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Статті в журналах з теми "Algorithms- Protein"
Hulianytskyi, Leonid, and Sergii Chornozhuk. "Genetic Algorithm with New Stochastic Greedy Crossover Operator for Protein Structure Folding Problem." Cybernetics and Computer Technologies, no. 2 (July 24, 2020): 19–29. http://dx.doi.org/10.34229/2707-451x.20.2.3.
Повний текст джерелаCavanaugh, David, and Krishnan Chittur. "A hydrophobic proclivity index for protein alignments." F1000Research 4 (October 21, 2015): 1097. http://dx.doi.org/10.12688/f1000research.6348.1.
Повний текст джерелаCavanaugh, David, and Krishnan Chittur. "A hydrophobic proclivity index for protein alignments." F1000Research 4 (October 15, 2020): 1097. http://dx.doi.org/10.12688/f1000research.6348.2.
Повний текст джерелаBegleiter, R., R. El-Yaniv, and G. Yona. "On Prediction Using Variable Order Markov Models." Journal of Artificial Intelligence Research 22 (December 1, 2004): 385–421. http://dx.doi.org/10.1613/jair.1491.
Повний текст джерелаMoschopoulos, Charalampos, Grigorios Beligiannis, Spiridon Likothanassis, and Sophia Kossida. "Using a Genetic Algorithm and Markov Clustering on Protein–Protein Interaction Graphs." International Journal of Systems Biology and Biomedical Technologies 1, no. 2 (April 2012): 35–47. http://dx.doi.org/10.4018/ijsbbt.2012040103.
Повний текст джерелаWang, Derui, and Jingyu Hou. "Explore the hidden treasure in protein–protein interaction networks — An iterative model for predicting protein functions." Journal of Bioinformatics and Computational Biology 13, no. 05 (October 2015): 1550026. http://dx.doi.org/10.1142/s0219720015500262.
Повний текст джерелаDandekar, Thomas, and Patrick Argos. "Potential of genetic algorithms in protein folding and protein engineering simulations." "Protein Engineering, Design and Selection" 5, no. 7 (1992): 637–45. http://dx.doi.org/10.1093/protein/5.7.637.
Повний текст джерелаGainza, Pablo, Hunter M. Nisonoff, and Bruce R. Donald. "Algorithms for protein design." Current Opinion in Structural Biology 39 (August 2016): 16–26. http://dx.doi.org/10.1016/j.sbi.2016.03.006.
Повний текст джерелаBrown, Michael Scott, Tommy Bennett, and James A. Coker. "Niche Genetic Algorithms are better than traditional Genetic Algorithms for de novo Protein Folding." F1000Research 3 (October 7, 2014): 236. http://dx.doi.org/10.12688/f1000research.5412.1.
Повний текст джерелаKhatami, Mohammad Hassan, Udson C. Mendes, Nathan Wiebe, and Philip M. Kim. "Gate-based quantum computing for protein design." PLOS Computational Biology 19, no. 4 (April 12, 2023): e1011033. http://dx.doi.org/10.1371/journal.pcbi.1011033.
Повний текст джерелаДисертації з теми "Algorithms- Protein"
Derevyanko, Georgy. "Structure-based algorithms for protein-protein interactions." Thesis, Grenoble, 2014. http://www.theses.fr/2014GRENY070/document.
Повний текст джерелаThe phenotype of every known living organism is determined mainly by the complicated interactions between the proteins produced in this organism. Understanding the orchestration of the organismal responses to the external or internal stimuli is based on the understanding of the interactions of individual proteins and their complexes structures. The prediction of a complex of two or more proteins is the problem of the protein-protein docking field. Docking algorithms usually have two major steps: exhaustive 6D rigid-body search followed by the scoring. In this work we made contribution to both of these steps. We developed a novel algorithm for 6D exhaustive search, HermiteFit. It is based on Hermite decomposition of 3D functions into the Hermite basis. We implemented this algorithm in the program for fitting low-resolution electron density maps. We showed that it outperforms existing algorithms in terms of time-per-point while maintaining the same output model accuracy. We also developed a novel approach to computation of a scoring function, which is based on simple logical arguments and avoids an ambiguous computation of the reference state. We compared it to the existing scoring functions on the widely used protein-protein docking benchmarks. Finally, we developed an approach to include water-protein interactions into the scoring functions and validated our method during the Critical Assessment of Protein Interactions round 47
Lassmann, Timo. "Algorithms for building and evaluating multiple sequence alignments /." Stockholm, 2006. http://diss.kib.ki.se/2006/91-7140-887-8/.
Повний текст джерелаHosur, Raghavendra. "Structure-based algorithms for protein-protein interaction prediction." Thesis, Massachusetts Institute of Technology, 2012. http://hdl.handle.net/1721.1/75843.
Повний текст джерелаThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student submitted PDF version of thesis.
Includes bibliographical references (p. 109-124).
Protein-protein interactions (PPIs) play a central role in all biological processes. Akin to the complete sequencing of genomes, complete descriptions of interactomes is a fundamental step towards a deeper understanding of biological processes, and has a vast potential to impact systems biology, genomics, molecular biology and therapeutics. PPIs are critical in maintenance of cellular integrity, metabolism, transcription/ translation, and cell-cell communication. This thesis develops new methods that significantly advance our efforts at structure- based approaches to predict PPIs and boost confidence in emerging high-throughput (HTP) data. The aims of this thesis are, 1) to utilize physicochemical properties of protein interfaces to better predict the putative interacting regions and increase coverage of PPI prediction, 2) increase confidence in HTP datasets by identifying likely experimental errors, and 3) provide residue-level information that gives us insights into structure-function relationships in PPIs. Taken together, these methods will vastly expand our understanding of macromolecular networks. In this thesis, I introduce two computational approaches for structure-based proteinprotein interaction prediction: iWRAP and Coev2Net. iWRAP is an interface threading approach that utilizes biophysical properties specific to protein interfaces to improve PPI prediction. Unlike previous structure-based approaches that use single structures to make predictions, iWRAP first builds profiles that characterize the hydrophobic, electrostatic and structural properties specific to protein interfaces from multiple interface alignments. Compatibility with these profiles is used to predict the putative interface region between the two proteins. In addition to improved interface prediction, iWRAP provides better accuracy and close to 50% increase in coverage on genome-scale PPI prediction tasks. As an application, we effectively combine iWRAP with genomic data to identify novel cancer related genes involved in chromatin remodeling, nucleosome organization and ribonuclear complex assembly - processes known to be critical in cancer. Coev2Net addresses some of the limitations of iWRAP, and provides techniques to increase coverage and accuracy even further. Unlike earlier sequence and structure profiles, Coev2Net explicitly models long-distance correlations at protein interfaces. By formulating interface co-evolution as a high-dimensional sampling problem, we enrich sequence/structure profiles with artificial interacting homologus sequences for families which do not have known multiple interacting homologs. We build a spanning-tree based graphical model induced by the simulated sequences as our interface profile. Cross-validation results indicate that this approach is as good as previous methods at PPI prediction. We show that Coev2Net's predictions correlate with experimental observations and experimentally validate some of the high-confidence predictions. Furthermore, we demonstrate how analysis of the predicted interfaces together with human genomic variation data can help us understand the role of these mutations in disease and normal cells.
by Raghavendra Hosur.
Ph.D.
Bazzoli, A. "Protein structure prediction and protein design with evolutionary algorithms." Doctoral thesis, Università degli Studi di Milano, 2009. http://hdl.handle.net/2434/64478.
Повний текст джерелаLappe, Michael. "Novel algorithms for protein interaction networks." Thesis, University of Cambridge, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.615625.
Повний текст джерелаSajjadi, Sajdeh [Verfasser]. "Step by step in fast protein-protein docking algorithms / Sajdeh Sajjadi." Lübeck : Zentrale Hochschulbibliothek Lübeck, 2014. http://d-nb.info/1060276887/34.
Повний текст джерелаC, Dukka Bahadur K. "Clique-based algorithms for protein structure prediction." 京都大学 (Kyoto University), 2006. http://hdl.handle.net/2433/143887.
Повний текст джерелаThomas, Dallas, and University of Lethbridge Faculty of Arts and Science. "Algorithms & experiments for the protein chain lattice fitting problem." Thesis, Lethbridge, Alta. : University of Lethbridge, Faculty of Arts and Science, 2006, 2006. http://hdl.handle.net/10133/535.
Повний текст джерелаix, 47 leaves ; 29 cm.
Gamalielsson, Jonas. "Models for Protein Structure Prediction by Evolutionary Algorithms." Thesis, University of Skövde, Department of Computer Science, 2001. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-623.
Повний текст джерелаEvolutionary algorithms (EAs) have been shown to be competent at solving complex, multimodal optimisation problems in applications where the search space is large and badly understood. EAs are therefore among the most promising classes of algorithms for solving the Protein Structure Prediction Problem (PSPP). The PSPP is how to derive the 3D-structure of a protein given only its sequence of amino acids. This dissertation defines, evaluates and shows limitations of simplified models for solving the PSPP. These simplified models are off-lattice extensions to the lattice HP model which has been proposed and is claimed to possess some of the properties of real protein folding such as the formation of a hydrophobic core. Lattice models usually model a protein at the amino acid level of detail, use simple energy calculations and are used mainly for search algorithm development. Off-lattice models usually model the protein at the atomic level of detail, use more complex energy calculations and may be used for comparison with real proteins. The idea is to combine the fast energy calculations of lattice models with the increased spatial possibilities of an off-lattice environment allowing for comparison with real protein structures. A hypothesis is presented which claims that a simplified off-lattice model which considers other amino acid properties apart from hydrophobicity will yield simulated structures with lower Root Mean Square Deviation (RMSD) to the native fold than a model only considering hydrophobicity. The hypothesis holds for four of five tested short proteins with a maximum of 46 residues. Best average RMSD for any model tested is above 6Å, i.e. too high for useful structure prediction and excludes significant resemblance between native and simulated structure. Hence, the tested models do not contain the necessary biological information to capture the complex interactions of real protein folding. It is also shown that the EA itself is competent and can produce near-native structures if given a suitable evaluation function. Hence, EAs are useful for eventually solving the PSPP.
Parry-Smith, David John. "Algorithms and data structures for protein sequence analysis." Thesis, University of Leeds, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.277404.
Повний текст джерелаКниги з теми "Algorithms- Protein"
Rangwala, Huzefa. Introduction to protein structure prediction: Methods and algorithms. Hoboken, N.J: Wiley, 2010.
Знайти повний текст джерелаRangwala, Huzefa, G. Karypis, and G. Karypis. Introduction to protein structure prediction: Methods and algorithms. Hoboken, N.J: Wiley, 2010.
Знайти повний текст джерелаPan, Yi, Jianxin Wang, and Min Li. Algorithmic and Artificial Intelligence Methods for Protein Bioinformatics. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118567869.
Повний текст джерелаAlgorithmic and artificial intelligence methods for protein bioinformatics. Hoboken, New Jersey: Wiley, IEEE Computer Society, 2014.
Знайти повний текст джерелаDonald, Bruce R. Algorithms in structural molecular biology. Cambridge, Mass: MIT Press, 2011.
Знайти повний текст джерелаInge, Jonassen, and Taylor W. R, eds. Protein bioinformatics: An algorithmic approach to sequence and structure analysis. New York: J. Wiley & Sons, 2004.
Знайти повний текст джерелаM, Sansano Allen, and Langley Research Center, eds. Minimizing overhead in parallel algorithms through overlapping communication/computation. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1997.
Знайти повний текст джерелаSokolov, Artem, and Oleg Zhdanov. Cryptographic constructions on the basis of functions of multivalued logic. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1045434.
Повний текст джерелаKostyukov, Viktor. Molecular mechanics of biopolymers. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1010677.
Повний текст джерелаInformation-theoretic evaluation for computational biomedical ontologies. Cham: Springer, 2014.
Знайти повний текст джерелаЧастини книг з теми "Algorithms- Protein"
Jothi, Raja, and Teresa M. Przytycka. "Computational Approaches to Predict Protein-Protein and Domain-Domain Interactions." In Bioinformatics Algorithms, 465–91. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2007. http://dx.doi.org/10.1002/9780470253441.ch21.
Повний текст джерелаMalod-Dognin, Noël, Rumen Andonov, and Nicola Yanev. "Maximum Cliques in Protein Structure Comparison." In Experimental Algorithms, 106–17. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13193-6_10.
Повний текст джерелаYao, Yin, and Martin C. Frith. "Improved DNA-versus-Protein Homology Search for Protein Fossils." In Algorithms for Computational Biology, 146–58. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-74432-8_11.
Повний текст джерелаMaji, Pradipta, and Sushmita Paul. "Identification of Disease Genes Using Gene Expression and Protein–Protein Interaction Data." In Scalable Pattern Recognition Algorithms, 155–70. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05630-2_6.
Повний текст джерелаvon Looz, Moritz, Mario Wolter, Christoph R. Jacob, and Henning Meyerhenke. "Better Partitions of Protein Graphs for Subsystem Quantum Chemistry." In Experimental Algorithms, 353–68. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-38851-9_24.
Повний текст джерелаYoo, Paul D., Bing Bing Zhou, and Albert Y. Zomaya. "Protein Domain Boundary Prediction." In Algorithms in Computational Molecular Biology, 501–19. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2010. http://dx.doi.org/10.1002/9780470892107.ch23.
Повний текст джерелаYao, Qiuming, Jianjiong Gao, and Dong Xu. "Musite: Tool for Predicting Protein Phosphorylation Sites." In Encyclopedia of Algorithms, 1393–97. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-2864-4_600.
Повний текст джерелаLi, Shuai Cheng, and Yen Kaow Ng. "On Protein Structure Alignment under Distance Constraint." In Algorithms and Computation, 65–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10631-6_9.
Повний текст джерелаYao, Qiuming, Jianjiong Gao, and Dong Xu. "Musite: Tool for Predicting Protein Phosphorylation Sites." In Encyclopedia of Algorithms, 1–5. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-642-27848-8_600-1.
Повний текст джерелаDemaine, Erik D., Stefan Langerman, and Joseph O’Rourke. "Geometric Restrictions on Producible Polygonal Protein Chains." In Algorithms and Computation, 395–404. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-24587-2_41.
Повний текст джерелаТези доповідей конференцій з теми "Algorithms- Protein"
Hu, Jing, and Yihang Du. "Predicting Moonlighting Proteins from Protein Sequence." In 14th International Conference on Bioinformatics Models, Methods and Algorithms. SCITEPRESS - Science and Technology Publications, 2023. http://dx.doi.org/10.5220/0011782300003414.
Повний текст джерела"PYCOEVOL - A Python Workflow to Study Protein-protein Coevolution." In International Conference on Bioinformatics Models, Methods and Algorithms. SciTePress - Science and and Technology Publications, 2012. http://dx.doi.org/10.5220/0003737901430149.
Повний текст джерелаLi, Zhao, Zhang Tianchi, and Zhang Jing. "Optimization Algorithms for Flexible Protein-Protein Docking." In 2012 Third International Conference on Digital Manufacturing and Automation (ICDMA). IEEE, 2012. http://dx.doi.org/10.1109/icdma.2012.135.
Повний текст джерела"ProRank+ - A Method for Detecting Protein Complexes in Protein Interaction Networks." In International Conference on Bioinformatics Models, Methods and Algorithms. SCITEPRESS - Science and and Technology Publications, 2014. http://dx.doi.org/10.5220/0004910802390244.
Повний текст джерелаArikawa, Keisuke. "Investigation of Algorithms for Analyzing Protein Internal Motion From Viewpoint of Robot Kinematics." In ASME 2010 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/detc2010-28551.
Повний текст джерела"PREDICTION OF CHIMERIC PROTEIN FOLD." In International Conference on Bioinformatics Models, Methods and Algorithms. SciTePress - Science and and Technology Publications, 2012. http://dx.doi.org/10.5220/0003790102340239.
Повний текст джерелаZhang, Yan-Ping, Yong-Cheng Wang, Li-Na Zhang, and Chen-Chu Xu. "Prediction of protein-protein interaction sites using covering algorithms." In Education (ICCSE 2010). IEEE, 2010. http://dx.doi.org/10.1109/iccse.2010.5593619.
Повний текст джерела"SUBSET SEED EXTENSION TO PROTEIN BLAST." In International Conference on Bioinformatics Models, Methods and Algorithms. SciTePress - Science and and Technology Publications, 2011. http://dx.doi.org/10.5220/0003147601490158.
Повний текст джерелаWinter, Pawel, and Rasmus Fonseca. "Alpha Complexes in Protein Structure Prediction." In International Conference on Bioinformatics Models, Methods and Algorithms. SCITEPRESS - Science and and Technology Publications, 2015. http://dx.doi.org/10.5220/0005251401780182.
Повний текст джерелаSatou, Kenji, Yoshiki Shimaguchi, Kunti Mahmudah, Ngoc Nguyen, Mera Delimayanti, Bedy Purnama, Mamoru Kubo, Makiko Kakikawa, and Yoichi Yamada. "Prediction of Subnuclear Location for Nuclear Protein." In 10th International Conference on Bioinformatics Models, Methods and Algorithms. SCITEPRESS - Science and Technology Publications, 2019. http://dx.doi.org/10.5220/0007570502760280.
Повний текст джерелаЗвіти організацій з теми "Algorithms- Protein"
Martin, Shawn Bryan, Kenneth L. Sale, Jean-Loup Michel Faulon, and Diana C. Roe. Developing algorithms for predicting protein-protein interactions of homology modeled proteins. Office of Scientific and Technical Information (OSTI), January 2006. http://dx.doi.org/10.2172/883467.
Повний текст джерелаRangwala, Huzefa, and George Karypis. Incremental Window-based Protein Sequence Alignment Algorithms. Fort Belvoir, VA: Defense Technical Information Center, March 2006. http://dx.doi.org/10.21236/ada444856.
Повний текст джерелаSapiro, Guillermo. New Forcefields and Algorithms for Computational Protein Design. Fort Belvoir, VA: Defense Technical Information Center, January 2003. http://dx.doi.org/10.21236/ada428012.
Повний текст джерелаDeRonne, Kevin W., and George Karypis. Effective Optimization Algorithms for Fragment-Assembly Based Protein Structure Prediction. Fort Belvoir, VA: Defense Technical Information Center, March 2006. http://dx.doi.org/10.21236/ada444732.
Повний текст джерелаRohrbough, James G., Linda Breci, Nirav Merchant, Susan Miller, and Paul A. Haynes. Verification of Single-Peptide Protein Identifications by the Application of Complementary Database Search Algorithms. Fort Belvoir, VA: Defense Technical Information Center, October 2005. http://dx.doi.org/10.21236/ada439637.
Повний текст джерелаKim, Sangtae. Microstructural Models of Interactions That Govern Protein Conformations: Algorithms for High Performance Computer Architectures. Fort Belvoir, VA: Defense Technical Information Center, January 1998. http://dx.doi.org/10.21236/ada360981.
Повний текст джерелаCARR, ROBERT D., GIUSEPPE LANCIA, and SORIN ISTRAIL. Branch-and-Cut Algorithms for Independent Set Problems: Integrality Gap and An Application to Protein Structure Alignment. Office of Scientific and Technical Information (OSTI), September 2000. http://dx.doi.org/10.2172/764804.
Повний текст джерелаGregurick, S. K. AB Initio Protein Tertiary Structure Prediction: Comparative-Genetic Algorithm with Graph Theoretical Methods. Office of Scientific and Technical Information (OSTI), April 2001. http://dx.doi.org/10.2172/834523.
Повний текст джерелаGronberg, J., and J. Hollar. Trigger Algorithm Design for a SUSY Lepton Trigger based on Forward Proton Tagging. Office of Scientific and Technical Information (OSTI), March 2010. http://dx.doi.org/10.2172/975215.
Повний текст джерелаSnihur, Robert Michael. Subjet multiplicity of quark and gluon jets reconstructed with the relative transverse momenta algorithm in proton - anti-proton collisions. Office of Scientific and Technical Information (OSTI), January 2000. http://dx.doi.org/10.2172/1421439.
Повний текст джерела