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Relevant bibliographies by topics / Bioinformatics Molecular biology Bioinformatics

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Academic literature on the topic 'Bioinformatics Molecular biology Bioinformatics'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "Bioinformatics Molecular biology Bioinformatics"

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Liang, Xin, Wen Zhu, Zhibin Lv, and Quan Zou. "Molecular Computing and Bioinformatics." Molecules 24, no. 13 (2019): 2358. http://dx.doi.org/10.3390/molecules24132358.

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Molecular computing and bioinformatics are two important interdisciplinary sciences that study molecules and computers. Molecular computing is a branch of computing that uses DNA, biochemistry, and molecular biology hardware, instead of traditional silicon-based computer technologies. Research and development in this area concerns theory, experiments, and applications of molecular computing. The core advantage of molecular computing is its potential to pack vastly more circuitry onto a microchip than silicon will ever be capable of—and to do it cheaply. Molecules are only a few nanometers in size, making it possible to manufacture chips that contain billions—even trillions—of switches and components. To develop molecular computers, computer scientists must draw on expertise in subjects not usually associated with their field, including organic chemistry, molecular biology, bioengineering, and smart materials. Bioinformatics works on the contrary; bioinformatics researchers develop novel algorithms or software tools for computing or predicting the molecular structure or function. Molecular computing and bioinformatics pay attention to the same object, and have close relationships, but work toward different orientations.

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LLOYD, A. "C028 Bioinformatics and molecular biology." Journal of the European Academy of Dermatology and Venereology 9 (September 1997): S64. http://dx.doi.org/10.1016/s0926-9959(97)89102-7.

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Thornton, Janet, Graham Cameron, and Cath Brooksbank. "The European Bioinformatics Institute: Leading the bioinformatics revolution." Biochemist 26, no. 4 (2004): 33–38. http://dx.doi.org/10.1042/bio02604033.

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Life without databases is almost inconceivable to today's researchers in the biomolecular sciences -- the world of biomolecules is freely available on the Internet, along with a powerful set of tools for analysing the data. Many of the world's most widely used data resources are hosted and developed at the European Molecular Biology Laboratory (EMBL)'s European Bioinformatics Institute (EBI), often in collaboration with partners throughout the world. The EBI is also a thriving research centre.

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Lederman, Lynne. "Bioinformatics and Systems Biology." BioTechniques 46, no. 7 (2009): 501–3. http://dx.doi.org/10.2144/000113177.

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Rajpal, Deepak K. "Understanding Biology Through Bioinformatics." International Journal of Toxicology 24, no. 3 (2005): 147–52. http://dx.doi.org/10.1080/10915810590948325.

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During the journey from the discovery of DNA to be the source of genetic information and elucidation of double-helical nature of DNA molecule to the assembly of human genome sequence and there after, bioinformatics has become an integral part of modern biology. Bioinformatics relies substantially on significant contributions made by scientists in various fields, including but not limited to, linguistics, biology, mathematics, computer science, and statistics. There is an ever increasing amount of data to elucidate toxic mechanisms and/or adverse effects of xenobiotics in the field of toxicogenomics. Annotation in combination with various bioinformatics analytical tools can play a crucial role in the understanding of genes and proteins, and can potentially help draw meaningful conclusions from various data sources. This article attempts to present a simple overview of bioinformatics, and an effort is made to discuss annotation.

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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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Counsell, Damian. "Bioinformatics and Molecular Evolution." Comparative and Functional Genomics 6, no. 5-6 (2005): 317–19. http://dx.doi.org/10.1002/cfg.486.

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Grisham, William, Natalie A. Schottler, Joanne Valli-Marill, Lisa Beck, and Jackson Beatty. "Teaching Bioinformatics and Neuroinformatics by Using Free Web-based Tools." CBE—Life Sciences Education 9, no. 2 (2010): 98–107. http://dx.doi.org/10.1187/cbe.09-11-0079.

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This completely computer-based module's purpose is to introduce students to bioinformatics resources. We present an easy-to-adopt module that weaves together several important bioinformatic tools so students can grasp how these tools are used in answering research questions. Students integrate information gathered from websites dealing with anatomy (Mouse Brain Library), quantitative trait locus analysis (WebQTL from GeneNetwork), bioinformatics and gene expression analyses (University of California, Santa Cruz Genome Browser, National Center for Biotechnology Information's Entrez Gene, and the Allen Brain Atlas), and information resources (PubMed). Instructors can use these various websites in concert to teach genetics from the phenotypic level to the molecular level, aspects of neuroanatomy and histology, statistics, quantitative trait locus analysis, and molecular biology (including in situ hybridization and microarray analysis), and to introduce bioinformatic resources. Students use these resources to discover 1) the region(s) of chromosome(s) influencing the phenotypic trait, 2) a list of candidate genes—narrowed by expression data, 3) the in situ pattern of a given gene in the region of interest, 4) the nucleotide sequence of the candidate gene, and 5) articles describing the gene. Teaching materials such as a detailed student/instructor's manual, PowerPoints, sample exams, and links to free Web resources can be found at http://mdcune.psych.ucla.edu/modules/bioinformatics .

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de Bono, Stephanie. "Bioinformatics boost." Trends in Biochemical Sciences 26, no. 7 (2001): 413. http://dx.doi.org/10.1016/s0968-0004(01)01914-4.

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Martz, Eric. "Structural bioinformatics." Biochemistry and Molecular Biology Education 31, no. 5 (2003): 370–71. http://dx.doi.org/10.1002/bmb.2003.494031059996.

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Dissertations / Theses on the topic "Bioinformatics Molecular biology Bioinformatics"

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Pettersson, Fredrik. "A multivariate approach to computational molecular biology." Doctoral thesis, Umeå : Univ, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-609.

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Lopes, Pinto Fernando. "Development of Molecular Biology and Bioinformatics Tools : From Hydrogen Evolution to Cell Division in Cyanobacteria." Doctoral thesis, Uppsala universitet, Institutionen för fotokemi och molekylärvetenskap, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-110842.

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The use of fossil fuels presents a particularly interesting challenge - our society strongly depends on coal and oil, but we are aware that their use is damaging the environment. Currently, this awareness is gaining momentum, and pressure to evolve towards an energetically cleaner planet is very strong. Molecular hydrogen (H2) is an environmentally suitable energy carrier that could initially supplement or even substitute fossil fuels. Ideally, the primary energy source to produce hydrogen gas should be renewable, and the process of conversion back to energy without polluting emissions, making this cycle environmentally clean. Photoconversion of water to hydrogen can be achieved using the following strategies: 1) the use of photochemical fuel cells, 2) by applying photovoltaics, or 3) by promoting production of hydrogen by photosynthetic microorganisms, either phototrophic anoxygenic bacteria and cyanobacteria or eukaryotic green algae. For photobiological H2 production cyanobacteria are among the ideal candidates since they: a) are capable of H2 evolution, and b) have simple nutritional requirements - they can grow in air (N2 and CO2), water and mineral salts, with light as the only energy source. As this project started, a vision and a set of overall goals were established. These postulated that improved H2 production over a long period demanded: 1) selection of strains taking in consideration their specific hydrogen metabolism, 2) genetic modification in order to improve the H2 evolution, and 3) cultivation conditions in bioreactors should be exmined and improved. Within these goals, three main research objectives were set: 1) update and document the use of cyanobacteria for hydrogen production, 2) create tools to improve molecular biology work at the transcription analysis level, and 3) study cell division in cyanobacteria. This work resulted in: 1) the publication of a review on hydrogen evolution by cyanobacteria, 2) the development of tools to assist understanding of transcription, and 3) the start of a new fundamental research approach to ultimately improve the yield of H2 evolution by cyanobacteria.

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Sparring, Leonard. "Conquering Chemical Space : Optimization of Docking Libraries through Interconnected Molecular Features." Thesis, Uppsala universitet, Institutionen för biologisk grundutbildning, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-416977.

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Copied selected text to selection primary: The development of new pharmaceuticals is a long and ardous process that typically requires more than 10 years from target identification to approved drug. This process often relies on high throughput screening of molecular libraries. However, this is a costly and time-intensive approach and the selection of molecules to screen is not obvious, especially in relation to the size of chemical space, which has been estimated to consist of 10 60 compounds. To accelerate this exploration, molecules can be obtained from virtual chemical libraries and tested in-silico using molecular docking. Still, such methods are incapable of handling the increasingly colossal virtual libraries, currently reaching into the billions. As the libraries continue to expand, a pre-selection of compounds will be necessitated to allow accurate docking-predictions. This project aims to investigate whether the search for ligands in vast molecular libraries can be made more efficient with the aid of classifiers extended with the conformal prediction framework. This is also explored in conjunction with a fragment based approach, where information from smaller molecules are used to predict larger, lead-like molecules. The methods in this project are retrospectively tested with two clinically relevant G protein-coupled receptor targets, A 2A and D 2 . Both of these targets are involved in devastating disease, including Parkinson’s disease and cancer. The framework developed in this project has the capacity to reduce a chemical library of > 170 million tenfold, while retaining the 80 % of molecules scoring among the top 1 % of the entire library. Furthermore, it is also capable of finding known ligands. This will allow for reduction of ultra-large chemical libraries to manageable sizes, and will allow increased sampling of selected molecules. Moreover, the framework can be used as a modular extension on top of almost any classifier. The fragment-based approaches that were tested in this project performed unreliably and will be explored further.

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Yao, Xiaoquan. "Sequence features affecting translation initiation in eukaryotes: A bioinformatic approach." Thesis, University of Ottawa (Canada), 2008. http://hdl.handle.net/10393/27658.

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Sequence features play an important role in the regulation of translation initiation. This thesis focuses on the sequence features affecting eukaryotic initiation. The characteristics of 5' untranslated region in Saccharomyces cerevisiae were explored. It is found that the 40 nucleotides upstream of the start codon is the critical region for translation initiation in yeast. Moreover, this thesis attempted to solve some controversies related to the start codon context. Two key nucleotides in the start codon context are the third nucleotide upstream of the start codon (-3 site) and the nucleotide immediately following the start codon (+4 site). Two hypotheses regarding +4G (G at +4 site) in Kozak consensus, the translation initiation hypothesis and the amino acid constraint hypothesis, were tested. The relationship between the -3 and +4 sites in seven eukaryotic species does not support the translation initiation hypothesis. The amino acid usage at the position after the initiator (P1' position) compared to other positions in the coding sequences of seven eukaryotic species was examined. The result is consistent with the amino acid constraint hypothesis. In addition, this thesis explored the relationship between +4 nucleotide and translation efficiency in yeast. The result shows that +4 nucleotide is not important for translation efficiency, which does not support the translation initiation hypothesis. This work improves our current understanding of eukaryotic translation initiation process.

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Lesurf, Robert. "Molecular pathway analysis of mouse models for breast cancer." Thesis, McGill University, 2009. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=32499.

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Human breast cancer is an extremely heterogeneous disease, consisting of a number of different subtypes with varying levels of aggressiveness reflected by distinct, but largely undefined, molecular profiles. Here we have analyzed several novel mouse models for breast cancer in the context of the human subtypes, and have shown parallels between the mice and humans at numerous biologically relevant levels. In addition, we have developed a statistical framework to help elucidate the individual molecular components that are at play across a panel of human breast or murine mammary tumors. Our results indicate that, while no mouse model captures all aspects of the human disease, they each contain components that are shared by a subset of human breast tumors. Furthermore, our statistical framework provides numerous advantages over previous methodologies, in helping to reveal the individual molecular pathways that make up the biology of the tumors.<br>Le cancer du sein est connue pour être une maladie très hétérogène, composé d'un nombre de différents sous-types avec différents niveaux de l'agressivité et distinctes, mais indéfini, profils moléculaires. Ici, nous avons analysé plusieurs nouveaux modèles de souris pour le cancer du sein, dans le cadre des sous-types, et nous avons trouver des parallèles à un certain nombre de niveaux pertinents biologiques. En outre, nous avons développé une méthodologie statistique pour aider à élucider les différents composants moléculaires qui sont à jouer dans un groupe de tumours de sein d'humains ou mammaires murins. Nos résultats indiquent que, même si aucun modèle de souris capte tous les aspects de la maladie chez l'homme, chacun contiennent des composants qui sont partagées par un sous-ensemble de tumeurs mammaires humaines. En outre, notre outil statistique offre de nombreux avantages par rapport aux précédentes méthodes, pour aider à révéler les voies moléculaires qui composent la biologie des tumeurs.

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Lutimba, Stuart. "Determination of specificity and affinity of the Lactose permease (LacY) protein of Escherichia coli through application of molecular dynamics simulation." Thesis, Högskolan i Skövde, Institutionen för biovetenskap, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-15933.

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Proteins are essential in all living organisms. They are involved in various critical activities and are also structural components of cells and tissues. Lactose permease a membrane protein has become a prototype for the major facilitator super family and utilises an existing electrochemical proton gradient to shuttle galactoside sugars to the cell. Therefore it exists in two principle states exposing the internal binding site to either side of the membrane. From previous studies it has been suggested that protonation precedes substrate binding but it is still unclear why this has to occur in the event of substrate binding. Therefore this study aimed to bridge this gap and to determine the chemical characteristics of the transport pathway. Molecular dynamics simulation methods and specialised simulation hardware were employed to elucidate the dependency of substrate binding on the protonation nature of Lactose permease. Protein models that differed in their conformation as well as their protonation states were defined from their respective X-ray structures. Targeted molecular dynamics was implemented to drive the substrate to the binding site and umbrella sampling was used to define the free energy of the transport pathway. It was therefore suggested that protonation for sugar binding is due to the switch-like mechanism of Glu325 in the residue-residue interaction (His322 and Glu269) that leads to sugar binding only in the protonated state of LacY. Furthermore, the free energy profile of sugar transport path way was lower only in the protonated state which indicates stability of sugar binding in the protonated state.

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Chen, Huiling Zhou Huan Xiang Ferrone Frank A. "Prediction of protein structures and protein-protein interactions : a bioinformatics approach /." Philadelphia, Pa. : Drexel University, 2005. http://dspace.library.drexel.edu/handle/1860/481.

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Rho, Mina. "Probabilistic models in computational molecular biology applied to the identification of mobile genetic elements and gene finding." [Bloomington, Ind.] : Indiana University, 2009. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3386714.

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Thesis (Ph.D.)--Indiana University, School of Informatics and Computing, 2009.<br>Title from PDF t.p. (viewed on Jul 22, 2010). Source: Dissertation Abstracts International, Volume: 70-12, Section: B, page: 7299. Adviser: Haixu Tang.

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Ma, Siming. "Molecular Patterns and Signatures of Longevity." Thesis, Harvard University, 2016. http://nrs.harvard.edu/urn-3:HUL.InstRepos:33493444.

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Since their divergence from a common ancestor some 200 million years ago, mammals have undergone significant diversification in physiology, morphology, habitat, size, and longevity. The maximum lifespan of mammalian species ranges from under 3 to over 200 years, but the molecular basis of such variation is poorly understood. While many genes, pathways, dietary interventions, and pharmacological compounds have been shown to influence the lifespan of model organisms, it is not known whether the same mechanisms are responsible for the longevity variation across different species. This thesis presents the analyses of gene expression and the levels of metabolites, chemical elements, and/or proteins, across multiple organs and tissues of up to 42 species of mammals, as well as the analyses of 5 long-lived mouse models, 22 natural isolates of yeast, and 16 species of fruit flies, to identify the molecular patterns and signatures associated with species longevity. The results show that longer-lived mammals up-regulate ribosomal proteins and genes involved in DNA repair, and down-regulate ubiquitin-mediated proteolysis and apoptotic functions. Some of the metabolic changes in long-lived mammals, such as higher levels of sphingomyelins and glycerophospholipids but lower levels of polyunsaturated triacylglycerols, were also observed in long-lived mouse models. Yeast strains of varying replicative lifespan differed in their aerobic respiration capacity, attributable to different protein composition in mitochondria. Long-lived fruit flies overexpressed the genes involved in lipid metabolism but suppressed the genes involved in neuronal development. Many genes previously implicated in lifespan control in model organisms also showed the expected correlation with the longevity traits across species. This thesis presents the snapshots of the complex changes associated with species natural lifespan variation and offers new insights into the mechanisms of longevity control and potential lifespan extension strategies.<br>Medical Sciences

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Chaivorapol, Christina. "Systematic identification of regulatory pathways in human and mouse embryonic stem cells and other mammalian systems." Diss., Search in ProQuest Dissertations & Theses. UC Only, 2008. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3328095.

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Books on the topic "Bioinformatics Molecular biology Bioinformatics"

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Structural bioinformatics. 2nd ed. Wiley-Blackwell, 2009.

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Attwood, Teresa K. Introduction to bioinformatics. Longman, 1999.

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Narayanan, P. Bioinformatics: A primer. New Age International (P) Ltd., Publishers, 2005.

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Molecular bioinformatics: Algorithms and applications. Walter de Gruyter, 1996.

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Seifert, D. Bioinformatics methods and protocols. Humana, 2010.

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Bioinformatics for comparative proteomics. Humana Press, 2011.

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Bioinformatics and functional genomics. Wiley-Liss, 2004.

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Pevsner, Jonathan. Bioinformatics and Functional Genomics. John Wiley & Sons, Ltd., 2005.

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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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Wang, Xiangdong. Bioinformatics of Human Proteomics. Springer Netherlands, 2013.

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Book chapters on the topic "Bioinformatics Molecular biology Bioinformatics"

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Juan, Hsueh-Fen, and Hsuan-Cheng Huang. "Bioinformatics." In Methods in Molecular Biology. Humana Press, 2007. http://dx.doi.org/10.1007/978-1-59745-304-2_25.

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Loewe, Robert P., and Peter J. Nelson. "Microarray Bioinformatics." In Methods in Molecular Biology. Humana Press, 2010. http://dx.doi.org/10.1007/978-1-59745-551-0_18.

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Ayyildiz, Dilara, and Silvano Piazza. "Introduction to Bioinformatics." In Methods in Molecular Biology. Springer New York, 2019. http://dx.doi.org/10.1007/978-1-4939-9442-7_1.

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Sintchenko, Vitali, and Michael P. V. Roper. "Pathogen Genome Bioinformatics." In Methods in Molecular Biology. Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-0847-9_10.

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Reiche, Kristin, Katharina Schutt, Kerstin Boll, Friedemann Horn, and Jörg Hackermüller. "Bioinformatics for RNomics." In Methods in Molecular Biology. Humana Press, 2011. http://dx.doi.org/10.1007/978-1-61779-027-0_14.

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Chacko, Elsa, and Shoba Ranganathan. "Graphs in Bioinformatics." In Algorithms in Computational Molecular Biology. John Wiley & Sons, Inc., 2010. http://dx.doi.org/10.1002/9780470892107.ch10.

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Eichler, Gabriel S. "Bioinformatics/Biostatistics: Microarray Analysis." In Methods in Molecular Biology. Humana Press, 2011. http://dx.doi.org/10.1007/978-1-60327-216-2_22.

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Tafer, Hakim. "Bioinformatics of siRNA Design." In Methods in Molecular Biology. Humana Press, 2013. http://dx.doi.org/10.1007/978-1-62703-709-9_22.

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Picardi, Ernesto. "Correction to: RNA Bioinformatics." In Methods in Molecular Biology. Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-1307-8_32.

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Han, Gye Won, Chris Rife, and Michael R. Sawaya. "Applications of Bioinformatics to Protein Structures: How Protein Structure and Bioinformatics Overlap." In Methods in Molecular Biology. Humana Press, 2009. http://dx.doi.org/10.1007/978-1-59745-524-4_8.

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Conference papers on the topic "Bioinformatics Molecular biology Bioinformatics"

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Pevzner, Pavel. "Bioinformatics: a Servant or the Queen of Molecular Biology?" In 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2019. http://dx.doi.org/10.1109/bibm47256.2019.8983148.

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"Molecular dynamics modeling of multicolor FRET-experiment." In SYSTEMS BIOLOGY AND BIOINFORMATICS. Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, 2019. http://dx.doi.org/10.18699/sbb-2019-02.

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Tverdislov, V. A., and E. V. Malyshko. "Chiral dualism, arrow of symmetry and molecular machines." In Mathematical Biology and Bioinformatics. IMPB RAS - Branch of KIAM RAS, 2018. http://dx.doi.org/10.17537/icmbb18.97.

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Zaytsev, A. Y., M. M. Olshevets, N. S. Fialko, V. V. Yakovlev, and V. D. Lakhno. "Artificial Normalization in the Modeling of Charge Transfer in Molecular Chains." In Mathematical Biology and Bioinformatics. IMPB RAS - Branch of KIAM RAS, 2020. http://dx.doi.org/10.17537/icmbb20.8.

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"Molecular processes in Solanum phureja roots in response to Globodera rostochiensis infection." In SYSTEMS BIOLOGY AND BIOINFORMATICS. Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, 2019. http://dx.doi.org/10.18699/sbb-2019-10.

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Likhachev, I. V., and N. K. Balabaev. "Parallelism of different levels in the program of molecular dynamics simulation PUMA-CUDA." In Mathematical Biology and Bioinformatics. IMPB RAS - Branch of KIAM RAS, 2018. http://dx.doi.org/10.17537/icmbb18.53.

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Riznichenko, G. Yu, A. N. Diakonova, I. B. Kovalenko, T. Yu Plyusnina, S. S. Khruschev, and V. A. Fyodorov. "Models of cellular and molecular regulation of the photosynthetic chain of hydrogen-producing microalgae." In Mathematical Biology and Bioinformatics. IMPB RAS - Branch of KIAM RAS, 2018. http://dx.doi.org/10.17537/icmbb18.25.

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Avakyan, L. A., A. S. Manukyan, E. V. Paramonova, et al. "Reactive force-field molecular dynamic models of iron/oxide/carbon nanocomposites designed for magnetic hyperthermia." In Mathematical Biology and Bioinformatics. IMPB RAS - Branch of KIAM RAS, 2018. http://dx.doi.org/10.17537/icmbb18.42.

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Nikolaev, G. I., N. A. Shuldov, I. P. Bosko, A. I. Anischenko, A. V. Tuzikov, and A. M. Andrianov. "Application of Deep Learning and Molecular Modelling Methods to Identify Potential HIV-1 Entry Inhibitors." In Mathematical Biology and Bioinformatics. IMPB RAS - Branch of KIAM RAS, 2020. http://dx.doi.org/10.17537/icmbb20.6.

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Filippov, S. V. "Methods of working with dynamic molecular models, built in an environment of open 3D editor Blender." In Mathematical Biology and Bioinformatics. IMPB RAS - Branch of KIAM RAS, 2018. http://dx.doi.org/10.17537/icmbb18.62.

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Reports on the topic "Bioinformatics Molecular biology Bioinformatics"

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Chakraborty, Srijani. Promises and Challenges of Systems Biology. Nature Library, 2020. http://dx.doi.org/10.47496/nl.blog.09.

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Modern systems biology is essentially interdisciplinary, tying molecular biology, the omics, bioinformatics and non-biological disciplines like computer science, engineering, physics, and mathematics together.

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Wallace, Susan S. DOE EPSCoR Initiative in Structural and computational Biology/Bioinformatics. Office of Scientific and Technical Information (OSTI), 2008. http://dx.doi.org/10.2172/924036.

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Kamalakaran, Sitharthan, and Josh Dubnau. A Strategy to Rapidly Re-Sequence the NF1 Genomic Loci Using Microarrays and Bioinformatics for Molecular Classification of the Disease. Defense Technical Information Center, 2006. http://dx.doi.org/10.21236/ada478099.

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