Добірка наукової літератури з теми "Protein Structure Networks (PSN)"
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Статті в журналах з теми "Protein Structure Networks (PSN)"
Felline, Angelo, Michele Seeber, and Francesca Fanelli. "webPSN v2.0: a webserver to infer fingerprints of structural communication in biomacromolecules." Nucleic Acids Research 48, W1 (May 19, 2020): W94—W103. http://dx.doi.org/10.1093/nar/gkaa397.
Повний текст джерелаDuong, Vy T., Elizabeth M. Diessner, Gianmarc Grazioli, Rachel W. Martin, and Carter T. Butts. "Neural Upscaling from Residue-Level Protein Structure Networks to Atomistic Structures." Biomolecules 11, no. 12 (November 30, 2021): 1788. http://dx.doi.org/10.3390/biom11121788.
Повний текст джерелаAydınkal, Rasim Murat, Onur Serçinoğlu, and Pemra Ozbek. "ProSNEx: a web-based application for exploration and analysis of protein structures using network formalism." Nucleic Acids Research 47, W1 (May 22, 2019): W471—W476. http://dx.doi.org/10.1093/nar/gkz390.
Повний текст джерелаNewaz, Khalique, Mahboobeh Ghalehnovi, Arash Rahnama, Panos J. Antsaklis, and Tijana Milenković. "Network-based protein structural classification." Royal Society Open Science 7, no. 6 (June 2020): 191461. http://dx.doi.org/10.1098/rsos.191461.
Повний текст джерелаFanelli, Francesca, Angelo Felline, Francesco Raimondi, and Michele Seeber. "Structure network analysis to gain insights into GPCR function." Biochemical Society Transactions 44, no. 2 (April 11, 2016): 613–18. http://dx.doi.org/10.1042/bst20150283.
Повний текст джерелаChasapis, Christos T., and Alexios Vlamis-Gardikas. "Probing Conformational Dynamics by Protein Contact Networks: Comparison with NMR Relaxation Studies and Molecular Dynamics Simulations." Biophysica 1, no. 2 (April 8, 2021): 157–67. http://dx.doi.org/10.3390/biophysica1020012.
Повний текст джерелаMahmud, Khandakar Abu Hasan Al, Fuad Hasan, Md Ishak Khan, and Ashfaq Adnan. "Shock-Induced Damage Mechanism of Perineuronal Nets." Biomolecules 12, no. 1 (December 22, 2021): 10. http://dx.doi.org/10.3390/biom12010010.
Повний текст джерелаLubovac, Zelmina. "Investigating Topological and Functional Features of Multimodular Proteins." Journal of Biomedicine and Biotechnology 2009 (2009): 1–10. http://dx.doi.org/10.1155/2009/472415.
Повний текст джерелаDrago, Valentina, Luisa Di Paola, Claire Lesieur, Renato Bernardini, Claudio Bucolo, and Chiara Bianca Maria Platania. "In-Silico Characterization of von Willebrand Factor Bound to FVIII." Applied Sciences 12, no. 15 (August 4, 2022): 7855. http://dx.doi.org/10.3390/app12157855.
Повний текст джерелаDANICH, V. M., and S. M. SHEVCHENKO. "FORMALIZATION OF THE CONCEPT OF SOCIAL SPACE OF THE SUBJECT THROUGH THE CONCEPT OF SOCIAL NETWORKS." REVIEW OF TRANSPORT ECONOMICS AND MANAGEMENT, no. 4(20) (November 30, 2020): 182–94. http://dx.doi.org/10.15802/rtem2020/228878.
Повний текст джерелаДисертації з теми "Protein Structure Networks (PSN)"
Zhao, Jing. "Protein Structure Prediction Based on Neural Networks." Thèse, Université d'Ottawa / University of Ottawa, 2013. http://hdl.handle.net/10393/23636.
Повний текст джерелаZotenko, Elena. "Computational methods in protein structure comparison and analysis of protein interaction networks." College Park, Md.: University of Maryland, 2007. http://hdl.handle.net/1903/7621.
Повний текст джерелаThesis research directed by: Dept. of Computer Science. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
Grochow, Joshua A. "On the structure and evolution of protein interaction networks." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/42053.
Повний текст джерелаThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Includes bibliographical references (p. 107-114).
The study of protein interactions from the networks point of view has yielded new insights into systems biology [Bar03, MA03, RSM+02, WS98]. In particular, "network motifs" become apparent as a useful and systematic tool for describing and exploring networks [BP06, MKFV06, MSOI+02, SOMMA02, SV06]. Finding motifs has involved either exact counting (e.g. [MSOI+02]) or subgraph sampling (e.g. [BP06, KIMA04a, MZW05]). In this thesis we develop an algorithm to count all instances of a particular subgraph, which can be used to query whether a given subgraph is a significant motif. This method can be used to perform exact counting of network motifs faster and with less memory than previous methods, and can also be combined with subgraph sampling to find larger motifs than ever before -- we have found motifs with up to 15 nodes and explored subgraphs up to 20 nodes. Unlike previous methods, this method can also be used to explore motif clustering and can be combined with network alignment techniques [FNS+06, KSK+03]. We also present new methods of estimating parameters for models of biological network growth, and present a new model based on these parameters and underlying binding domains. Finally, we propose an experiment to explore the effect of the whole genome duplication [KBL04] on the protein-protein interaction network of S. cerevisiae, allowing us to distinguish between cases of subfunctionalization and neofunctionalization.
by Joshua A. Grochow.
M.Eng.
Valenta, Martin. "Predikce proteinových domén." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2013. http://www.nusl.cz/ntk/nusl-236163.
Повний текст джерелаTsilo, Lipontseng Cecilia. "Protein secondary structure prediction using neural networks and support vector machines." Thesis, Rhodes University, 2009. http://hdl.handle.net/10962/d1002809.
Повний текст джерелаAlistair, Chalk. "PREDICTION OF PROTEIN SECONDARY STRUCTURE by Incorporating Biophysical Information into Artificial Neural Networks." Thesis, University of Skövde, Department of Computer Science, 1998. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-235.
Повний текст джерелаThis project applied artificial neural networks to the field of secondary structure prediction of proteins. A NETtalk architecture with a window size 13 was used. Over-fitting was avoided by the use of 3 real numbers to represent amino acids, reducing the number of adjustable weights to 840. Two alternative representations of amino acids that incorporated biophysical data were created and tested. They were tested both separately and in combination on a standard 7-fold cross-validation set of 126 proteins. The best performance was achieved using an average result from two predictions. This was then filtered and gave the following results. Accuracy levels for core structures were: Q3total accuracy of 61.3% consisting of Q3 accuracy’s of 54.0%, 38.1% & 77.0% for Helix, Strand and Coil respectively with Matthew’s correlation’s Ca = 0.34, Cb = 0.26 , Cc = 0.31. The average lengths of structures predicted were 9.8, 4.9 and 11.0, for helix, sheet and coil respectively. These results are lower than those of other methods using single sequences and localist representations. The most likely reason for this is over generalisation caused by using a small number of units.
Reyaz-Ahmed, Anjum B. "Protein Secondary Structure Prediction Using Support Vector Machines, Nueral Networks and Genetic Algorithms." Digital Archive @ GSU, 2007. http://digitalarchive.gsu.edu/cs_theses/43.
Повний текст джерелаMulnaes, Daniel [Verfasser]. "TopSuite: A meta-suite for protein structure prediction using deep neural networks / Daniel Mulnaes." Düsseldorf : Universitäts- und Landesbibliothek der Heinrich-Heine-Universität Düsseldorf, 2020. http://d-nb.info/1222261634/34.
Повний текст джерелаRoyer, Loic. "Unraveling the Structure and Assessing the Quality of Protein Interaction Networks with Power Graph Analysis." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2017. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-62562.
Повний текст джерелаPlanas, Iglesias Joan 1980. "On the study of 3D structure of proteins for developing new algorithms to complete the interactome and cell signalling networks." Doctoral thesis, Universitat Pompeu Fabra, 2013. http://hdl.handle.net/10803/104152.
Повний текст джерелаLes proteïnes tenen un paper indispensable en virtualment qualsevol procés biològic. Les funcions de les proteïnes estan determinades per la seva estructura tridimensional (3D) i són coordinades per mitjà d’una complexa xarxa d’interaccions protiques (en anglès, protein-protein interactions, PPIs). Axí doncs, una comprensió en profunditat d’aquestes xarxes és fonamental per entendre la biologia cel•lular. Per a l’anàlisi de les xarxes d’interacció de proteïnes, l’ús de tècniques computacionals ha esdevingut fonamental als darrers temps. Els mètodes in silico aprofiten el coneixement actual sobre les interaccions proteiques per fer prediccions de noves interaccions o de les funcions de les proteïnes. Actualment existeixen diferents mètodes per a la predicció de noves interaccions de proteines. De tota manera, resultats recents demostren que aquests mètodes poden beneficiar-se del coneixement sobre parelles de proteïnes no interaccionants (en anglès, non-interacting pairs, NIPs). Per a la tasca de predir la funció de les proteïnes, el principi de “culpable per associació” (en anglès, guilt by association, GBA) és usat per extendre l’anotació de proteïnes de funció coneguda a través de xarxes d’interacció de proteïnes. En aquesta tesi es presenta un nou mètode pre a la predicció d’interaccions proteiques i un nou protocol basat per a completar xarxes de senyalització cel•lular. iLoops és un mètode que utilitza dades de parells no interaccionants i coneixement de l’estructura 3D de les proteïnes per a predir interaccions de proteïnes. També s’ha desenvolupat un nou protocol per a completar xarxes de senyalització cel•lular, una tasca relacionada amb la predicció de les funcions de les proteïnes. Aquest protocol es basa en aplicar el principi GBA a xarxes d’interaccions proteiques.
Книги з теми "Protein Structure Networks (PSN)"
Protein interaction networks: Computational analysis. Cambridge: Cambridge University Press, 2009.
Знайти повний текст джерелаFlapan, Erica. Knots, molecules, and the universe: An introduction to topology. Providence, Rhode Island: American Mathematical Society, 2015.
Знайти повний текст джерелаMrozek, Dariusz. High-Performance Computational Solutions in Protein Bioinformatics. Springer London, Limited, 2014.
Знайти повний текст джерелаHigh-Performance Computational Solutions in Protein Bioinformatics. Springer International Publishing AG, 2014.
Знайти повний текст джерелаNewman, Mark. Biological networks. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198805090.003.0005.
Повний текст джерелаЧастини книг з теми "Protein Structure Networks (PSN)"
Vanhala, J., and E. Clementi. "Protein Structure Prediction and Neural Networks." In Modem Techniques in Computational Chemistry: MOTECC-91, 991–1015. Dordrecht: Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-011-3032-5_25.
Повний текст джерелаPunta, Marco, and Burkhard Rost. "Neural Networks Predict Protein Structure and Function." In Methods in Molecular Biology™, 198–225. Totowa, NJ: Humana Press, 2008. http://dx.doi.org/10.1007/978-1-60327-101-1_11.
Повний текст джерелаGuermeur, Yann, and Patrick Gallinari. "Combining statistical models for protein secondary structure prediction." In Artificial Neural Networks — ICANN 96, 599–604. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/3-540-61510-5_102.
Повний текст джерелаZhou, Yaoqi, and Eshel Faraggi. "Prediction of One-Dimensional Structural Properties Of Proteins by Integrated Neural Networks." In Introduction to Protein Structure Prediction, 45–74. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2010. http://dx.doi.org/10.1002/9780470882207.ch4.
Повний текст джерелаFang, Yi, Mengtian Sun, Guoxian Dai, and Karthik Ramani. "The Intrinsic Geometric Structure of Protein-Protein Interaction Networks for Protein Interaction Prediction." In Intelligent Computing in Bioinformatics, 487–93. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09330-7_56.
Повний текст джерелаWu, Cathy H. "Neural Networks for Molecular Sequence Classification." In The Protein Folding Problem and Tertiary Structure Prediction, 279–305. Boston, MA: Birkhäuser Boston, 1994. http://dx.doi.org/10.1007/978-1-4684-6831-1_9.
Повний текст джерелаMason, Oliver, Mark Verwoerd, and Peter Clifford. "Inference of Protein Function from the Structure of Interaction Networks." In Structural Analysis of Complex Networks, 439–61. Boston: Birkhäuser Boston, 2010. http://dx.doi.org/10.1007/978-0-8176-4789-6_18.
Повний текст джерелаCondori, E. Sejje, J. Soncco Lupa, S. Barrios Cornejo, and V. Machaca Arceda. "ArgosMol: A Web Tool for Protein Structure Prediction and Visualization." In Lecture Notes in Networks and Systems, 604–16. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98012-2_43.
Повний текст джерелаShao, Jianlin, Dong Xu, Lanzhou Wang, and Yifei Wang. "Bayesian Neural Networks for Prediction of Protein Secondary Structure." In Advanced Data Mining and Applications, 544–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11527503_65.
Повний текст джерелаShi, S. Y. M., and P. N. Suganthan. "Feature Analysis and Classification of Protein Secondary Structure Data." In Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003, 1151–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-44989-2_137.
Повний текст джерелаТези доповідей конференцій з теми "Protein Structure Networks (PSN)"
KUCHAIEV, OLEKSII, and NATAŠA PRŽULJ. "LEARNING THE STRUCTURE OF PROTEIN-PROTEIN INTERACTION NETWORKS." In Proceedings of the Pacific Symposium. WORLD SCIENTIFIC, 2008. http://dx.doi.org/10.1142/9789812836939_0005.
Повний текст джерелаBohr, Henrik, Jacob Bohr, So̸ren Brunak, Rodney M. J. Cotterill, Henrik Fredholm, Benny Lautrup, and Steffen B. Petersen. "Neural Networks Applied to Protein Structure." In Advances in biomolecular simulations. AIP, 1991. http://dx.doi.org/10.1063/1.41313.
Повний текст джерелаSingh, Preeti, and Yan-Qing Zhang. "Protein secondary structure prediction using neural networks." In Defense and Security, edited by Belur V. Dasarathy. SPIE, 2004. http://dx.doi.org/10.1117/12.541411.
Повний текст джерелаWu, Daniel Duanqing, and Xiaohua Hu. "Mining and analyzing the topological structure of protein-protein interaction networks." In the 2006 ACM symposium. New York, New York, USA: ACM Press, 2006. http://dx.doi.org/10.1145/1141277.1141318.
Повний текст джерелаDong Wang, Shiyuan Han, Yuehui Chen, Wenzheng Bao, Kun Ma, and Ajith Abraham. "A new protein structure classification model." In 2014 6th International Conference on Computational Aspects of Social Networks (CASoN). IEEE, 2014. http://dx.doi.org/10.1109/cason.2014.6920419.
Повний текст джерелаRatul, Md Aminur Rab, Maryam Tavakol Elahi, M. Hamed Mozaffari, and WonSook Lee. "PS8-Net: A Deep Convolutional Neural Network to Predict the Eight-State Protein Secondary Structure." In 2020 Digital Image Computing: Techniques and Applications (DICTA). IEEE, 2020. http://dx.doi.org/10.1109/dicta51227.2020.9363393.
Повний текст джерелаBabaei, Sepideh, Seyyed A. Seyyedsalehi, and Amir Geranmayeh. "Pruning neural networks for protein secondary structure prediction." In 2008 8th IEEE International Conference on Bioinformatics and BioEngineering (BIBE). IEEE, 2008. http://dx.doi.org/10.1109/bibe.2008.4696702.
Повний текст джерелаAydin, Zafer, and Ommu Gulsum Uzut. "Combining classifiers for protein secondary structure prediction." In 2017 9th International Conference on Computational Intelligence and Communication Networks (CICN). IEEE, 2017. http://dx.doi.org/10.1109/cicn.2017.8319350.
Повний текст джерелаDzikovska, Vasilka, Mile Oreskovic, Slobodan Kalajdziski, Kire Trivodaliev, and Danco Davcev. "Protein Secondary Structure Prediction Method Based on Neural Networks." In 2008 2nd International Conference on Bioinformatics and Biomedical Engineering. IEEE, 2008. http://dx.doi.org/10.1109/icbbe.2008.48.
Повний текст джерелаZhu, Hongbing, Chengdong Pu, Xiaoli Lin, Jinguang Gu, Shanjun Zhang, and Mengsi Su. "Protein Structure Prediction with EPSO in Toy Model." In 2009 Second International Conference on Intelligent Networks and Intelligent Systems (ICINIS). IEEE, 2009. http://dx.doi.org/10.1109/icinis.2009.172.
Повний текст джерелаЗвіти організацій з теми "Protein Structure Networks (PSN)"
Kirchhoff, Helmut, and Ziv Reich. Protection of the photosynthetic apparatus during desiccation in resurrection plants. United States Department of Agriculture, February 2014. http://dx.doi.org/10.32747/2014.7699861.bard.
Повний текст джерела