Relevant bibliographies by topics / Medical Bioinformatics

Contents

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

Academic literature on the topic 'Medical Bioinformatics'

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

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Journal articles on the topic "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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Dissertations / Theses on the topic "Medical Bioinformatics"

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Wu, Xi. "Ontology-driven Web-based Medical Image Sharing Interface for Epilepsy Research." Case Western Reserve University School of Graduate Studies / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=case1496660866436638.

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Bao, Shunxing. "Algorithmic Enhancements to Data Colocation Grid Frameworks for Big Data Medical Image Processing." Thesis, Vanderbilt University, 2019. http://pqdtopen.proquest.com/#viewpdf?dispub=13877282.

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<p> Large-scale medical imaging studies to date have predominantly leveraged in-house, laboratory-based or traditional grid computing resources for their computing needs, where the applications often use hierarchical data structures (e.g., Network file system file stores) or databases (e.g., COINS, XNAT) for storage and retrieval. The resulting performance for laboratory-based approaches reveal that performance is impeded by standard network switches since typical processing can saturate network bandwidth during transfer from storage to processing nodes for even moderate-sized studies. On the other hand, the grid may be costly to use due to the dedicated resources used to execute the tasks and lack of elasticity. With increasing availability of cloud-based big data frameworks, such as Apache Hadoop, cloud-based services for executing medical imaging studies have shown promise.</p><p> Despite this promise, our studies have revealed that existing big data frameworks illustrate different performance limitations for medical imaging applications, which calls for new algorithms that optimize their performance and suitability for medical imaging. For instance, Apache HBases data distribution strategy of region split and merge is detrimental to the hierarchical organization of imaging data (e.g., project, subject, session, scan, slice). Big data medical image processing applications involving multi-stage analysis often exhibit significant variability in processing times ranging from a few seconds to several days. Due to the sequential nature of executing the analysis stages by traditional software technologies and platforms, any errors in the pipeline are only detected at the later stages despite the sources of errors predominantly being the highly compute-intensive first stage. This wastes precious computing resources and incurs prohibitively higher costs for re-executing the application. To address these challenges, this research propose a framework - Hadoop &amp; HBase for Medical Image Processing (HadoopBase-MIP) - which develops a range of performance optimization algorithms and employs a number of system behaviors modeling for data storage, data access and data processing. We also introduce how to build up prototypes to help empirical system behaviors verification. Furthermore, we introduce a discovery with the development of HadoopBase-MIP about a new type of contrast for medical imaging deep brain structure enhancement. And finally we show how to move forward the Hadoop based framework design into a commercialized big data / High performance computing cluster with cheap, scalable and geographically distributed file system.</p><p>
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Goldstein, Theodore C. "Tools for extracting actionable medical knowledge from genomic big data." Thesis, University of California, Santa Cruz, 2013. http://pqdtopen.proquest.com/#viewpdf?dispub=3589324.

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<p> Cancer is an ideal target for personal genomics-based medicine that uses high-throughput genome assays such as DNA sequencing, RNA sequencing, and expression analysis (collectively called <i>omics</i>); however, researchers and physicians are overwhelmed by the quantities of big data from these assays and cannot interpret this information accurately without specialized tools. To address this problem, I have created software methods and tools called <i>OCCAM</i> (OmiC&nbsp;data Cancer Analytic Model) and DIPSC (Differential Pathway Signature Correlation) for automatically extracting knowledge from this data and turning it into an actionable knowledge base called the <i>activitome.</i> An activitome signature measures a mutation's effect on the cellular molecular pathway. As well, activitome signatures can also be computed for clinical phenotypes. By comparing the vectors of activitome signatures of different mutations and clinical outcomes, intrinsic relationships between these events may be uncovered. OCCAM identifies activitome signatures that can be used to guide the development and application of therapies. DIPSC overcomes the confounding problem of correlating multiple activitome signatures from the same set of samples. In addition, to support the collection of this big data, I have developed <i>MedBook,</i> a federated distributed social network designed for a medical research and decision support system. OCCAM and DIPSC are two of the many apps that will operate inside of MedBook. MedBook extends the Galaxy system with a signature database, an end-user oriented application platform, a rich data medical knowledge-publishing model, and the Biomedical Evidence Graph (BMEG). The goal of MedBook is to improve the outcomes by learning from every patient.</p>
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Chi, Chih-Lin. "Medical decision support systems based on machine learning." Diss., University of Iowa, 2009. https://ir.uiowa.edu/etd/283.

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This dissertation discusses three problems from different areas of medical research and their machine learning solutions. Each solution is a distinct type of decision support system. They show three common properties: personalized healthcare decision support, reduction of the use of medical resources, and improvement of outcomes. The first decision support system assists individual hospital selection. This system can help a user make the best decision in terms of the combination of mortality, complication, and travel distance. Both machine learning and optimization techniques are utilized in this type of decision support system. Machine learning methods, such as Support Vector Machines, learn a decision function. Next, the function is transformed into an objective function and then optimization methods are used to find the values of decision variables to reach the desired outcome with the most confidence. The second decision support system assists diagnostic decisions in a sequential decision-making setting by finding the most promising tests and suggesting a diagnosis. The system can speed up the diagnostic process, reduce overuse of medical tests, save costs, and improve the accuracy of diagnosis. In this study, the system finds the test most likely to confirm a diagnosis based on the pre-test probability computed from the patient's information including symptoms and the results of previous tests. If the patient's disease post-test probability is higher than the treatment threshold, a diagnostic decision will be made, and vice versa. Otherwise, the patient needs more tests to help make a decision. The system will then recommend the next optimal test and repeat the same process. The third decision support system recommends the best lifestyle changes for an individual to lower the risk of cardiovascular disease (CVD). As in the hospital recommendation system, machine learning and optimization are combined to capture the relationship between lifestyle and CVD, and then generate recommendations based on individual factors including preference and physical condition. The results demonstrate several recommendation strategies: a whole plan of lifestyle changes, a package of n lifestyle changes, and the compensatory plan (the plan that compensates for unwanted lifestyle changes or real-world limitations).
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Ramraj, Varun. "Exploiting whole-PDB analysis in novel bioinformatics applications." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:6c59c813-2a4c-440c-940b-d334c02dd075.

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The Protein Data Bank (PDB) is the definitive electronic repository for experimentally-derived protein structures, composed mainly of those determined by X-ray crystallography. Approximately 200 new structures are added weekly to the PDB, and at the time of writing, it contains approximately 97,000 structures. This represents an expanding wealth of high-quality information but there seem to be few bioinformatics tools that consider and analyse these data as an ensemble. This thesis explores the development of three efficient, fast algorithms and software implementations to study protein structure using the entire PDB. The first project is a crystal-form matching tool that takes a unit cell and quickly (< 1 second) retrieves the most related matches from the PDB. The unit cell matches are combined with sequence alignments using a novel Family Clustering Algorithm to display the results in a user-friendly way. The software tool, Nearest-cell, has been incorporated into the X-ray data collection pipeline at the Diamond Light Source, and is also available as a public web service. The bulk of the thesis is devoted to the study and prediction of protein disorder. Initially, trying to update and extend an existing predictor, RONN, the limitations of the method were exposed and a novel predictor (called MoreRONN) was developed that incorporates a novel sequence-based clustering approach to disorder data inferred from the PDB and DisProt. MoreRONN is now clearly the best-in-class disorder predictor and will soon be offered as a public web service. The third project explores the development of a clustering algorithm for protein structural fragments that can work on the scale of the whole PDB. While protein structures have long been clustered into loose families, there has to date been no comprehensive analytical clustering of short (~6 residue) fragments. A novel fragment clustering tool was built that is now leading to a public database of fragment families and representative structural fragments that should prove extremely helpful for both basic understanding and experimentation. Together, these three projects exemplify how cutting-edge computational approaches applied to extensive protein structure libraries can provide user-friendly tools that address critical everyday issues for structural biologists.
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Wendler, Jason Patrick. "Accessing complex genomic variation in Plasmodium falciparum natural infections." Thesis, University of Oxford, 2015. http://ora.ox.ac.uk/objects/uuid:c9f1ea37-7005-4757-a869-7eba82406a26.

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Genetic polymorphism in Plasmodium falciparum is a considerable obstacle to malaria intervention. Parasites have repeatedly evolved to overcome every front-line antimalarial deployed throughout history, and artemisinin resistant populations are expanding in Southeast Asia. Promising vaccine candidates routinely fail when challenged by the genetic diversity of natural parasite populations, and a recent trial using a blood-stage antigen showed immunity was allele specific. Modern sequencing technologies have revolutionized our understanding of parasite genomics and population genetics by providing access to single nucleotide variation, but characterizing more complex polymorphism remains a key challenge. Solving this problem is important because the selective pressures from drugs and host immunity often create complex polymorphism in the most clinically relevant genes that is missed using standard genotyping methods. In three sections, this thesis is a narrative about 1) encountering complex variation, 2) overcoming it with novel tools, and then 3) innovatively applying those tools to old and new questions. I first show examples of complex variation in a vaccine candidate (EBA-175) and a drug resistance gene (pfcrt) while reporting SNP based analyses of Kenyan and Tanzanian field isolates. While introducing this complex variation I also describe biological insights discovered in these populations. In Kenya I show evidence that chloroquine resistance selects for parasites that are primaquine sensitive, use a GWAS approach to discover new drug resistance loci, and catalogue variation in known resistance genes. In Tanzania I describe the population structure and allele frequencies of parasites from two geographic regions. In the second section of the thesis I develop methods for accessing complex variation and demonstrate their utility by producing de novo assemblies of eba-175, pfcrt, ama1, and msp3.4 from thousands of sequenced samples. Finally, in the third section I apply these tools in depth to eba-175. I comprehensively characterize the SNP and structural variation in eba-175 using an alignment of 1419 de novo assemblies. I use this resource to illustrate the profiles of positive selection across the gene, and corroborate these signals of balancing selection by showing the geographic distribution of the F/C indels and a lesser known 6bp indel positioned between the DBL domains. I then use the alignments to design Sequenom genotyping assays that facilitate a genome wide association study, testing for human associations with the eba-175 indels in the infecting parasite. I close by reporting a potential association on human chromosome 14 with the 6bp indel in eba-175.
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Albukhnefis, Adil Lateef Mahmood. "Nuclei and Nucleoli Segmentation and Analysis." Kent State University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=kent1461260282.

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Yako, Yandiswa. "Bioinformatics-based strategies to identify PFHBII-causing and HCM main locus and/or HCM modifying mutations." Thesis, Stellenbosch : University of Stellenbosch, 2004. http://hdl.handle.net/10019.1/16473.

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Thesis (MSc)--University of Stellenbosch, 2004.<br>ENGLISH ABSTRACT: Progressive familial heart block type II (PFHBII) is an inherited cardiac conduction disorder of unknown aetiology, which has been described in a South African family. The disorder was mapped to a 2.9 centimorgan (cM) locus on chromosome 1q32.2-32.3. Clinically, PFHBII manifests cardiac conduction aberrations, that progress to a disease of the heart muscle, dilated cardiomyopathy (DCM). DCM is also reported as an end phase in hypertrophic cardiomyopathy (HCM), another heart muscle disorder. These cardiomyopathies are genetically heterogeneous with some of the genes reported as causes of both disorders. Therefore, genes identified as causes of HCM and DCM were considered plausible candidates for PFHBII mutation analysis. Additionally, this study provided an opportunity to assess potential modifiers of HCM. HCM exhibits marked phenotypic variability, observed within and between families harbouring the same causative mutation. Genes within the PFHBII locus were selected for PCR-SSCP analysis based on homology to genes previously reported as causing conduction system disorders associated with arrhythmias, DCM and/or HCM. Results were confirmed by direct sequencing and association between the detected variants and HCM parameters was assessed using a quantitative transmission disequilibrium test (QTDT). Eleven plausible candidate genes were selected within the PFHBII locus and two of the genes, PFKFB2 and ATF3, that encode for 6-phosphofructo-2,6-bisphosphatase (PFK-2/FBPase-2) and activating transcription factor 3 (ATF3), respectively, were analysed for PFHBII-causing and HCM main locus and/or HCM modifying mutations. Mutation analysis of PFKFB2 and ATF3 in the PFHBII family revealed no PFHBII causal mutation. PFKFB2 and ATF3 were later localised outside the PFHBII locus, and, therefore, were excluded as PFHBII plausible candidates. Further analysis of the two genes for HCM main locus and/or HCM modifying mutations in the HCM panel identified several sequence variants. QTDT analysis of these variants showed no significant association. Completion of the Human Genome Project (HGP) and annotation of new genes within the PFHBII locus allowed the identification of more PFHBII plausible candidate genes. Identification of causal mutations in plausible PFHBII candidate genes will allow molecular diagnosis of PFHBII pathophysiology. Furthermore, identification of both HCM-modifying and HCM-causing genes will give insight into the phenotypic variability noted among South African HCM-affected individuals and into the molecular cause of the disease among individuals with HCM-like clinical features.<br>AFRIKAANSE OPSOMMING: Progressiewe familiële hartblok tipe II (PFHBII) is ʼn oorgeërfde hart geleidingsiekte van onbekende etiologie wat in ʼn Suid-Afrikaanse familie beskryf is. Die siekte is ʼn 2.9 sentimorgan (cM) lokus op chromosoom 1q32.2-32.3 gekarteer. Klinies presenteer PFHBII met geleidingsfwykings wat uitloop op gedilateerde kardiomiopatie (DCM). DCM word ook gerapporteer as ʼn endfase in hipertrofiese kardiomiopatie (HCM), ʼn ander hartspiersiekte. Die kardiomiopatieë is geneties heterogeen, met ʼn aantal gene wat as oorsaak van altwee siektetoestande gerapporteer word. Daarom is alle gene wat geïdentifiseer is as oorsake van DCM en HCM, as moontlike kandidaatgene vir PFHBII mutasieanaliese beskou. Bykomend het hierdie studie die geleentheid gebied om potensiële modifiseerders van HCM te assesseer. HCM toon beduidende fenotipiese variasie binne en tussen families wat dieselfde siekteveroorsakende mutasie het. Gene binne die PFHBII-lokus is geselekteer vir PCR-SSCP-analiese gebaseer op homologie met gene wat voorheen gerapporteer is om betrokke te wees by geleidingsiesisteemsiektes, geassosieerde arritmieë, DCM en/of HCM. Resultate is bevestig deur volgordebepaling. Assosiasie tusssen ontdekte variante en die siekteparameter is bepaal met ʼn kwantitatiewe transmissie disekwilibrium toets (QTDT). Elf moontlike kandidaatgene in die PFHBII-lokus is geselekteer en twee van die gene, PFKFB2 en ATF3, wat kodeer vir 6-fosfofrukto-2,6-bifosfatase (PFK-2/FBPase-2) en aktiveringstranskripsiefaktor 3 (ATF3) respektiewelik, is vir PFHBII-oorsakende en HCMhooflokus en/of HCM-modifiseerende mutasies ondersoek. Mutasie-analiese van PFKFB2 en ATF3 in die PFHBII-familie het nie ʼn siekteveroorsakende mutasie onthul/uitgelig nie. PFKFB2 en ATF3 is later buite die PFHBII-lokus geplaas en dus ook as moontlike PFHBII-kandidate uitgesluit. Verdere ondersoek van díe twee gene vir HCM-hooflokus en/of HCM-modifiserende mutasies in die HCM-paneel het ʼn aantal volgorde variante geïdentifiseer. QTDT-analiese van die variante het geen beduidende assosiasies aangetoon nie. Voltooiing van die Menslike Genoom Projek (HGP) en annotasie van nuwe gene in die PFHBIIlokus het tot die identifikasie van verdere moontlike PFHBII-kandidaatgene gelei. Identifikase van siekte-veroorsaakende mutasies in die moontlike PFHBII-kandidaatgene sal die molekulêre diagnose van PFHBII toelaat en insig in die patofisiologie van die siekte gee. Verder, identifikasie van beide HCM-veroorsakende of HCM-modifiserende gene kan insig gee in die fenotipiese varieerbaarheid wat onder Suid-Afrikaanse HCM-geaffekteerde individue waargeneem word en ook in die molekulêre oorsake van die siekte in individue met HCMsoortige kliniese kenmerke.
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Garbom, Sara. "A strategy to identify novel antimicrobial compounds : a bioinformatics and HTS approach." Doctoral thesis, Umeå : Department of Molecular Biology, Umeå University, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-900.

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Kalari, Krishna Rani. "Computational approach to identify deletions or duplications within a gene." Diss., University of Iowa, 2006. http://ir.uiowa.edu/etd/64.

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Books on the topic "Medical Bioinformatics"

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Wuhan, China) International Conference on Medical Engineering and Bioinformatics (2014. Medical engineering and bioinformatics. WIT Press, 2015.

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Muppalaneni, Naresh Babu, Maode Ma, and Sasikumar Gurumoorthy. Soft Computing and Medical Bioinformatics. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-0059-2.

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Weston, Paul. Bioinformatics Software Engineering. John Wiley & Sons, Ltd., 2005.

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Husmeier, Dirk, Richard Dybowski, and Stephen Roberts, eds. Probabilistic Modeling in Bioinformatics and Medical Informatics. Springer London, 2005. http://dx.doi.org/10.1007/b138794.

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Bioinformatics Biocomputing and Perl. John Wiley & Sons, Ltd., 2005.

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1966-, Barry Paul, ed. Bioinformatics, biocomputing and Perl: An introduction to bioinformatics computing skills and practice. Wiley, 2004.

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Bisbee, Chester Allan. Bioinformatics: Biotechnology enters the information age. International Business Communications, 1997.

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1978-, Stajich Jason Eric, Hansen David, and SpringerLink (Online service), eds. Bioinformatics: Tools and Applications. Springer-Verlag New York, 2009.

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Bioinformatics methods in clinical research. Humana Press, 2010.

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International Workshop on Practical Applications of Computational Biology and Bioinformatics (4th 2010 Guimarães, Portugal). Advances in bioinformatics: 4th International Workshop on Practical Applications of Computational Biology and Bioinformatics 2010 (IWPACBB 2010). Springer, 2010.

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Book chapters on the topic "Medical Bioinformatics"

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Altman, Russ B. "Bioinformatics." In Medical Informatics. Springer New York, 2001. http://dx.doi.org/10.1007/978-0-387-21721-5_18.

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Ramsden, Jeremy J. "Medical applications." In Bioinformatics: An Introduction. Springer Netherlands, 2004. http://dx.doi.org/10.1007/978-1-4020-2950-9_16.

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Schröttner, Jörg, Robert Neubauer, and Christian Baumgartner. "Standards and Regulations for (Bio)Medical Software." In Translational Bioinformatics. Springer Netherlands, 2016. http://dx.doi.org/10.1007/978-94-017-7543-4_12.

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Daniel, C., E. Albuisson, T. Dart, P. Avillach, M. Cuggia, and Y. Guo. "Translational Bioinformatics and Clinical Research Informatics." In Medical Informatics, e-Health. Springer Paris, 2013. http://dx.doi.org/10.1007/978-2-8178-0478-1_17.

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Song, Yeong-Tae, Neekoo Torkian, and Fatimah Al-Dossary. "Ontological Personal Healthcare Using Medical Standards." In Bioinformatics and Biomedical Engineering. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16480-9_50.

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Wu, Menglin, and Quansen Sun. "Multi-modality Medical Case Retrieval Using Heterogeneous Information." In Intelligent Computing in Bioinformatics. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09330-7_11.

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Dobrovolny, Michal, Karel Mls, Ondrej Krejcar, Sebastien Mambou, and Ali Selamat. "Medical Image Data Upscaling with Generative Adversarial Networks." In Bioinformatics and Biomedical Engineering. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45385-5_66.

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Berestneva, Olga G., Olga V. Marukhina, Sergey V. Romanchukov, and Elena V. Berestneva. "Visualization and Cognitive Graphics in Medical Scientific Research." In Bioinformatics and Biomedical Engineering. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-17935-9_39.

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Kumari, Archana, Swarna Kanchan, Rajeshwar P. Sinha, and Minu Kesheri. "Applications of Bio-molecular Databases in Bioinformatics." In Medical Imaging in Clinical Applications. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-33793-7_15.

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Otawova, Radka, Vojtech Kamensky, Pavla Hasenohrlova, and Vladimir Rogalewicz. "Cost-Effectiveness Studies on Medical Devices: Application in Cardiology." In Bioinformatics and Biomedical Engineering. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16483-0_16.

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Conference papers on the topic "Medical Bioinformatics"

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"Session MP8a2: Bioinformatics and medical imaging." In 2014 48th Asilomar Conference on Signals, Systems and Computers. IEEE, 2014. http://dx.doi.org/10.1109/acssc.2014.7094473.

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Wasnik, S., P. Donachy, T. Harmer, et al. "GeneGrid: From "Virtual" Bioinformatics Laboratory to "Smart" Bioinformatics Laboratory." In Proceedings. 19th IEEE International Symposium on Computer-Based Medical Systems. IEEE, 2006. http://dx.doi.org/10.1109/cbms.2006.90.

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Rodríguez-Segura, M. A., J. J. Godina-Nava, and S. Villa-Treviño. "The bioinformatics of microarrays to study cancer: Advantages and disadvantages." In MEDICAL PHYSICS: Twelfth Mexican Symposium on Medical Physics. AIP, 2012. http://dx.doi.org/10.1063/1.4764632.

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Ahn, Insung, Jin-Hwa Jang, Youngmahn Han, and Thai Quang Tung. "Phylogenetic Analysis of Dengue viruses using Bioinformatics Techniques." In Bioscience and Medical Research 2015. Science & Engineering Research Support soCiety, 2015. http://dx.doi.org/10.14257/astl.2015.105.03.

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Bagiroz, Beyza, Emre Doruk, and Oktay Yildiz. "Machine Learning In Bioinformatics: Gene Expression And Microarray Studies." In 2020 Medical Technologies Congress (TIPTEKNO). IEEE, 2020. http://dx.doi.org/10.1109/tiptekno50054.2020.9299285.

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Han, Youngmahn, and Hyunsik Kim. "A Scalable Computing Framework for Large-Scale Bioinformatics Analysis." In Bioscience and Medical Research 2013. Science & Engineering Research Support soCiety, 2013. http://dx.doi.org/10.14257/astl.2013.33.11.

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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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Nishikawa, Robert. "“Imaging in the age of medical bioinformatics”." In Engineering Conference (BSEC): Exploring the Intersections of Interdisciplinary Biomedical Research. IEEE, 2009. http://dx.doi.org/10.1109/bsec.2009.5090445.

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Zhu, Xiaoping, Xiaohong Li, and Ke Yu. "The Application of Medical Virtual Simulation Technology in Medical Education." In 2015 International Forum on Bioinformatics and Medical Engineering. Atlantis Press, 2015. http://dx.doi.org/10.2991/bme-15.2015.16.

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Mirto, Maria, Marco Passante, and Giovanni Aloisio. "The ProGenGrid virtual laboratory for bioinformatics." In 2012 25th IEEE International Symposium on Computer-Based Medical Systems (CBMS). IEEE, 2012. http://dx.doi.org/10.1109/cbms.2012.6266328.

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Reports on the topic "Medical Bioinformatics"

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Srivastava, Anuj. A Statistical Theory for Shape Analysis of Curves and Surfaces with Applications in Image Analysis, Biometrics, Bioinformatics and Medical Diagnostics. Defense Technical Information Center, 2010. http://dx.doi.org/10.21236/ada532601.

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