Literatura académica sobre el tema "Protein Structure Networks (PSNs)"
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Artículos de revistas sobre el tema "Protein Structure Networks (PSNs)"
Duong, Vy T., Elizabeth M. Diessner, Gianmarc Grazioli, Rachel W. Martin y Carter T. Butts. "Neural Upscaling from Residue-Level Protein Structure Networks to Atomistic Structures". Biomolecules 11, n.º 12 (30 de noviembre de 2021): 1788. http://dx.doi.org/10.3390/biom11121788.
Texto completoNewaz, Khalique, Mahboobeh Ghalehnovi, Arash Rahnama, Panos J. Antsaklis y Tijana Milenković. "Network-based protein structural classification". Royal Society Open Science 7, n.º 6 (junio de 2020): 191461. http://dx.doi.org/10.1098/rsos.191461.
Texto completoYan, Wenying, Daqing Zhang, Chen Shen, Zhongjie Liang y Guang Hu. "Recent Advances on the Network Models in Target-based Drug Discovery". Current Topics in Medicinal Chemistry 18, n.º 13 (4 de octubre de 2018): 1031–43. http://dx.doi.org/10.2174/1568026618666180719152258.
Texto completoAydınkal, Rasim Murat, Onur Serçinoğlu y Pemra Ozbek. "ProSNEx: a web-based application for exploration and analysis of protein structures using network formalism". Nucleic Acids Research 47, W1 (22 de mayo de 2019): W471—W476. http://dx.doi.org/10.1093/nar/gkz390.
Texto completoFelline, Angelo, Michele Seeber y Francesca Fanelli. "webPSN v2.0: a webserver to infer fingerprints of structural communication in biomacromolecules". Nucleic Acids Research 48, W1 (19 de mayo de 2020): W94—W103. http://dx.doi.org/10.1093/nar/gkaa397.
Texto completoHa, Tae Won, Ji Hun Jeong, HyeonSeok Shin, Hyun Kyu Kim, Jeong Suk Im, Byung Hoo Song, Jacob Hanna et al. "Characterization of Endoplasmic Reticulum (ER) in Human Pluripotent Stem Cells Revealed Increased Susceptibility to Cell Death upon ER Stress". Cells 9, n.º 5 (26 de abril de 2020): 1078. http://dx.doi.org/10.3390/cells9051078.
Texto completoPuspitasari, Ira, Shukor Sanim Mohd Fauzi y Cheng-Yuan Ho. "Factors Driving Users’ Engagement in Patient Social Network Systems". Informatics 8, n.º 1 (9 de febrero de 2021): 8. http://dx.doi.org/10.3390/informatics8010008.
Texto completoDeng, Yu Qiao y Ge Song. "A Verifiable Visual Cryptography Scheme Using Neural Networks". Advanced Materials Research 756-759 (septiembre de 2013): 1361–65. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.1361.
Texto completoGreene, L. H. "Protein structure networks". Briefings in Functional Genomics 11, n.º 6 (4 de octubre de 2012): 469–78. http://dx.doi.org/10.1093/bfgp/els039.
Texto completoHase, T., Y. Suzuki, S. Ogisima y H. Tanaka. "Hierarchical Structure of Protein Protein Interaction Networks". Seibutsu Butsuri 43, supplement (2003): S244. http://dx.doi.org/10.2142/biophys.43.s244_1.
Texto completoTesis sobre el tema "Protein Structure Networks (PSNs)"
Zhao, Jing. "Protein Structure Prediction Based on Neural Networks". Thèse, Université d'Ottawa / University of Ottawa, 2013. http://hdl.handle.net/10393/23636.
Texto completoZotenko, 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.
Texto completoThesis 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.
Texto completoThis 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.
Tsilo, Lipontseng Cecilia. "Protein secondary structure prediction using neural networks and support vector machines". Thesis, Rhodes University, 2009. http://hdl.handle.net/10962/d1002809.
Texto completoAlistair, 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.
Texto completoThis 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.
Texto completoMulnaes, 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.
Texto completoRoyer, 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.
Texto completoPlanas, 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.
Texto completoLes 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.
Senekal, Frederick Petrus. "Protein secondary structure prediction using amino acid regularities". Diss., Pretoria : [s.n.], 2008. http://upetd.up.ac.za/thesis/available/etd-01232009-120040/.
Texto completoLibros sobre el tema "Protein Structure Networks (PSNs)"
Protein interaction networks: Computational analysis. Cambridge: Cambridge University Press, 2009.
Buscar texto completoFlapan, Erica. Knots, molecules, and the universe: An introduction to topology. Providence, Rhode Island: American Mathematical Society, 2015.
Buscar texto completoMrozek, Dariusz. High-Performance Computational Solutions in Protein Bioinformatics. Springer London, Limited, 2014.
Buscar texto completoHigh-Performance Computational Solutions in Protein Bioinformatics. Springer International Publishing AG, 2014.
Buscar texto completoNewman, Mark. Biological networks. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198805090.003.0005.
Texto completoCapítulos de libros sobre el tema "Protein Structure Networks (PSNs)"
Vanhala, J. y E. Clementi. "Protein Structure Prediction and Neural Networks". En 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.
Texto completoPunta, Marco y Burkhard Rost. "Neural Networks Predict Protein Structure and Function". En Methods in Molecular Biology™, 198–225. Totowa, NJ: Humana Press, 2008. http://dx.doi.org/10.1007/978-1-60327-101-1_11.
Texto completoGuermeur, Yann y Patrick Gallinari. "Combining statistical models for protein secondary structure prediction". En Artificial Neural Networks — ICANN 96, 599–604. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/3-540-61510-5_102.
Texto completoZhou, Yaoqi y Eshel Faraggi. "Prediction of One-Dimensional Structural Properties Of Proteins by Integrated Neural Networks". En Introduction to Protein Structure Prediction, 45–74. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2010. http://dx.doi.org/10.1002/9780470882207.ch4.
Texto completoFang, Yi, Mengtian Sun, Guoxian Dai y Karthik Ramani. "The Intrinsic Geometric Structure of Protein-Protein Interaction Networks for Protein Interaction Prediction". En Intelligent Computing in Bioinformatics, 487–93. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09330-7_56.
Texto completoWu, Cathy H. "Neural Networks for Molecular Sequence Classification". En 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.
Texto completoMason, Oliver, Mark Verwoerd y Peter Clifford. "Inference of Protein Function from the Structure of Interaction Networks". En Structural Analysis of Complex Networks, 439–61. Boston: Birkhäuser Boston, 2010. http://dx.doi.org/10.1007/978-0-8176-4789-6_18.
Texto completoCondori, E. Sejje, J. Soncco Lupa, S. Barrios Cornejo y V. Machaca Arceda. "ArgosMol: A Web Tool for Protein Structure Prediction and Visualization". En 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.
Texto completoShao, Jianlin, Dong Xu, Lanzhou Wang y Yifei Wang. "Bayesian Neural Networks for Prediction of Protein Secondary Structure". En Advanced Data Mining and Applications, 544–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11527503_65.
Texto completoShi, S. Y. M. y P. N. Suganthan. "Feature Analysis and Classification of Protein Secondary Structure Data". En 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.
Texto completoActas de conferencias sobre el tema "Protein Structure Networks (PSNs)"
KUCHAIEV, OLEKSII y NATAŠA PRŽULJ. "LEARNING THE STRUCTURE OF PROTEIN-PROTEIN INTERACTION NETWORKS". En Proceedings of the Pacific Symposium. WORLD SCIENTIFIC, 2008. http://dx.doi.org/10.1142/9789812836939_0005.
Texto completoBohr, Henrik, Jacob Bohr, So̸ren Brunak, Rodney M. J. Cotterill, Henrik Fredholm, Benny Lautrup y Steffen B. Petersen. "Neural Networks Applied to Protein Structure". En Advances in biomolecular simulations. AIP, 1991. http://dx.doi.org/10.1063/1.41313.
Texto completoSingh, Preeti y Yan-Qing Zhang. "Protein secondary structure prediction using neural networks". En Defense and Security, editado por Belur V. Dasarathy. SPIE, 2004. http://dx.doi.org/10.1117/12.541411.
Texto completoWu, Daniel Duanqing y Xiaohua Hu. "Mining and analyzing the topological structure of protein-protein interaction networks". En the 2006 ACM symposium. New York, New York, USA: ACM Press, 2006. http://dx.doi.org/10.1145/1141277.1141318.
Texto completoDong Wang, Shiyuan Han, Yuehui Chen, Wenzheng Bao, Kun Ma y Ajith Abraham. "A new protein structure classification model". En 2014 6th International Conference on Computational Aspects of Social Networks (CASoN). IEEE, 2014. http://dx.doi.org/10.1109/cason.2014.6920419.
Texto completoBabaei, Sepideh, Seyyed A. Seyyedsalehi y Amir Geranmayeh. "Pruning neural networks for protein secondary structure prediction". En 2008 8th IEEE International Conference on Bioinformatics and BioEngineering (BIBE). IEEE, 2008. http://dx.doi.org/10.1109/bibe.2008.4696702.
Texto completoAydin, Zafer y Ommu Gulsum Uzut. "Combining classifiers for protein secondary structure prediction". En 2017 9th International Conference on Computational Intelligence and Communication Networks (CICN). IEEE, 2017. http://dx.doi.org/10.1109/cicn.2017.8319350.
Texto completoDzikovska, Vasilka, Mile Oreskovic, Slobodan Kalajdziski, Kire Trivodaliev y Danco Davcev. "Protein Secondary Structure Prediction Method Based on Neural Networks". En 2008 2nd International Conference on Bioinformatics and Biomedical Engineering. IEEE, 2008. http://dx.doi.org/10.1109/icbbe.2008.48.
Texto completoZhu, Hongbing, Chengdong Pu, Xiaoli Lin, Jinguang Gu, Shanjun Zhang y Mengsi Su. "Protein Structure Prediction with EPSO in Toy Model". En 2009 Second International Conference on Intelligent Networks and Intelligent Systems (ICINIS). IEEE, 2009. http://dx.doi.org/10.1109/icinis.2009.172.
Texto completoZhu, Hongbing, Jun Wu y Jianguo Wu. "Protein Structure Prediction with Improved Quantum Immune Algorithm". En 2010 3rd International Conference on Intelligent Networks and Intelligent Systems (ICINIS). IEEE, 2010. http://dx.doi.org/10.1109/icinis.2010.49.
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