Relevant bibliographies by topics / Protein Structure Networks (PSNs)

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Academic literature on the topic 'Protein Structure Networks (PSNs)'

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Published: 6 September 2023

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Journal articles on the topic "Protein Structure Networks (PSNs)"

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Duong, 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.

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Coarse-graining is a powerful tool for extending the reach of dynamic models of proteins and other biological macromolecules. Topological coarse-graining, in which biomolecules or sets thereof are represented via graph structures, is a particularly useful way of obtaining highly compressed representations of molecular structures, and simulations operating via such representations can achieve substantial computational savings. A drawback of coarse-graining, however, is the loss of atomistic detail—an effect that is especially acute for topological representations such as protein structure networks (PSNs). Here, we introduce an approach based on a combination of machine learning and physically-guided refinement for inferring atomic coordinates from PSNs. This “neural upscaling” procedure exploits the constraints implied by PSNs on possible configurations, as well as differences in the likelihood of observing different configurations with the same PSN. Using a 1 μs atomistic molecular dynamics trajectory of Aβ1–40, we show that neural upscaling is able to effectively recapitulate detailed structural information for intrinsically disordered proteins, being particularly successful in recovering features such as transient secondary structure. These results suggest that scalable network-based models for protein structure and dynamics may be used in settings where atomistic detail is desired, with upscaling employed to impute atomic coordinates from PSNs.
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Newaz, 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.

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Experimental determination of protein function is resource-consuming. As an alternative, computational prediction of protein function has received attention. In this context, protein structural classification (PSC) can help, by allowing for determining structural classes of currently unclassified proteins based on their features, and then relying on the fact that proteins with similar structures have similar functions. Existing PSC approaches rely on sequence-based or direct three-dimensional (3D) structure-based protein features. By contrast, we first model 3D structures of proteins as protein structure networks (PSNs). Then, we use network-based features for PSC. We propose the use of graphlets, state-of-the-art features in many research areas of network science, in the task of PSC. Moreover, because graphlets can deal only with unweighted PSNs, and because accounting for edge weights when constructing PSNs could improve PSC accuracy, we also propose a deep learning framework that automatically learns network features from weighted PSNs. When evaluated on a large set of approximately 9400 CATH and approximately 12 800 SCOP protein domains (spanning 36 PSN sets), the best of our proposed approaches are superior to existing PSC approaches in terms of accuracy, with comparable running times. Our data and code are available at https://doi.org/10.5281/zenodo.3787922
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Yan, Wenying, Daqing Zhang, Chen Shen, Zhongjie Liang, and Guang Hu. "Recent Advances on the Network Models in Target-based Drug Discovery." Current Topics in Medicinal Chemistry 18, no. 13 (October 4, 2018): 1031–43. http://dx.doi.org/10.2174/1568026618666180719152258.

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With the advancement of “proteomics” data and systems biology, new techniques are needed to meet the new era of drug discovery. Network theory is increasingly applied to describe complex biological systems, thus implying its essential roles in system-based drug design. In this review, we first summarized general network parameters used in describing biological systems, and then gave some recent applications of these network parameters as topological indices in drug design in terms of Protein Structure Networks (PSNs), Protein-Protein Interaction Networks (PPINs) including related structural PPINs, and Elastic Network Models (ENMs). These network models have enabled the development of new drugs relying on allosteric effects, describing anti-cancer targets, targeting hot spots and key proteins at the protein-protein interfaces and PPINs, and helped drug design by modulating conformational flexibility. Accordingly, we highlighted the integration of network models bringing new paradigms into the next-generation target-based drug discovery.
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Aydınkal, 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.

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AbstractProSNEx (Protein Structure Network Explorer) is a web service for construction and analysis of Protein Structure Networks (PSNs) alongside amino acid flexibility, sequence conservation and annotation features. ProSNEx constructs a PSN by adding nodes to represent residues and edges between these nodes using user-specified interaction distance cutoffs for either carbon-alpha, carbon-beta or atom-pair contact networks. Different types of weighted networks can also be constructed by using either (i) the residue-residue interaction energies in the format returned by gRINN, resulting in a Protein Energy Network (PEN); (ii) the dynamical cross correlations from a coarse-grained Normal Mode Analysis (NMA) of the protein structure; (iii) interaction strength. Upon construction of the network, common network metrics (such as node centralities) as well as shortest paths between nodes and k-cliques are calculated. Moreover, additional features of each residue in the form of conservation scores and mutation/natural variant information are included in the analysis. By this way, tool offers an enhanced and direct comparison of network-based residue metrics with other types of biological information. ProSNEx is free and open to all users without login requirement at http://prosnex-tool.com.
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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.

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Abstract A mixed Protein Structure Network (PSN) and Elastic Network Model-Normal Mode Analysis (ENM-NMA)-based strategy (i.e. PSN-ENM) was developed to investigate structural communication in bio-macromolecules. Protein Structure Graphs (PSGs) are computed on a single structure, whereas information on system dynamics is supplied by ENM-NMA. The approach was implemented in a webserver (webPSN), which was significantly updated herein. The webserver now handles both proteins and nucleic acids and relies on an internal upgradable database of network parameters for ions and small molecules in all PDB structures. Apart from the radical restyle of the server and some changes in the calculation setup, other major novelties concern the possibility to: a) compute the differences in nodes, links, and communication pathways between two structures (i.e. network difference) and b) infer links, hubs, communities, and metapaths from consensus networks computed on a number of structures. These new features are useful to identify commonalties and differences between two different functional states of the same system or structural-communication signatures in homologous or analogous systems. The output analysis relies on 3D-representations, interactive tables and graphs, also available for download. Speed and accuracy make this server suitable to comparatively investigate structural communication in large sets of bio-macromolecular systems. URL: http://webpsn.hpc.unimore.it.
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Ha, 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, no. 5 (April 26, 2020): 1078. http://dx.doi.org/10.3390/cells9051078.

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Human pluripotent stem cells (hPSCs), such as embryonic stem cells (ESCs) and induced pluripotent stem cells (iPSCs), have a well-orchestrated program for differentiation and self-renewal. However, the structural features of unique proteostatic-maintaining mechanisms in hPSCs and their features, distinct from those of differentiated cells, in response to cellular stress remain unclear. We evaluated and compared the morphological features and stress response of hPSCs and fibroblasts. Compared to fibroblasts, electron microscopy showed simpler/fewer structures with fewer networks in the endoplasmic reticulum (ER) of hPSCs, as well as lower expression of ER-related genes according to meta-analysis. As hPSCs contain low levels of binding immunoglobulin protein (BiP), an ER chaperone, thapsigargin treatment sharply increased the gene expression of the unfolded protein response. Thus, hPSCs with decreased chaperone function reacted sensitively to ER stress and entered apoptosis faster than fibroblasts. Such ER stress-induced apoptotic processes were abolished by tauroursodeoxycholic acid, an ER-stress reliever. Hence, our results revealed that as PSCs have an underdeveloped structure and express fewer BiP chaperone proteins than somatic cells, they are more susceptible to ER stress-induced apoptosis in response to stress.
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Puspitasari, Ira, Shukor Sanim Mohd Fauzi, and Cheng-Yuan Ho. "Factors Driving Users’ Engagement in Patient Social Network Systems." Informatics 8, no. 1 (February 9, 2021): 8. http://dx.doi.org/10.3390/informatics8010008.

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Participatory medicine and e-health help to promote health literacy among non-medical professionals. Users of e-health systems actively participate in a patient social network system (PSNS) to share health information and experiences with other users with similar health conditions. Users’ activities provide valuable healthcare resources to develop effective participatory medicine between patients, caregivers, and medical professionals. This study aims to investigate the factors of patients’ engagement in a PSNS by integrating and modifying an existing behavioral model and information system model (i.e., affective events theory (AET) and self-determination theory (SDT)). The AET is used to model the structure, the affective aspects of the driven behavior, and actual affective manifestation. The SDT is used to model interest and its relations with behavior. The data analysis and model testing are based on structural equation modeling, using responses from 428 users. The results indicate that interest and empathy promote users’ engagement in a PSNS. The findings from this study suggest recommendations to further promote users’ participation in a PSNS from the sociotechnical perspective, which include sensitizing and constructive engagement features. Furthermore, the data generated from a user’s participation in a PSNS could contribute to the study of clinical manifestations of disease, especially an emerging disease.
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Deng, Yu Qiao, and Ge Song. "A Verifiable Visual Cryptography Scheme Using Neural Networks." Advanced Materials Research 756-759 (September 2013): 1361–65. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.1361.

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This paper proposes a new verifiable visual cryptography scheme for general access structures using pi-sigma neural networks (VVCSPSN), which is based on probabilistic signature scheme (PSS), which is considered as security and effective verification method. Compared to other high-order networks, PSN has a highly regular structure, needs a much smaller number of weights and less training time. Using PSNs capability of large-scale parallel classification, VCSPSN reduces the information communication rate greatly, makes best known upper bound polynomial, and distinguishes the deferent information in secret image.
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Greene, L. H. "Protein structure networks." Briefings in Functional Genomics 11, no. 6 (October 4, 2012): 469–78. http://dx.doi.org/10.1093/bfgp/els039.

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Hase, T., Y. Suzuki, S. Ogisima, and 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.

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Dissertations / Theses on the topic "Protein Structure Networks (PSNs)"

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Zhao, Jing. "Protein Structure Prediction Based on Neural Networks." Thèse, Université d'Ottawa / University of Ottawa, 2013. http://hdl.handle.net/10393/23636.

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Proteins are the basic building blocks of biological organisms, and are responsible for a variety of functions within them. Proteins are composed of unique amino acid sequences. Some has only one sequence, while others contain several sequences that are combined together. These combined amino acid sequences fold to form a unique three-dimensional (3D) shape. Although the sequences may fold proteins into different 3D shapes in diverse environments, proteins with similar amino acid sequences typically have similar 3D shapes and functions. Knowledge of the 3D shape of a protein is important in both protein function analysis and drug design, for example when assessing the toxicity reduction associated with a given drug. Due to the complexity of protein 3D shapes and the close relationship between shapes and functions, the prediction of protein 3D shapes has become an important topic in bioinformatics. This research introduces a new approach to predict proteins’ 3D shapes, utilizing a multilayer artificial neural network. Our novel solution allows one to learn and predict the representations of the 3D shape associated with a protein by starting directly from its amino acid sequence descriptors. The input of the artificial neural network is a set of amino acid sequence descriptors we created based on a set of probability density functions. In our algorithm, the probability density functions are calculated by the correlation between the constituent amino acids, according to the substitution matrix. The output layer of the network is formed by 3D shape descriptors provided by an information retrieval system, called CAPRI. This system contains the pose invariant 3D shape descriptors, and retrieves proteins having the closest structures. The network is trained by proteins with known amino acid sequences and 3D shapes. Once the network has been trained, it is able to predict the 3D shape descriptors of the query protein. Based on the predicted 3D shape descriptors, the CAPRI system allows the retrieval of known proteins with 3D shapes closest to the query protein. These retrieved proteins may be verified as to whether they are in the same family as the query protein, since proteins in the same family generally have similar 3D shapes. The search for similar 3D shapes is done against a database of more than 45,000 known proteins. We present the results when evaluating our approach against a number of protein families of various sizes. Further, we consider a number of different neural network architectures and optimization algorithms. When the neural network is trained with proteins that are from large families where the proteins in the same family have similar amino acid sequences, the accuracy for finding proteins from the same family is 100%. When we employ proteins whose family members have dissimilar amino acid sequences, or those from a small protein family, in which case, neural networks with one hidden layer produce more promising results than networks with two hidden layers, and the performance may be improved by increasing the number of hidden nodes when the networks have one hidden layer.
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Zotenko, 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.

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Thesis (Ph. D.) -- University of Maryland, College Park, 2007.
Thesis 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.
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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.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.
This 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.
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Tsilo, Lipontseng Cecilia. "Protein secondary structure prediction using neural networks and support vector machines." Thesis, Rhodes University, 2009. http://hdl.handle.net/10962/d1002809.

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Predicting the secondary structure of proteins is important in biochemistry because the 3D structure can be determined from the local folds that are found in secondary structures. Moreover, knowing the tertiary structure of proteins can assist in determining their functions. The objective of this thesis is to compare the performance of Neural Networks (NN) and Support Vector Machines (SVM) in predicting the secondary structure of 62 globular proteins from their primary sequence. For each NN and SVM, we created six binary classifiers to distinguish between the classes’ helices (H) strand (E), and coil (C). For NN we use Resilient Backpropagation training with and without early stopping. We use NN with either no hidden layer or with one hidden layer with 1,2,...,40 hidden neurons. For SVM we use a Gaussian kernel with parameter fixed at = 0.1 and varying cost parameters C in the range [0.1,5]. 10- fold cross-validation is used to obtain overall estimates for the probability of making a correct prediction. Our experiments indicate for NN and SVM that the different binary classifiers have varying accuracies: from 69% correct predictions for coils vs. non-coil up to 80% correct predictions for stand vs. non-strand. It is further demonstrated that NN with no hidden layer or not more than 2 hidden neurons in the hidden layer are sufficient for better predictions. For SVM we show that the estimated accuracies do not depend on the value of the cost parameter. As a major result, we will demonstrate that the accuracy estimates of NN and SVM binary classifiers cannot distinguish. This contradicts a modern belief in bioinformatics that SVM outperforms other predictors.
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Alistair, 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.

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This 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.

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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.

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Bioinformatics techniques to protein secondary structure prediction mostly depend on the information available in amino acid sequence. Support vector machines (SVM) have shown strong generalization ability in a number of application areas, including protein structure prediction. In this study, a new sliding window scheme is introduced with multiple windows to form the protein data for training and testing SVM. Orthogonal encoding scheme coupled with BLOSUM62 matrix is used to make the prediction. First the prediction of binary classifiers using multiple windows is compared with single window scheme, the results shows single window not to be good in all cases. Two new classifiers are introduced for effective tertiary classification. This new classifiers use neural networks and genetic algorithms to optimize the accuracy of the tertiary classifier. The accuracy level of the new architectures are determined and compared with other studies. The tertiary architecture is better than most available techniques.
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Mulnaes, 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.

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Royer, 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.

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Molecular biology has entered an era of systematic and automated experimentation. High-throughput techniques have moved biology from small-scale experiments focused on specific genes and proteins to genome and proteome-wide screens. One result of this endeavor is the compilation of complex networks of interacting proteins. Molecular biologists hope to understand life's complex molecular machines by studying these networks. This thesis addresses tree open problems centered upon their analysis and quality assessment. First, we introduce power graph analysis as a novel approach to the representation and visualization of biological networks. Power graphs are a graph theoretic approach to lossless and compact representation of complex networks. It groups edges into cliques and bicliques, and nodes into a neighborhood hierarchy. We demonstrate power graph analysis on five examples, and show its advantages over traditional network representations. Moreover, we evaluate the algorithm performance on a benchmark, test the robustness of the algorithm to noise, and measure its empirical time complexity at O (e1.71)- sub-quadratic in the number of edges e. Second, we tackle the difficult and controversial problem of data quality in protein interaction networks. We propose a novel measure for accuracy and completeness of genome-wide protein interaction networks based on network compressibility. We validate this new measure by i) verifying the detrimental effect of false positives and false negatives, ii) showing that gold standard networks are highly compressible, iii) showing that authors' choice of confidence thresholds is consistent with high network compressibility, iv) presenting evidence that compressibility is correlated with co-expression, co-localization and shared function, v) showing that complete and accurate networks of complex systems in other domains exhibit similar levels of compressibility than current high quality interactomes. Third, we apply power graph analysis to networks derived from text-mining as well to gene expression microarray data. In particular, we present i) the network-based analysis of genome-wide expression profiles of the neuroectodermal conversion of mesenchymal stem cells. ii) the analysis of regulatory modules in a rare mitochondrial cytopathy: emph{Mitochondrial Encephalomyopathy, Lactic acidosis, and Stroke-like episodes} (MELAS), and iii) we investigate the biochemical causes behind the enhanced biocompatibility of tantalum compared with titanium.
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Planas, 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.

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Proteins are indispensable players in virtually all biological events. The functions of proteins are determined by their three dimensional (3D) structure and coordinated through intricate networks of protein-protein interactions (PPIs). Hence, a deep comprehension of such networks turns out to be crucial for understanding the cellular biology. Computational approaches have become critical tools for analysing PPI networks. In silico methods take advantage of the existing PPI knowledge to both predict new interactions and predict the function of proteins. Regarding the task of predicting PPIs, several methods have been already developed. However, recent findings demonstrate that such methods could take advantage of the knowledge on non-interacting protein pairs (NIPs). On the task of predicting the function of proteins,the Guilt-by-Association (GBA) principle can be exploited to extend the functional annotation of proteins over PPI networks. In this thesis, a new algorithm for PPI prediction and a protocol to complete cell signalling networks are presented. iLoops is a method that uses NIP data and structural information of proteins to predict the binding fate of protein pairs. A novel protocol for completing signalling networks –a task related to predicting the function of a protein, has also been developed. The protocol is based on the application of GBA principle in PPI networks.
Les 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.
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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/.

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Books on the topic "Protein Structure Networks (PSNs)"

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Protein interaction networks: Computational analysis. Cambridge: Cambridge University Press, 2009.

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Flapan, Erica. Knots, molecules, and the universe: An introduction to topology. Providence, Rhode Island: American Mathematical Society, 2015.

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Mrozek, Dariusz. High-Performance Computational Solutions in Protein Bioinformatics. Springer London, Limited, 2014.

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High-Performance Computational Solutions in Protein Bioinformatics. Springer International Publishing AG, 2014.

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Newman, Mark. Biological networks. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198805090.003.0005.

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A discussion of a range of different kinds of biological networks. The chapter starts with a discussion of biochemical networks such metabolic and protein interaction networks and methods for determining their structure, particularly focusing on high-throughput methods such as the yeast two-hybrid screen. Next is a discussion of neural networks and other networks in the brain, along with measurement techniques such as slice electron microscopy, optical microscopy, transsynaptic tracing, functional MRI, and diffusion MRI. Finally, there is a discussion of ecological networks, and particularly food webs.
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Book chapters on the topic "Protein Structure Networks (PSNs)"

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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.

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Punta, 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.

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Guermeur, 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.

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Zhou, 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.

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Fang, 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.

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Wu, 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.

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Mason, 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.

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Condori, 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.

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Shao, 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.

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Shi, 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.

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Conference papers on the topic "Protein Structure Networks (PSNs)"

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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.

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Bohr, 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.

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Singh, 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.

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Wu, 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.

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Dong 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.

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Babaei, 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.

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Aydin, 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.

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Dzikovska, 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.

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Zhu, 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.

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Zhu, Hongbing, Jun Wu, and Jianguo Wu. "Protein Structure Prediction with Improved Quantum Immune Algorithm." In 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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You might also be interested in the extended bibliographies on the topic 'Protein Structure Networks (PSNs)' for particular source types:
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