Littérature scientifique sur le sujet « Computational Genomic »
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Articles de revues sur le sujet "Computational Genomic"
Nalbantoglu, Ozkan Ufuk, et Khalid Sayood. « Computational Genomic Signatures ». Synthesis Lectures on Biomedical Engineering 6, no 2 (31 mai 2011) : 1–129. http://dx.doi.org/10.2200/s00360ed1v01y201105bme041.
Texte intégralYelick, Katherine, Aydın Buluç, Muaaz Awan, Ariful Azad, Benjamin Brock, Rob Egan, Saliya Ekanayake et al. « The parallelism motifs of genomic data analysis ». Philosophical Transactions of the Royal Society A : Mathematical, Physical and Engineering Sciences 378, no 2166 (20 janvier 2020) : 20190394. http://dx.doi.org/10.1098/rsta.2019.0394.
Texte intégralHien, Le Thi Thu, Nguyen Tuong Van, Kim Thi Phuong Oanh, Nguyen Dang Ton, Huynh Thi Thu Hue, Nguyen Thuy Duong, Pham Le Bich Hang et Nguyen Hai Ha. « Genomics and big data : Research, development and applications ». Vietnam Journal of Biotechnology 19, no 3 (13 octobre 2021) : 393–410. http://dx.doi.org/10.15625/1811-4989/16158.
Texte intégralLu, Bingxin, et Hon Wai Leong. « Computational methods for predicting genomic islands in microbial genomes ». Computational and Structural Biotechnology Journal 14 (2016) : 200–206. http://dx.doi.org/10.1016/j.csbj.2016.05.001.
Texte intégralSalamon, Hugh, Midori Kato-Maeda, Peter M. Small, Jorg Drenkow et Thomas R. Gingeras. « Detection of Deleted Genomic DNA Using a Semiautomated Computational Analysis of GeneChip Data ». Genome Research 10, no 12 (21 novembre 2000) : 2044–54. http://dx.doi.org/10.1101/gr.152900.
Texte intégralZUO, GuangHong, et BaiLin HAO. « Computational microbiology in genomic era ». SCIENTIA SINICA Vitae 47, no 2 (22 janvier 2017) : 159–70. http://dx.doi.org/10.1360/n052016-00312.
Texte intégralLe, Vinh. « A computational framework to analyze human genomes ». Journal of Computer Science and Cybernetics 35, no 2 (3 juin 2019) : 105–18. http://dx.doi.org/10.15625/1813-9663/35/2/13827.
Texte intégralCui, Zhe, Jayaram Kancherla, Kyle W. Chang, Niklas Elmqvist et Héctor Corrada Bravo. « Proactive visual and statistical analysis of genomic data in Epiviz ». Bioinformatics 36, no 7 (29 novembre 2019) : 2195–201. http://dx.doi.org/10.1093/bioinformatics/btz883.
Texte intégralWan, Peng, et Dongsheng Che. « A Computational Framework for Tracing the Origins of Genomic Islands in Prokaryotes ». International Scholarly Research Notices 2014 (28 octobre 2014) : 1–9. http://dx.doi.org/10.1155/2014/732857.
Texte intégralChorbadjiev, Lubomir, Jude Kendall, Joan Alexander, Viacheslav Zhygulin, Junyan Song, Michael Wigler et Alexander Krasnitz. « Integrated Computational Pipeline for Single-Cell Genomic Profiling ». JCO Clinical Cancer Informatics, no 4 (septembre 2020) : 464–71. http://dx.doi.org/10.1200/cci.19.00171.
Texte intégralThèses sur le sujet "Computational Genomic"
Mumey, Brendan Marshall. « Some computational problems from genomic mapping / ». Thesis, Connect to this title online ; UW restricted, 1997. http://hdl.handle.net/1773/6932.
Texte intégralAlkan, Can. « Computational Studies on Evolution and Functionality of Genomic Repeats ». Case Western Reserve University School of Graduate Studies / OhioLINK, 2005. http://rave.ohiolink.edu/etdc/view?acc_num=case1120143436.
Texte intégralGaspar, Paulo Miguel da Silva. « Computational methods for gene characterization and genomic knowledge extraction ». Doctoral thesis, Universidade de Aveiro, 2014. http://hdl.handle.net/10773/13949.
Texte intégralMotivation: Medicine and health sciences are changing from the classical symptom-based to a more personalized and genetics-based paradigm, with an invaluable impact in health-care. While advancements in genetics were already contributing significantly to the knowledge of the human organism, the breakthrough achieved by several recent initiatives provided a comprehensive characterization of the human genetic differences, paving the way for a new era of medical diagnosis and personalized medicine. Data generated from these and posterior experiments are now becoming available, but its volume is now well over the humanly feasible to explore. It is then the responsibility of computer scientists to create the means for extracting the information and knowledge contained in that data. Within the available data, genetic structures contain significant amounts of encoded information that has been uncovered in the past decades. Finding, reading and interpreting that information are necessary steps for building computational models of genetic entities, organisms and diseases; a goal that in due course leads to human benefits. Aims: Numerous patterns can be found within the human variome and exome. Exploring these patterns enables the computational analysis and manipulation of digital genomic data, but requires specialized algorithmic approaches. In this work we sought to create and explore efficient methodologies to computationally calculate and combine known biological patterns for various purposes, such as the in silico optimization of genetic structures, analysis of human genes, and prediction of pathogenicity from human genetic variants. Results: We devised several computational strategies to evaluate genes, explore genomes, manipulate sequences, and analyze patients’ variomes. By resorting to combinatorial and optimization techniques we were able to create and combine sequence redesign algorithms to control genetic structures; by combining the access to several web-services and external resources we created tools to explore and analyze available genetic data and patient data; and by using machine learning we developed a workflow for analyzing human mutations and predicting their pathogenicity.
Motivação: A medicina e as ciências da saúde estão atualmente num processo de alteração que muda o paradigma clássico baseado em sintomas para um personalizado e baseado na genética. O valor do impacto desta mudança nos cuidados da saúde é inestimável. Não obstante as contribuições dos avanços na genética para o conhecimento do organismo humano até agora, as descobertas realizadas recentemente por algumas iniciativas forneceram uma caracterização detalhada das diferenças genéticas humanas, abrindo o caminho a uma nova era de diagnóstico médico e medicina personalizada. Os dados gerados por estas e outras iniciativas estão disponíveis mas o seu volume está muito para lá do humanamente explorável, e é portanto da responsabilidade dos cientistas informáticos criar os meios para extrair a informação e conhecimento contidos nesses dados. Dentro dos dados disponíveis estão estruturas genéticas que contêm uma quantidade significativa de informação codificada que tem vindo a ser descoberta nas últimas décadas. Encontrar, ler e interpretar essa informação são passos necessários para construir modelos computacionais de entidades genéticas, organismos e doenças; uma meta que, em devido tempo, leva a benefícios humanos. Objetivos: É possível encontrar vários padrões no varioma e exoma humano. Explorar estes padrões permite a análise e manipulação computacional de dados genéticos digitais, mas requer algoritmos especializados. Neste trabalho procurámos criar e explorar metodologias eficientes para o cálculo e combinação de padrões biológicos conhecidos, com a intenção de realizar otimizações in silico de estruturas genéticas, análises de genes humanos, e previsão da patogenicidade a partir de diferenças genéticas humanas. Resultados: Concebemos várias estratégias computacionais para avaliar genes, explorar genomas, manipular sequências, e analisar o varioma de pacientes. Recorrendo a técnicas combinatórias e de otimização criámos e conjugámos algoritmos de redesenho de sequências para controlar estruturas genéticas; através da combinação do acesso a vários web-services e recursos externos criámos ferramentas para explorar e analisar dados genéticos, incluindo dados de pacientes; e através da aprendizagem automática desenvolvemos um procedimento para analisar mutações humanas e prever a sua patogenicidade.
SINHA, AMIT U. « Discovery and Analysis of Genomic Patterns : Applications to Transcription Factor Binding and Genome Rearrangement ». University of Cincinnati / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1204227723.
Texte intégralSaha, Mandal Arnab. « Computational Analysis of the Evolution of Non-Coding Genomic Sequences ». University of Toledo Health Science Campus / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=mco1372349811.
Texte intégralDanks, Jacob R. « Algorithm Optimizations in Genomic Analysis Using Entropic Dissection ». Thesis, University of North Texas, 2015. https://digital.library.unt.edu/ark:/67531/metadc804921/.
Texte intégralCICCOLELLA, SIMONE. « Practical algorithms for Computational Phylogenetics ». Doctoral thesis, Università degli Studi di Milano-Bicocca, 2022. http://hdl.handle.net/10281/364980.
Texte intégralIn this manuscript we described the main computational challenges of the cancer phylogenetic field and we proposed different solutions for the three main problems of (i) the progression reconstruction of a tumor sample, (ii) the clustering of SCS data to allow for a cleaner and faster inference and (iii) the evaluation of different phylogenies. Furthermore we combined them into a usable pipeline to allow for a faster analysis.
Picard, Colette Lafontaine. « Dynamics of DNA methylation and genomic imprinting in arabidopsis ». Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/122539.
Texte intégralCataloged from PDF version of thesis.
Includes bibliographical references (pages 210-226).
DNA methylation is an epigenetic mark that is highly conserved and important in diverse cellular processes, ranging from transposon silencing to genomic imprinting. In plants, DNA methylation is both mitotically and meiotically heritable, and changes in DNA methylation can be generationally stable and have long-lasting consequences. This thesis aims to improve understanding of DNA methylation dynamics in plants, particularly across generations and during reproduction. In the first project, I present an analysis of the generational dynamics of gene body methylation using recombinant inbred lines derived from differentially methylated parents. I show that while gene body methylation is highly generationally stable, changes in methylation state occur nonrandomly and are enriched in regions of intermediate methylation.
Important DNA methylation changes also occur during seed development in flowering plants, and these changes underlie genomic imprinting, the phenomenon of parent-of-origin specific gene expression. In plants, imprinting occurs in the endosperm, a seed tissue that functions analogously to the mammalian placenta. Imprinted expression is linked to DNA methylation patterns that serve to differentiate the maternally- and paternally-inherited alleles, but the mechanisms used to achieve imprinted expression are often unknown. I next explore imprinted expression and DNA methylation in Arabidopsis lyrata, a close relative of the model plant Arabidopsis thaliana. I find that the majority of imprinted genes in A. lyrata endosperm are also imprinted in A. thaliana, suggesting that imprinted expression is generally conserved. Surprisingly, a subset of A. lyrata imprinted genes are associated with a novel DNA methylation pattern and may be regulated by a different mechanism than their A.
thaliana counterparts. I then explore the genetics of paternal suppression of the seed abortion phenotype caused by mutation of a maternally expressed imprinted gene. Finally, I present the first large single-nuclei RNA-seq dataset generated in plants, reporting data from 1,093 individual nuclei obtained from developing seeds. I find evidence of previously uncharacterized cell states in endosperm, and examine imprinted expression at the single-cell level. Together, these projects contribute to our understanding of DNA methylation and imprinting dynamics during plant development, and highlight the strong generational stability of certain DNA methylation patterns.
by Colette Lafontaine Picard.
Ph. D.
Ph.D. Massachusetts Institute of Technology, Computational and Systems Biology Program
Rezwan, Faisal Ibne. « Improving computational predictions of Cis-regulatory binding sites in genomic data ». Thesis, University of Hertfordshire, 2011. http://hdl.handle.net/2299/7133.
Texte intégralAlkhnbashi, Omer S. [Verfasser], et Rolf [Akademischer Betreuer] Backofen. « Computational characterisation of genomic CRISPR-Cas systems in archaea and bacteria ». Freiburg : Universität, 2017. http://d-nb.info/1139210904/34.
Texte intégralLivres sur le sujet "Computational Genomic"
Nalbantoglu, Ozkan Ufuk, et Khalid Sayood. Computational Genomic Signatures. Cham : Springer International Publishing, 2011. http://dx.doi.org/10.1007/978-3-031-01650-9.
Texte intégralDassanayake, Ranil S. Genomic and proteomic techniques : In post genomics era. Oxford : Alpha Science International, 2011.
Trouver le texte intégralBioinformatics in the post-genomic era : Genome, transcriptome, proteome, and information-based medicine. Boston : Addison-Wesley, 2005.
Trouver le texte intégralMasood, Nosheen, et Saima Shakil Malik, dir. 'Essentials of Cancer Genomic, Computational Approaches and Precision Medicine. Singapore : Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1067-0.
Texte intégralInc, ebrary, dir. Advances in genomic sequence analysis and pattern discovery. Hackensack, N.J : World Scientific, 2011.
Trouver le texte intégralDennis, Wigle, Jurisica Igor et Wong Bill, dir. Cancer informatics in the post genomic era. New York : Springer, 2007.
Trouver le texte intégralDwyer, Rex A. Genomic Perl : From bioinformatics basics to working code. Cambridge : Cambridge University Press, 2003.
Trouver le texte intégralY, Galperin Michael, et Koonin Eugene V, dir. Frontiers in computational genomics. Norfolk, U.K : Caister, 2003.
Trouver le texte intégralShugart, Yin Yao, dir. Applied Computational Genomics. Dordrecht : Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-5558-1.
Texte intégralYao, Yin, dir. Applied Computational Genomics. Singapore : Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1071-3.
Texte intégralChapitres de livres sur le sujet "Computational Genomic"
Nalbantoglu, Ozkan Ufuk, et Khalid Sayood. « Applications : Phylogeny Construction ». Dans Computational Genomic Signatures, 49–60. Cham : Springer International Publishing, 2011. http://dx.doi.org/10.1007/978-3-031-01650-9_4.
Texte intégralWong, Bill, et Igor Jurisica. « Computational Platforms ». Dans Cancer Informatics in the Post Genomic Era, 85–86. Boston, MA : Springer US, 2007. http://dx.doi.org/10.1007/978-0-387-69321-7_6.
Texte intégralZhang, Jie. « Biostatistics, Data Mining and Computational Modeling ». Dans Genomic Approach to Asthma, 339–47. Singapore : Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8764-6_15.
Texte intégralRitter, Otto. « The Integrated Genomic Database (IGD) ». Dans Computational Methods in Genome Research, 57–73. Boston, MA : Springer US, 1994. http://dx.doi.org/10.1007/978-1-4615-2451-9_5.
Texte intégralJurisica, Igor. « Integrative Computational Biology ». Dans Cancer Informatics in the Post Genomic Era, 129–45. Boston, MA : Springer US, 2007. http://dx.doi.org/10.1007/978-0-387-69321-7_8.
Texte intégralSharma, Abhishek, et Ashok Kumar. « Genomics, Transcriptomics and Proteomics for Computational Biology ». Dans Genomic, Proteomics, and Biotechnology, 1–11. Boca Raton : CRC Press, 2022. http://dx.doi.org/10.1201/9781003220831-1.
Texte intégralFertin, Guillaume, et Irena Rusu. « Computing Genomic Distances : An Algorithmic Viewpoint ». Dans Algorithms in Computational Molecular Biology, 773–97. Hoboken, NJ, USA : John Wiley & Sons, Inc., 2010. http://dx.doi.org/10.1002/9780470892107.ch34.
Texte intégralDuitama, Jorge. « Genomic Variants Detection and Genotyping ». Dans Computational Methods for Next Generation Sequencing Data Analysis, 133–47. Hoboken, NJ, USA : John Wiley & Sons, Inc., 2016. http://dx.doi.org/10.1002/9781119272182.ch6.
Texte intégralZanetti, João Paulo Pereira, Leonid Chindelevitch et João Meidanis. « Generalizations of the Genomic Rank Distance to Indels ». Dans Algorithms for Computational Biology, 152–64. Cham : Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-18174-1_11.
Texte intégralTapia, José Juan, Enrique Morett et Edgar E. Vallejo. « A Clustering Genetic Algorithm for Genomic Data Mining ». Dans Studies in Computational Intelligence, 249–75. Berlin, Heidelberg : Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01088-0_11.
Texte intégralActes de conférences sur le sujet "Computational Genomic"
Kanwal, Sehrish, Andrew Lonie et Richard O. Sinnott. « Digital reproducibility requirements of computational genomic workflows ». Dans 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2017. http://dx.doi.org/10.1109/bibm.2017.8217887.
Texte intégral« Computational Solutions to Explore Genomic 3D Organization ». Dans 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2022. http://dx.doi.org/10.1109/bibm55620.2022.9995500.
Texte intégralNgo, Mathias, et Raphael Labayrade. « Multi-Genomic Algorithms ». Dans 2014 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making (MCDM). IEEE, 2014. http://dx.doi.org/10.1109/mcdm.2014.7007187.
Texte intégralCannataro, Mario. « Session details : Genomic variation ». Dans BCB '21 : 12th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics. New York, NY, USA : ACM, 2021. http://dx.doi.org/10.1145/3478666.
Texte intégralNabavi, Sheida. « Session details : Genomic variation ». Dans BCB '22 : 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics. New York, NY, USA : ACM, 2022. http://dx.doi.org/10.1145/3552480.
Texte intégral« Computational pipeline for genomic epidemiology surveillance of pathogenic bacteria ». Dans Bioinformatics of Genome Regulation and Structure/ Systems Biology. institute of cytology and genetics siberian branch of the russian academy of science, Novosibirsk State University, 2020. http://dx.doi.org/10.18699/bgrs/sb-2020-068.
Texte intégralOcchipinti, Annalisa, et Claudio Angione. « A Computational Model of Cancer Metabolism for Personalised Medicine ». Dans Building Bridges in Medical Science 2021. Cambridge Medicine Journal, 2021. http://dx.doi.org/10.7244/cmj.2021.03.001.3.
Texte intégralSchwartz, Russell. « Computationally resolving heterogeneity in mixed genomic samples ». Dans 2016 IEEE 6th International Conference on Computational Advances in Bio and Medical Sciences (ICCABS). IEEE, 2016. http://dx.doi.org/10.1109/iccabs.2016.7802796.
Texte intégralYue, Dong, Yidong Chen, Shou-Jiang Gao et Yufei Huang. « Computational prediction of microRNA regulatory pathways ». Dans 2011 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS). IEEE, 2011. http://dx.doi.org/10.1109/gensips.2011.6169469.
Texte intégralKim, Sungeun, Li Shen, Andrew J. Saykin et John D. West. « Data synthesis and tool development for exploring imaging genomic patterns ». Dans 2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB). IEEE, 2009. http://dx.doi.org/10.1109/cibcb.2009.4925742.
Texte intégralRapports d'organisations sur le sujet "Computational Genomic"
Stevens, Rick. Development of an Extensible Computational Framework for Centralized Storage and Distributed Curation and Analysis of Genomic Data Genome-scale Metabolic Models. Office of Scientific and Technical Information (OSTI), août 2010. http://dx.doi.org/10.2172/1234257.
Texte intégralFluhr, Robert, et Volker Brendel. Harnessing the genetic diversity engendered by alternative gene splicing. United States Department of Agriculture, décembre 2005. http://dx.doi.org/10.32747/2005.7696517.bard.
Texte intégralFridman, Eyal, Jianming Yu et Rivka Elbaum. Combining diversity within Sorghum bicolor for genomic and fine mapping of intra-allelic interactions underlying heterosis. United States Department of Agriculture, janvier 2012. http://dx.doi.org/10.32747/2012.7597925.bard.
Texte intégralEdwards, Jeremy, S. Metabolic engineering of deinococcus radiodurans based on computational analysis and functional genomics. Office of Scientific and Technical Information (OSTI), février 2005. http://dx.doi.org/10.2172/836597.
Texte intégralEbrahim, Ali. Development and Dissemination of Computational Methods for Genome-scale Modeling. Office of Scientific and Technical Information (OSTI), janvier 2016. http://dx.doi.org/10.2172/1468961.
Texte intégralOvcharenko, I. FY06 LDRD Final Report "Development of Computational Techniques For Decoding The Language of Genomes". Office of Scientific and Technical Information (OSTI), janvier 2007. http://dx.doi.org/10.2172/899447.
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