trends both supporting and challenging integration of the subdisciplines of MI and BI into a unified field of biomedical informatics (BMI), centered around the evolution of information cybernetics.

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Wang, Lili, Kim Lipsey, Carol Murray, Neville Prendergast, and Paul Schoening. "The Bioinformatics Program at Washington University's Bernard Becker Medical Library." Medical Reference Services Quarterly 26, no. 2 (2007): 87–98. http://dx.doi.org/10.1300/j115v26n02_08.

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Holloway, Ele. "From Genotype to Phenotype: Linking Bioinformatics and Medical Informatics Ontologies." Comparative and Functional Genomics 3, no. 5 (2002): 447–50. http://dx.doi.org/10.1002/cfg.181.

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A small group of around 40 people came together at the Chancellors Conference Centre in Manchester for the Ontologies Workshop, chaired by Alan Rector and Robert Stevens. The workshop was, rather strangely, spread over 2 half days. In hindsight, this programme worked very well as it gave people the opportunity to chat over a drink on the Saturday evening and share ideas, before launching into the second half on the following day. The participants were from various walks of life, all with a common interest in finding out more about ontologies and promoting collaborations between the medical informatics and bioinformatics ontology communities.

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Oliver, Gavin R., Steven N. Hart, and Eric W. Klee. "Bioinformatics for Clinical Next Generation Sequencing." Clinical Chemistry 61, no. 1 (2015): 124–35. http://dx.doi.org/10.1373/clinchem.2014.224360.

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Abstract BACKGROUND Next generation sequencing (NGS)-based assays continue to redefine the field of genetic testing. Owing to the complexity of the data, bioinformatics has become a necessary component in any laboratory implementing a clinical NGS test. CONTENT The computational components of an NGS-based work flow can be conceptualized as primary, secondary, and tertiary analytics. Each of these components addresses a necessary step in the transformation of raw data into clinically actionable knowledge. Understanding the basic concepts of these analysis steps is important in assessing and addressing the informatics needs of a molecular diagnostics laboratory. Equally critical is a familiarity with the regulatory requirements addressing the bioinformatics analyses. These and other topics are covered in this review article. SUMMARY Bioinformatics has become an important component in clinical laboratories generating, analyzing, maintaining, and interpreting data from molecular genetics testing. Given the rapid adoption of NGS-based clinical testing, service providers must develop informatics work flows that adhere to the rigor of clinical laboratory standards, yet are flexible to changes as the chemistry and software for analyzing sequencing data mature.

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Greenhough, Beth. "Decontextualised? Dissociated? Detached? Mapping the Networks of Bioinformatics Exchange." Environment and Planning A: Economy and Space 38, no. 3 (2006): 445–63. http://dx.doi.org/10.1068/a37434.

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The author reviews debates surrounding the Icelandic government's Act on a Health Sector Database (139/1998), which provided for the licensing of a private company to build and operate a centralised database of Icelandic medical records for use in gene-discovery research. Using the concept of the ‘boundary object’ drawn from actor-network theory, the author argues that, through their exploitation as bodily commodities, medical records are actively engaged in redefining existing boundaries between medical and scientific space, between civic and individual concerns, and between human subjects and scientific objects.

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Ahram, Mamoun, and Emanuel F. Petricoin. "Proteomics Discovery of Disease Biomarkers." Biomarker Insights 3 (January 2008): BMI.S689. http://dx.doi.org/10.4137/bmi.s689.

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Recent technological developments in proteomics have shown promising initiatives in identifying novel biomarkers of various diseases. Such technologies are capable of investigating multiple samples and generating large amount of data end-points. Examples of two promising proteomics technologies are mass spectrometry, including an instrument based on surface enhanced laser desorption/ionization, and protein microarrays. Proteomics data must, however, undergo analytical processing using bioinformatics. Due to limitations in proteomics tools including shortcomings in bioinformatics analysis, predictive bioinformatics can be utilized as an alternative strategy prior to performing elaborate, high-throughput proteomics procedures. This review describes mass spectrometry, protein microarrays, and bioinformatics and their roles in biomarker discovery, and highlights the significance of integration between proteomics and bioinformatics.

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Abroun, Saeid, Najmaldin Saki, Rahim Fakher, and Farahnaz Asghari. "Biology and Bioinformatics of Myeloma Cell." Laboratory Hematology 18, no. 4 (2012): 30–41. http://dx.doi.org/10.1532/lh96.11003.

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Benton, A., J. H. Holmes, S. Hill, A. Chung, and L. Ungar. "medpie: an information extraction package for medical message board posts." Bioinformatics 28, no. 5 (2012): 743–44. http://dx.doi.org/10.1093/bioinformatics/bts030.

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Mugo, Jacquiline W., Ephifania Geza, Joel Defo, et al. "A multi-scenario genome-wide medical population genetics simulation framework." Bioinformatics 33, no. 19 (2017): 2995–3002. http://dx.doi.org/10.1093/bioinformatics/btx369.

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Clay, Michael R., and Kevin E. Fisher. "Bioinformatics Education in Pathology Training: Current Scope and Future Direction." Cancer Informatics 16 (January 1, 2017): 117693511770338. http://dx.doi.org/10.1177/1176935117703389.

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Training anatomic and clinical pathology residents in the principles of bioinformatics is a challenging endeavor. Most residents receive little to no formal exposure to bioinformatics during medical education, and most of the pathology training is spent interpreting histopathology slides using light microscopy or focused on laboratory regulation, management, and interpretation of discrete laboratory data. At a minimum, residents should be familiar with data structure, data pipelines, data manipulation, and data regulations within clinical laboratories. Fellowship-level training should incorporate advanced principles unique to each subspecialty. Barriers to bioinformatics education include the clinical apprenticeship training model, ill-defined educational milestones, inadequate faculty expertise, and limited exposure during medical training. Online educational resources, case-based learning, and incorporation into molecular genomics education could serve as effective educational strategies. Overall, pathology bioinformatics training can be incorporated into pathology resident curricula, provided there is motivation to incorporate, institutional support, educational resources, and adequate faculty expertise.

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Ouyang, Zenhwa, Jan Sargeant, Alison Thomas, et al. "A scoping review of ‘big data’, ‘informatics’, and ‘bioinformatics’ in the animal health and veterinary medical literature." Animal Health Research Reviews 20, no. 1 (2019): 1–18. http://dx.doi.org/10.1017/s1466252319000136.

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AbstractResearch in big data, informatics, and bioinformatics has grown dramatically (Andreu-Perez J, et al., 2015, IEEE Journal of Biomedical and Health Informatics 19, 1193–1208). Advances in gene sequencing technologies, surveillance systems, and electronic medical records have increased the amount of health data available. Unconventional data sources such as social media, wearable sensors, and internet search engine activity have also contributed to the influx of health data. The purpose of this study was to describe how ‘big data’, ‘informatics’, and ‘bioinformatics’ have been used in the animal health and veterinary medical literature and to map and chart publications using these terms through time. A scoping review methodology was used. A literature search of the terms ‘big data’, ‘informatics’, and ‘bioinformatics’ was conducted in the context of animal health and veterinary medicine. Relevance screening on abstract and full-text was conducted sequentially. In order for articles to be relevant, they must have used the words ‘big data’, ‘informatics’, or ‘bioinformatics’ in the title or abstract and full-text and have dealt with one of the major animal species encountered in veterinary medicine. Data items collected for all relevant articles included species, geographic region, first author affiliation, and journal of publication. The study level, study type, and data sources were collected for primary studies. After relevance screening, 1093 were classified. While there was a steady increase in ‘bioinformatics’ articles between 1995 and the end of the study period, ‘informatics’ articles reached their peak in 2012, then declined. The first ‘big data’ publication in animal health and veterinary medicine was in 2012. While few articles used the term ‘big data’ (n = 14), recent growth in ‘big data’ articles was observed. All geographic regions produced publications in ‘informatics’ and ‘bioinformatics’ while only North America, Europe, Asia, and Australia/Oceania produced publications about ‘big data’. ‘Bioinformatics’ primary studies tended to use genetic data and tended to be conducted at the genetic level. In contrast, ‘informatics’ primary studies tended to use non-genetic data sources and conducted at an organismal level. The rapidly evolving definition of ‘big data’ may lead to avoidance of the term.

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Vandamme, Anne-Mieke. "Bioinformatics in virus research." Infection, Genetics and Evolution 7, no. 3 (2007): 353. http://dx.doi.org/10.1016/j.meegid.2007.04.001.

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Lee-Barber, Jasmine, Violet Kulo, Harold Lehmann, Ada Hamosh, and Joann Bodurtha. "Bioinformatics for medical students: a 5-year experience using OMIM® in medical student education." Genetics in Medicine 21, no. 2 (2018): 493–97. http://dx.doi.org/10.1038/s41436-018-0076-7.

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Liò, P. "Phylogenomics and bioinformatics of SARS-CoV." Trends in Microbiology 12, no. 3 (2004): 106–11. http://dx.doi.org/10.1016/j.tim.2004.01.005.

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Sirintrapun, S. Joseph, Ahmet Zehir, Aijazuddin Syed, JianJiong Gao, Nikolaus Schultz, and Donavan T. Cheng. "Translational Bioinformatics and Clinical Research (Biomedical) Informatics." Clinics in Laboratory Medicine 36, no. 1 (2016): 153–81. http://dx.doi.org/10.1016/j.cll.2015.09.013.

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Journal articles on the topic 'Medical Bioinformatics'

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