Relevant bibliographies by topics / Protein interaction

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Protein interaction'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Protein interaction"

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Sun, Zheng, Shihao Li, Fuhua Li, and Jianhai Xiang. "Bioinformatic Prediction of WSSV-Host Protein-Protein Interaction." BioMed Research International 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/416543.

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WSSV is one of the most dangerous pathogens in shrimp aquaculture. However, the molecular mechanism of how WSSV interacts with shrimp is still not very clear. In the present study, bioinformatic approaches were used to predict interactions between proteins from WSSV and shrimp. The genome data of WSSV (NC_003225.1) and the constructed transcriptome data ofF. chinensiswere used to screen potentially interacting proteins by searching in protein interaction databases, including STRING, Reactome, and DIP. Forty-four pairs of proteins were suggested to have interactions between WSSV and the shrimp.
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DeBlasio, Stacy L., Juan D. Chavez, Mariko M. Alexander, et al. "Visualization of Host-Polerovirus Interaction Topologies Using Protein Interaction Reporter Technology." Journal of Virology 90, no. 4 (2015): 1973–87. http://dx.doi.org/10.1128/jvi.01706-15.

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ABSTRACTDemonstrating direct interactions between host and virus proteins during infection is a major goal and challenge for the field of virology. Most protein interactions are not binary or easily amenable to structural determination. Using infectious preparations of a polerovirus (Potato leafroll virus[PLRV]) and protein interaction reporter (PIR), a revolutionary technology that couples a mass spectrometric-cleavable chemical cross-linker with high-resolution mass spectrometry, we provide the first report of a host-pathogen protein interaction network that includes data-derived, topologica
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Kulharia, Mahesh. "Geometrical and electro-static determinants of protein-protein interactions." Bioinformation 17, no. 10 (2021): 851–60. http://dx.doi.org/10.6026/97320630017851.

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Protein-protein interactions (PPI) are pivotal to the numerous processes in the cell. Therefore, it is of interest to document the analysis of these interactions in terms of binding sites, topology of the interacting structures and physiochemical properties of interacting interfaces and the of forces interactions. The interaction interface of obligatory protein-protein complexes differs from that of the transient interactions. We have created a large database of protein-protein interactions containing over100 thousand interfaces. The structural redundancy was eliminated to obtain a non-redunda
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Yang, Huiying, Yuehua Ke, Jian Wang, et al. "Insight into Bacterial Virulence Mechanisms against Host Immune Response via the Yersinia pestis-Human Protein-Protein Interaction Network." Infection and Immunity 79, no. 11 (2011): 4413–24. http://dx.doi.org/10.1128/iai.05622-11.

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ABSTRACTAYersinia pestis-human protein interaction network is reported here to improve our understanding of its pathogenesis. Up to 204 interactions between 66Y. pestisbait proteins and 109 human proteins were identified by yeast two-hybrid assay and then combined with 23 previously published interactions to construct a protein-protein interaction network. Topological analysis of the interaction network revealed that human proteins targeted byY. pestiswere significantly enriched in the proteins that are central in the human protein-protein interaction network. Analysis of this network showed t
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Han, Ying, Liang Cheng, and Weiju Sun. "Analysis of Protein-Protein Interaction Networks through Computational Approaches." Protein & Peptide Letters 27, no. 4 (2020): 265–78. http://dx.doi.org/10.2174/0929866526666191105142034.

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The interactions among proteins and genes are extremely important for cellular functions. Molecular interactions at protein or gene levels can be used to construct interaction networks in which the interacting species are categorized based on direct interactions or functional similarities. Compared with the limited experimental techniques, various computational tools make it possible to analyze, filter, and combine the interaction data to get comprehensive information about the biological pathways. By the efficient way of integrating experimental findings in discovering PPIs and computational
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Lang, Benjamin, Jae-Seong Yang, Mireia Garriga-Canut, et al. "Matrix-screening reveals a vast potential for direct protein-protein interactions among RNA binding proteins." Nucleic Acids Research 49, no. 12 (2021): 6702–21. http://dx.doi.org/10.1093/nar/gkab490.

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Abstract RNA-binding proteins (RBPs) are crucial factors of post-transcriptional gene regulation and their modes of action are intensely investigated. At the center of attention are RNA motifs that guide where RBPs bind. However, sequence motifs are often poor predictors of RBP-RNA interactions in vivo. It is hence believed that many RBPs recognize RNAs as complexes, to increase specificity and regulatory possibilities. To probe the potential for complex formation among RBPs, we assembled a library of 978 mammalian RBPs and used rec-Y2H matrix screening to detect direct interactions between RB
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Bitbol, Anne-Florence, Robert S. Dwyer, Lucy J. Colwell, and Ned S. Wingreen. "Inferring interaction partners from protein sequences." Proceedings of the National Academy of Sciences 113, no. 43 (2016): 12180–85. http://dx.doi.org/10.1073/pnas.1606762113.

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Specific protein−protein interactions are crucial in the cell, both to ensure the formation and stability of multiprotein complexes and to enable signal transduction in various pathways. Functional interactions between proteins result in coevolution between the interaction partners, causing their sequences to be correlated. Here we exploit these correlations to accurately identify, from sequence data alone, which proteins are specific interaction partners. Our general approach, which employs a pairwise maximum entropy model to infer couplings between residues, has been successfully used to pre
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Hu, Yang, Ying Zhang, Jun Ren, Yadong Wang, Zhenzhen Wang, and Jun Zhang. "Statistical Approaches for the Construction and Interpretation of Human Protein-Protein Interaction Network." BioMed Research International 2016 (2016): 1–7. http://dx.doi.org/10.1155/2016/5313050.

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The overall goal is to establish a reliable human protein-protein interaction network and develop computational tools to characterize a protein-protein interaction (PPI) network and the role of individual proteins in the context of the network topology and their expression status. A novel and unique feature of our approach is that we assigned confidence measure to each derived interacting pair and account for the confidence in our network analysis. We integrated experimental data to infer human PPI network. Our model treated the true interacting status (yes versus no) for any given pair of hum
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Gursoy, Attila, Ozlem Keskin, and Ruth Nussinov. "Topological properties of protein interaction networks from a structural perspective." Biochemical Society Transactions 36, no. 6 (2008): 1398–403. http://dx.doi.org/10.1042/bst0361398.

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Protein–protein interactions are usually shown as interaction networks (graphs), where the proteins are represented as nodes and the connections between the interacting proteins are shown as edges. The graph abstraction of protein interactions is crucial for understanding the global behaviour of the network. In this mini review, we summarize basic graph topological properties, such as node degree and betweenness, and their relation to essentiality and modularity of protein interactions. The classification of hub proteins into date and party hubs with distinct properties has significant implica
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Wang, Tianwen, Ningning Yang, Chen Liang, et al. "Detecting Protein-Protein Interaction Based on Protein Fragment Complementation Assay." Current Protein & Peptide Science 21, no. 6 (2020): 598–610. http://dx.doi.org/10.2174/1389203721666200213102829.

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Proteins are the most critical executive molecules by responding to the instructions stored in the genetic materials in any form of life. More frequently, proteins do their jobs by acting as a roleplayer that interacts with other protein(s), which is more evident when the function of a protein is examined in the real context of a cell. Identifying the interactions between (or amongst) proteins is very crucial for the biochemistry investigation of an individual protein and for the attempts aiming to draw a holo-picture for the interacting members at the scale of proteomics (or protein-protein i
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Dissertations / Theses on the topic "Protein interaction"

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Stylianou, Julianna. "Protein-protein interaction of HSV-1 tegument proteins." Thesis, Imperial College London, 2013. http://hdl.handle.net/10044/1/24663.

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Herpes simplex virus type 1 virions contain a proteinaceous layer between the nucleocapsid and the virus envelope termed the tegument. The mechanism underlying tegumentation remains largely undefined for all herpesviruses, as does the role of many tegument proteins in virus replication. The networks of protein interactions involved in virus assembly have been largely explored and although large-scale studies have been carried out using yeast two hybrid analyses of herpesvirus protein interactions, few of the identified networks have been validated in infected cells. Here, the molecular interac
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Li, Wei. "Protein-protein interaction specificity of immunity proteins for DNase colicins." Thesis, University of East Anglia, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.302033.

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Tarsounas, Madalina Cecilia. "Synaptonemal complex proteins, post-translational modifications, protein-protein interactions and interaction with the rad51/dmc1 recombinases." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape7/PQDD_0007/NQ39313.pdf.

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McDowall, Mark. "Human protein-protein interaction prediction." Thesis, University of Dundee, 2011. https://discovery.dundee.ac.uk/en/studentTheses/697e465a-edbd-41d2-acda-5910a49e4157.

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Protein-protein interactions are essential for the survival of all living cells, allowing for processes such as cell signalling, metabolism and cell division to occur. Yet in humans there are only >38k annotated interactions of an interactome estimated to range between 150k to 600k interactions and out of a potential 300M protein pairs.Experimental methods to define the human interactome generate high quality results, but are expensive and slow. Computational methods play an important role to fill the gap.To further this goal, the prediction of human protein-protein interactions was investi
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Ospina, Forero Luis Eduardo. "Modelling protein-protein interaction networks." Thesis, University of Oxford, 2017. https://ora.ox.ac.uk/objects/uuid:c5150074-e160-4c59-8c23-f8804ae3dd2e.

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Proteins, the main motors of the cell, are in charge of performing a diverse array of biological functions. They rarely perform those functions alone, but generally work as groups of proteins that through a complex array of interactions perform a single biological function. These complex interactions between different proteins are often analysed via network theory, where a protein-protein interaction (PPI) network is created considering each protein as a node and each of their interactions as edges. Different approaches from the perspective of network analysis have been proposed to describe, a
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Wang, Chu. "Improved conformational sampling for protein-protein docking /." Thesis, Connect to this title online; UW restricted, 2007. http://hdl.handle.net/1773/9194.

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García-García, Javier 1982. "Protein-protein interaction network : management of databases and its applications on the computational study of protein-protein interactions." Doctoral thesis, Universitat Pompeu Fabra, 2015. http://hdl.handle.net/10803/286512.

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The use of protein-protein interaction networks has become crucial due to the emergence of systems biology. The completeness and quality of networks, crucial to understand the biochemical mechanisms underlying a system such as a cell, are still challenging the scientific community. This thesis focuses on the data completeness challenge by the development of flexible tools for biological data management. It presents a database framework, BIANA, in which the integrated access to several information sources tackles this problem by unraveling hidden biological associations. BIANA is used to develo
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Lam, Wai Kwan. "Investigation of interaction between solube adenylyl cyclase and p34SEI-1 /." View abstract or full-text, 2010. http://library.ust.hk/cgi/db/thesis.pl?BIOL%202010%20LAM.

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Johnson, David H. "High-throughput self-interaction chromatography applications in formulation prediction for proteins /." Birmingham, Ala. : University of Alabama at Birmingham, 2008. https://www.mhsl.uab.edu/dt/2009r/johnson.pdf.

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Thesis (M.S.)--University of Alabama at Birmingham, 2008.<br>Title from PDF title page (viewed Sept. 21, 2009). Additional advisors: Martha W. Bidez, W. Michael Carson, Richard A. Gray, W. William Wilson. Includes bibliographical references.
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Nam, Hye In. "Multiplexed fragmentation and protein interaction reporter technology application to human cells." Pullman, Wash. : Washington State University, 2009. http://www.dissertations.wsu.edu/Thesis/Summer2009/h_nam_071509.pdf.

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Thesis (M.S. in Chemistry)--Washington State University, August 2009.<br>Title from PDF title page (viewed on Sept. 21, 2009). "Department of Chemistry." Includes bibliographical references (p. 60-66).
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Books on the topic "Protein interaction"

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Werther, Meike, and Harald Seitz, eds. Protein – Protein Interaction. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-68820-4.

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Roy, Siddhartha, and Haian Fu, eds. Protein–Protein Interaction Regulators. Royal Society of Chemistry, 2020. http://dx.doi.org/10.1039/9781788016544.

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Canzar, Stefan, and Francisca Rojas Ringeling, eds. Protein-Protein Interaction Networks. Springer US, 2020. http://dx.doi.org/10.1007/978-1-4939-9873-9.

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Joël, Janin, and Wodak Shoshana J, eds. Protein modules and protein-protein interaction. Academic Press, 2002.

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Clarke, Adrienne E., and Ian A. Wilson, eds. Carbohydrate-Protein Interaction. Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-46641-0.

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E, Clarke A., and Wilson I. A, eds. Carbohydrate-protein interaction. Springer-Verlag, 1988.

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Haynes, Susan R. RNA-Protein Interaction Protocols. Humana Press, 1999. http://dx.doi.org/10.1385/1592596762.

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Lin, Ren-Jang, ed. RNA-Protein Interaction Protocols. Humana Press, 2008. http://dx.doi.org/10.1007/978-1-60327-475-3.

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Saenger, Wolfram, and Udo Heinemann, eds. Protein-Nucleic Acid Interaction. Macmillan Education UK, 1989. http://dx.doi.org/10.1007/978-1-349-09871-2.

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Raman, Jai, ed. RNA-Protein Interaction Protocols. Springer London, 2008. http://dx.doi.org/10.1007/978-1-84800-104-6.

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Book chapters on the topic "Protein interaction"

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Martin, Shawn, W. Michael Brown, and Jean-Loup Faulon. "Using Product Kernels to Predict Protein Interactions." In Protein – Protein Interaction. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/10_2007_084.

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Pitre, Sylvain, Md Alamgir, James R. Green, Michel Dumontier, Frank Dehne, and Ashkan Golshani. "Computational Methods For Predicting Protein–Protein Interactions." In Protein – Protein Interaction. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/10_2007_089.

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Chan, Catherine S., Tara M. L. Winstone, and Raymond J. Turner. "Investigating Protein–Protein Interactions by Far-Westerns." In Protein – Protein Interaction. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/10_2007_090.

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Abu-Farha, Mohamed, Fred Elisma, and Daniel Figeys. "Identification of Protein–Protein Interactions by Mass Spectrometry Coupled Techniques." In Protein – Protein Interaction. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/10_2007_091.

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Guan, Hongtao, and Endre Kiss-Toth. "Advanced Technologies for Studies on Protein Interactomes." In Protein – Protein Interaction. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/10_2007_092.

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Shin, Sung-Young, Sang-Mok Choo, Sun-Hee Woo, and Kwang-Hyun Cho. "Cardiac Systems Biology and Parameter Sensitivity Analysis: Intracellular Ca2+ Regulatory Mechanisms in Mouse Ventricular Myocytes." In Protein – Protein Interaction. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/10_2007_093.

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Beutling, Ulrike, Kai Städing, Theresia Stradal, and Ronald Frank. "Large-Scale Analysis of Protein–Protein Interactions Using Cellulose-Bound Peptide Arrays." In Protein – Protein Interaction. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/10_2008_096.

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Zhu, Yonggang, and Barbara E. Power. "Lab-on-a-chip in Vitro Compartmentalization Technologies for Protein Studies." In Protein – Protein Interaction. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/10_2008_098.

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Korf, Ulrike, Frauke Henjes, Christian Schmidt, et al. "Antibody Microarrays as an Experimental Platform for the Analysis of Signal Transduction Networks." In Protein – Protein Interaction. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/10_2008_101.

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Chappell, Thomas G., and Phillip N. Gray. "Protein Interactions: Analysis Using Allele Libraries." In Protein – Protein Interaction. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/10_2008_102.

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Conference papers on the topic "Protein interaction"

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Malviya, Bhupendra, Yigitaliyev Alisher, Mahima Sharma, Rajalakshmi G, Layth Hussein, and G. Yaswitha. "BERT-based Models for Predicting Protein-Protein Interaction Sites." In 2024 IEEE International Conference on Communication, Computing and Signal Processing (IICCCS). IEEE, 2024. http://dx.doi.org/10.1109/iicccs61609.2024.10763870.

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Alkhateeb, Noor Jamal, and Mamoun Awad. "Protein-Protein Interaction Sites Prediction Using Graph Convolutional Networks." In 2024 International Conference on Computer and Applications (ICCA). IEEE, 2024. https://doi.org/10.1109/icca62237.2024.10928145.

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Zhang, Zitong, Zhixian Wang, Lingling Zhao, Junjie Wang, and Chunyu Wang. "Multimodal Contrastive Learning for Protein–Protein Interaction Inhibitor Prediction." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10822227.

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Wang, Jianxin, Wei Peng, Yingjiao Chen, Yu Lu, and Yi Pan. "Identifying essential proteins based on protein domains in protein-protein interaction networks." In 2013 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2013. http://dx.doi.org/10.1109/bibm.2013.6732476.

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Doong, Shing, and Shu-Fen Hong. "Protein-Protein Interaction Document Mining." In 9th Joint Conference on Information Sciences. Atlantis Press, 2006. http://dx.doi.org/10.2991/jcis.2006.250.

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Bakar, Sakhinah Abu, Javid Taheri, and Albert Y. Zomaya. "Identifying Hub Proteins and Their Essentiality from Protein-protein Interaction Network." In Bioengineering (BIBE). IEEE, 2011. http://dx.doi.org/10.1109/bibe.2011.67.

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Lv, Guofeng, Zhiqiang Hu, Yanguang Bi, and Shaoting Zhang. "Learning Unknown from Correlations: Graph Neural Network for Inter-novel-protein Interaction Prediction." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/506.

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The study of multi-type Protein-Protein Interaction (PPI) is fundamental for understanding biological processes from a systematic perspective and revealing disease mechanisms. Existing methods suffer from significant performance degradation when tested in unseen dataset. In this paper, we investigate the problem and find that it is mainly attributed to the poor performance for inter-novel-protein interaction prediction. However, current evaluations overlook the inter-novel-protein interactions, and thus fail to give an instructive assessment. As a result, we propose to address the problem from
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Sun, Dengdi, and Maolin Hu. "Determining Protein Function by Protein-Protein Interaction Network." In 2007 1st International Conference on Bioinformatics and Biomedical Engineering. IEEE, 2007. http://dx.doi.org/10.1109/icbbe.2007.12.

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Shatnawi, Maad. "Protein-Protein Interaction Prediction: Recent Advances." In 2017 28th International Workshop on Database and Expert Systems Applications (DEXA). IEEE, 2017. http://dx.doi.org/10.1109/dexa.2017.30.

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Xu, Liangliang, and Fei Zhu. "Protein protein interaction visualization using VisANT." In 2011 International Conference on Computer Science and Service System (CSSS). IEEE, 2011. http://dx.doi.org/10.1109/csss.2011.5973926.

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Reports on the topic "Protein interaction"

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Wilding, George. Novel Inhibitors of Protein-Protein Interaction for Prostate Cancer Therapy. Defense Technical Information Center, 2014. http://dx.doi.org/10.21236/ada606109.

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Wilding, George. Novel Inhibitors of Protein-Protein Interaction for Prostate Cancer Therapy. Defense Technical Information Center, 2013. http://dx.doi.org/10.21236/ada580368.

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Wilding, George. Novel Inhibitors of Protein-Protein Interaction for Prostate Cancer Therapy. Defense Technical Information Center, 2011. http://dx.doi.org/10.21236/ada544659.

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Wilding, George. Novel Inhibitors of Protein-Protein Interaction for Prostate Cancer Therapy. Defense Technical Information Center, 2012. http://dx.doi.org/10.21236/ada560523.

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Beers, Eric, Amy Brunner, Richard Helm, and Allan Dickerman. Towards a map of the Populus biomass protein-protein interaction network. Office of Scientific and Technical Information (OSTI), 2015. http://dx.doi.org/10.2172/1312890.

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Hsieh, John C. F., Robert L. Jernigan, and Susan J. Lamont. Host-Pathogen Protein-Protein Interaction Prediction Using an in silico Model. Iowa State University, 2016. http://dx.doi.org/10.31274/ans_air-180814-224.

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Citovsky, Vitaly, and Yedidya Gafni. Suppression of RNA Silencing by TYLCV During Viral Infection. United States Department of Agriculture, 2009. http://dx.doi.org/10.32747/2009.7592126.bard.

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The Israeli isolate of Tomato yellow leaf curl geminivirus (TYLCV-Is) is a major tomato pathogen, causing extensive (up to 100%) crop losses in Israel and in the south-eastern U.S. (e.g., Georgia, Florida). Surprisingly, however, little is known about the molecular mechanisms of TYLCV-Is interactions with tomato cells. In the current BARD project, we have identified a TYLCV-Is protein, V2, which acts as a suppressor of RNA silencing, and showed that V2 interacts with the tomato (L. esculentum) member of the SGS3 (LeSGS3) protein family known to be involved in RNA silencing. This proposal will
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Silver, Pamela A. Genome-Wide Nucleic Acid/Protein Interaction in Breast Cancer. Defense Technical Information Center, 2005. http://dx.doi.org/10.21236/ada435620.

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Burma, Sandeep. Interaction of BRCA1 with the DNA-Dependent Protein Kinase. Defense Technical Information Center, 2005. http://dx.doi.org/10.21236/ada486717.

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Silver, Pamela A. Genome-Wide Nucleic Acid/Protein Interaction in Breast Cancer. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada426138.

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