Auswahl der wissenschaftlichen Literatur zum Thema „Engineering, Computer|Biology, Bioinformatics|Computer Science“
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Zeitschriftenartikel zum Thema "Engineering, Computer|Biology, Bioinformatics|Computer Science":
Paraskevopoulou-Kollia, Efrosyni-Alkisti, und Pantelis G. Bagos. „Bioinformatics Education in Greece: A Survey“. Biosaintifika: Journal of Biology & Biology Education 9, Nr. 1 (12.03.2017): 1. http://dx.doi.org/10.15294/biosaintifika.v9i1.7257.
ADEBO, PHILIP. „A PRIMER ON BIOINFORMATICS“. International Journal of Advanced Research in Computer Science and Software Engineering 8, Nr. 4 (01.05.2018): 9. http://dx.doi.org/10.23956/ijarcsse.v8i4.589.
Doom, T., M. Raymer, D. Krane und O. Garcia. „Crossing the interdisciplinary barrier: a baccalaureate computer science option in bioinformatics“. IEEE Transactions on Education 46, Nr. 3 (August 2003): 387–93. http://dx.doi.org/10.1109/te.2003.814593.
Heinemann, M., und S. Panke. „Synthetic biology--putting engineering into biology“. Bioinformatics 22, Nr. 22 (05.09.2006): 2790–99. http://dx.doi.org/10.1093/bioinformatics/btl469.
Kovats, Diane, Ron Shamir und Christiana Fogg. „Bonnie Berger named ISCB 2019 ISCB Accomplishments by a Senior Scientist Award recipient“. F1000Research 8 (23.05.2019): 721. http://dx.doi.org/10.12688/f1000research.19219.1.
Linshiz, Gregory, Alex Goldberg, Tania Konry und Nathan J. Hillson. „The Fusion of Biology, Computer Science, and Engineering: Towards Efficient and Successful Synthetic Biology“. Perspectives in Biology and Medicine 55, Nr. 4 (2012): 503–20. http://dx.doi.org/10.1353/pbm.2012.0044.
Likić, Vladimir A., Malcolm J. McConville, Trevor Lithgow und Antony Bacic. „Systems Biology: The Next Frontier for Bioinformatics“. Advances in Bioinformatics 2010 (09.02.2010): 1–10. http://dx.doi.org/10.1155/2010/268925.
Shegogue, Daniel, und W. Jim Zheng. „Object-oriented biological system integration: a SARS coronavirus example“. Bioinformatics 21, Nr. 10 (24.02.2005): 2502–9. http://dx.doi.org/10.1093/bioinformatics/bti344.
Miller, W., S. Schwartz und R. C. Hardison. „A point of contact between computer science and molecular biology“. IEEE Computational Science and Engineering 1, Nr. 1 (1994): 69–78. http://dx.doi.org/10.1109/99.295375.
Tadmor, Brigitta, und Bruce Tidor. „Interdisciplinary research and education at the biology–engineering–computer science interface: a perspective“. Drug Discovery Today 10, Nr. 17 (September 2005): 1183–89. http://dx.doi.org/10.1016/s1359-6446(05)03540-3.
Dissertationen zum Thema "Engineering, Computer|Biology, Bioinformatics|Computer Science":
Dinh, Hieu Trung. „Algorithms for DNA Sequence Assembly and Motif Search“. University of Connecticut, 2013.
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.
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.
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 & 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.
Ren, Kaiyu. „Mapping biomedical terms to UMLS concepts by an efficient layered dynamic programming framework“. The Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1398886613.
Sanga, Sandeep. „A Computational Systems Biology Approach to Predictive Oncology| A Computer Modeling and Bioinformatics Study Predicting Tumor Response to Therapy and Cancer Phenotypes“. Thesis, The University of Texas at Austin, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3684162.
Technological advances in the recent decades have enabled cancer researchers to probe the disease at multiple resolutions. This wealth of experimental data combined with computational systems biology methods is now leading to predictive models of cancer progression and response to therapy. We begin by presenting our research group's multi-scale in silico framework for modeling cancer, whose core is a tissue-scale computational model capable of tracking the progression of tumors from a diffusion-limited avascular phase through angiogenesis, and into invasive lesions with realistic, complex morphologies. We adapt this core model to consider the delivery of systemically-administered anticancer agents and their effect on lesions once they reach their intended nuclear target. We calibrate the model parameters using in vitro data from the literature, and demonstrate through simulation that transport limitations affecting drug and oxygen distributions play a significant role in hampering the efficacy of chemotherapy; a result that has since been validated by in vitro experimentation. While this study demonstrates the capability of our adapted core model to predict distributions (e.g., cell density, pressure, oxygen, nutrient, drug) within lesions and consequent tumor morphology, nevertheless, the underlying factors driving tumor-scale behavior occur at finer scales. What is needed in our multi-scale approach is to parallel reality, where molecular signaling models predict cellular behavior, and ultimately drive what is seen at the tumor level. Models of signaling pathways linked to cell models are already beginning to surface in the literature. We next transition our research to the molecular level, where we employ data mining and bioinformatics methods to infer signaling relationships underlying a subset of breast cancer that might benefit from targeted therapy of Androgen Receptor and associated pathways. Defining the architecture of signaling pathways is a critical first step towards development of pathways models underlying tumor models, while also providing valuable insight for drug discovery. Finally, we develop an agent-based, cell-scale model focused on predicting motility in response to chemical signals in the microenvironment, generally accepted to be a necessary feature of cancer invasion and metastasis. This research demonstrates the use of signaling models to predict emergent cell behavior, such as motility. The research studies presented in this dissertation are critical steps towards developing a predictive, in silico computational model for cancer progression and response to therapy. Our Laboratory for Computational & Predictive Oncology, in collaboration with research groups throughout in the United States and Europe are following a computational systems biology paradigm where model development is fueled by biological knowledge, and model predictions are refining experimental focus. The ultimate objective is a virtual cancer simulator capable of accurately simulating cancer progression and response to therapy on a patient-specific basis.
Berry, Eric Zachary 1980. „Bioinformatics and database tools for glycans“. Thesis, Massachusetts Institute of Technology, 2004. http://hdl.handle.net/1721.1/27085.
Includes bibliographical references (leaves 75-76).
Recent advances in biology have afforded scientists with the knowledge that polysaccharides play an active role in modulating cellular activities. Glycosaminoglycans (GAGs) are one such family of polysaccharides that play a very important role in regulating the functions of numerous important signaling molecules and enzymes in the cell. Developing bioinformatics tools has been integral to advancing genomics and proteomics. While these tools have been well-developed to store and process sequence and structure information for proteins and DNA, they are very poorly developed for polysaccharides. Glycan structures pose special problems because of their tremendous information density per fundamental unit, their often-branched structures, and the complicated nature of their building blocks. The GlycoBank, an online database of known GAG structures and functions, has been developed to overcome many of these difficulties by developing a common notation for researchers to describe GAG sequences, a common repository to view known structure-function relationships, and the complex tools and searches needed to facilitate their work. This thesis focuses on the development of GlycoBank. In addition, a large, NIGMS-funded consortium, the Consortium for Functional Glycomics, is a larger database that also aims to store polysaccharide structure-function information of a broader collection of polysaccharides. The ideas and concepts implemented in developing GlycoBank were instrumental in developing databases and bioinformatics tools for the Consortium for Functional Glycomics.
by Eric Zachary Berry.
M.Eng.and S.B.
Guo, Xinyu. „Design of A Systolic Array-Based FPGA Parallel Architecture for the BLAST Algorithm and Its Implementation“. University of Toledo / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1338478834.
Kho, Soon Jye. „Sample Mislabeling Detection and Correction in Bioinformatics Experimental Data“. Wright State University / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=wright1629736147173188.
Bozdag, Doruk. „Graph Coloring and Clustering Algorithms for Science and Engineering Applications“. The Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=osu1229459765.
Kalluru, Vikram Gajanan. „Identify Condition Specific Gene Co-expression Networks“. The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1338304258.
Zhong, Cuncong. „Computational Methods for Comparative Non-coding RNA Analysis: From Structural Motif Identification to Genome-wide Functional Classification“. Doctoral diss., University of Central Florida, 2013. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/5894.
Ph.D.
Doctorate
Computer Science
Engineering and Computer Science
Computer Science
Bücher zum Thema "Engineering, Computer|Biology, Bioinformatics|Computer Science":
Gavrilova, Marina L. Transactions on Computational Science IX: Special Issue on Voronoi Diagrams in Science and Engineering. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2010.
International Symposium on Frontiers of Computational Science (2005 Nagoya, Japan). Frontiers of computational science: Proceedings of the International Symposium on Frontiers of Computational Science 2005. Berlin: Springer, 2007.
Li, Kang. Life System Modeling and Intelligent Computing: International Conference on Life System Modeling and Simulation, LSMS 2010, and International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2010, Wuxi, China, September 17-20, 2010, Proceedings, Part II. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010.
Zhang, Yongjie (Jessica). Image-Based Geometric Modeling and Mesh Generation. Dordrecht: Springer Netherlands, 2013.
O'Connor, Rory V. Software Process Improvement and Capability Determination: 11th International Conference, SPICE 2011, Dublin, Ireland, May 30 – June 1, 2011. Proceedings. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011.
Perelman, Yevgeny. The NeuroProcessor: An Integrated Interface to Biological Neural Networks. Dordrecht: Springer Science+Business Media B.V., 2008.
Konagaya, Akihiko. Grid Computing in Life Science: First International Workshop on Life Science Grid, LSGRID 2004 Kanazawa, Japan, May 31-June 1, 2004, Revised Selected and Invited Papers. Berlin: Springer, 2005.
International ICST Conference on Nano-Networks. Nano-Net: 4th international ICST conference, Nano-Net 2009, Lucerne, Switzerland, October 18-20, 2009, proceedings. Berlin: Springer, 2009.
Álvarez, José R. Artificial Intelligence and Knowledge Engineering Applications: A Bioinspired Approach. Berlin Heidelberg: Springer-Verlag., 2005.
Ganguly, Niloy. Dynamics On and Of Complex Networks: Applications to Biology, Computer Science, and the Social Sciences. Boston: Birkhäuser Boston, 2009.
Buchteile zum Thema "Engineering, Computer|Biology, Bioinformatics|Computer Science":
Molina-Recio, Guillermo, Laura García-Hernández, Antonio Castilla-Melero, Juan M. Palomo-Romero, Rafael Molina-Luque, Antonio A. Sánchez-Muñoz, Antonio Arauzo-Azofra und Lorenzo Salas-Morera. „Impact of Health Apps in Health and Computer Science Publications. A Systematic Review from 2010 to 2014“. In Bioinformatics and Biomedical Engineering, 24–34. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16480-9_3.
Collins, James J. „Engineering Gene Regulatory Networks: A Reductionist Approach to Systems Biology“. In Lecture Notes in Computer Science, 505. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11415770_38.
Nagel, Wolfgang, und Christoph Zenger. „Engineering and Computer-Science“. In High Performance Computing in Science and Engineering ’98, 375–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-58600-2_35.
Zenger, Christoph. „Computer Science“. In High Performance Computing in Science and Engineering ’01, 483. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-642-56034-7_48.
Zenger, Christoph. „Computer Science“. In High Performance Computing in Science and Engineering ’99, 445. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-59686-5_42.
Shafer, Wade H. „Communications Engineering and Computer Science“. In Masters Theses in the Pure and Applied Sciences, 99–123. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-5969-6_10.
Shafer, Wade H. „Communications Engineering and Computer Science“. In Masters Theses in the Pure and Applied Sciences, 102–31. Boston, MA: Springer US, 1993. http://dx.doi.org/10.1007/978-1-4615-2453-3_10.
Shafer, Wade H. „Communications Engineering and Computer Science“. In Masters Theses in the Pure and Applied Sciences, 123–56. Boston, MA: Springer US, 1994. http://dx.doi.org/10.1007/978-1-4615-1969-0_10.
Shafer, Wade H. „Communications Engineering and Computer Science“. In Masters Theses in the Pure and Applied Sciences, 113–38. Boston, MA: Springer US, 1988. http://dx.doi.org/10.1007/978-1-4615-7391-3_10.
Shafer, Wade H. „Communications Engineering and Computer Science“. In Masters Theses in the Pure and Applied Sciences, 125–51. Boston, MA: Springer New York, 1987. http://dx.doi.org/10.1007/978-1-4615-7388-3_10.
Konferenzberichte zum Thema "Engineering, Computer|Biology, Bioinformatics|Computer Science":
Chilana, Parmit K., Carole L. Palmer und Andrew J. Ko. „Comparing bioinformatics software development by computer scientists and biologists: An exploratory study“. In 2009 ICSE Workshop on Software Engineering for Computational Science and Engineering (SECSE). IEEE, 2009. http://dx.doi.org/10.1109/secse.2009.5069165.
Fando, Roman, und Maria Klavdieva. „Bioinformatics: Past and Present“. In 2018 International Conference on Engineering Technologies and Computer Science (EnT). IEEE, 2018. http://dx.doi.org/10.1109/ent.2018.00013.
Run-ze, Zhang, Xu Hao, Xu Xiang-rong und Yu Ling-guo. „Research of path planning based on synthetic biology and DNA computer“. In 2015 6th IEEE International Conference on Software Engineering and Service Science (ICSESS). IEEE, 2015. http://dx.doi.org/10.1109/icsess.2015.7339233.
Sharif, J. M., M. S. Latiff, M. A. Ngadi, A. B. A. Hamid, M. S. S. Omar und M. M. A. Jamil. „Spatio-temporal Application for Collaborative Issues in Bioinformatics Datasets“. In 2008 International Conference on Computer Science and Software Engineering. IEEE, 2008. http://dx.doi.org/10.1109/csse.2008.338.
Wang, Dexing, Lixia Cui, Yanhua Wang, Hongchun Yuan und Jian Zhang. „Association Rule Mining Based on Concept Lattice in Bioinformatics Research“. In 2010 International Conference on Biomedical Engineering and Computer Science (ICBECS). IEEE, 2010. http://dx.doi.org/10.1109/icbecs.2010.5462360.
Chen, Gao, Yanlong Wu, Shijun Xiao und Canghai Li. „Bioinformatics Analysis of Mycobacterium Tuberculosis Gene rspL and Its Mutation“. In 2010 International Conference on Biomedical Engineering and Computer Science (ICBECS). IEEE, 2010. http://dx.doi.org/10.1109/icbecs.2010.5462484.
Rutkowski, Tomasz M. „Student teaching and research laboratory focusing on brain-computer interface paradigms - A creative environment for computer science students -“. In 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2015. http://dx.doi.org/10.1109/embc.2015.7319188.
Huang, Angkana, und Apirak Hoonlor. „A multi-layer graph analytics to identify bioinformatics tool usage practices from tool directories and PubMed indexed cross-citations“. In 2016 International Computer Science and Engineering Conference (ICSEC). IEEE, 2016. http://dx.doi.org/10.1109/icsec.2016.7859911.
Johnson, Bruce. „A Bioinformatics-Inspired Adaptation to Ukkonen's Edit Distance Calculating Algorithm and Its Applicability towards Distributed Data Mining“. In 2008 International Conference on Computer Science and Software Engineering. IEEE, 2008. http://dx.doi.org/10.1109/csse.2008.1014.
Si, Shan, Nie Peng, Jiang Li, Yuwen Yan und Xiaojun Yan. „Bioinformatics Analysis on Molecular Mechanism of Poria Cocos in Treatment of Jaundice“. In 2016 International Conference on Computer Engineering, Information Science & Application Technology (ICCIA 2016). Paris, France: Atlantis Press, 2016. http://dx.doi.org/10.2991/iccia-16.2016.4.
Berichte der Organisationen zum Thema "Engineering, Computer|Biology, Bioinformatics|Computer Science":
Chakraborty, Srijani. Promises and Challenges of Systems Biology. Nature Library, Oktober 2020. http://dx.doi.org/10.47496/nl.blog.09.
Hollingsworth, Jeffrey K. High-end-Computer System Performance: Science and Engineering - Final Report. Office of Scientific and Technical Information (OSTI), Januar 2012. http://dx.doi.org/10.2172/1033921.
Malek, Miroslaw. ONR Europe Reports. Computer Science/Computer Engineering in Central Europe: A Report on Czechoslovakia, Hungary, and Poland. Fort Belvoir, VA: Defense Technical Information Center, August 1992. http://dx.doi.org/10.21236/ada264083.
Daniel Reed. PERC 2 High-End Computer System Performance: Scalable Science and Engineering. Office of Scientific and Technical Information (OSTI), Oktober 2006. http://dx.doi.org/10.2172/927308.
Hashemian, Hassan. Infrastructure Academy Transportation Program. Mineta Transportation Institute, Januar 2021. http://dx.doi.org/10.31979/mti.2021.1919.
Knorr, Jeffrey B., und Murali Tummala. Summary of Research 2001, Department of Electrical and Computer Engineering, Graduate School of Engineering and Applied Sciences. Fort Belvoir, VA: Defense Technical Information Center, September 2002. http://dx.doi.org/10.21236/ada415421.
Silbar, R. R. A computer-based ``laboratory`` course in mathematical methods for science and engineering: The Legendre Polynomials module. Final report. Office of Scientific and Technical Information (OSTI), September 1998. http://dx.doi.org/10.2172/314117.
Weinberger, Catherine. Engineering Educational Opportunity: Impacts of 1970s and 1980s Policies to Increase the Share of Black College Graduates with Major in Engineering or Computer Science. Cambridge, MA: National Bureau of Economic Research, August 2017. http://dx.doi.org/10.3386/w23703.
Appoev, R. K., und Zh V. Ignatenko. Electronic educational and methodical complex of discipline "Operations research and optimization methods" (in areas of training 38.00.00 Economics and Management, 09.00.00 Computer Science and Engineering, 44.00.00 Education and pedagogical sciences). North-Caucasian Social Institute, Juni 2016. http://dx.doi.org/10.12731/appoevignatenko.01062016.21898.