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Relevant bibliographies by topics / Subcellular Localization

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Academic literature on the topic 'Subcellular Localization'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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Journal articles on the topic "Subcellular Localization"

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Han, Guo-Sheng, and Zu-Guo Yu. "ML-rRBF-ECOC: A Multi-Label Learning Classifier for Predicting Protein Subcellular Localization with Both Single and Multiple Sites." Current Proteomics 16, no. 5 (2019): 359–65. http://dx.doi.org/10.2174/1570164616666190103143945.

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Background: The subcellular localization of a protein is closely related with its functions and interactions. More and more evidences show that proteins may simultaneously exist at, or move between, two or more different subcellular localizations. Therefore, predicting protein subcellular localization is an important but challenging problem. Observation: Most of the existing methods for predicting protein subcellular localization assume that a protein locates at a single site. Although a few methods have been proposed to deal with proteins with multiple sites, correlations between subcellular

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Wu, Ze Yue, and Yue Hui Chen. "Predicting Protein Subcellular Localization Using the Algorithm of Diversity Finite Coefficient Combined with Artificial Neural Network." Advanced Materials Research 756-759 (September 2013): 3760–65. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.3760.

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Protein subcellular localization is an important research field of bioinformatics. The subcellular localization of proteins classification problem is transformed into several two classification problems with error-correcting output codes. In this paper, we use the algorithm of the increment of diversity combined with artificial neural network to predict protein in SNL6 which has six subcelluar localizations. The prediction ability was evaluated by 5-jackknife cross-validation. Its predicted result is 81.3%. By com-paring its results with other methods, it indicates the new approach is feasible

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Zhang, Yu-Hang, ShiJian Ding, Lei Chen, Tao Huang, and Yu-Dong Cai. "Subcellular Localization Prediction of Human Proteins Using Multifeature Selection Methods." BioMed Research International 2022 (September 12, 2022): 1–12. http://dx.doi.org/10.1155/2022/3288527.

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Subcellular localization attempts to assign proteins to one of the cell compartments that performs specific biological functions. Finding the link between proteins, biological functions, and subcellular localization is an effective way to investigate the general organization of living cells in a systematic manner. However, determining the subcellular localization of proteins by traditional experimental approaches is difficult. Here, protein–protein interaction networks, functional enrichment on gene ontology and pathway, and a set of proteins having confirmed subcellular localization were appl

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Wang, Xiao, Lixiang Yang, and Rong Wang. "DRpred: A Novel Deep Learning-Based Predictor for Multi-Label mRNA Subcellular Localization Prediction by Incorporating Bayesian Inferred Prior Label Relationships." Biomolecules 14, no. 9 (2024): 1067. http://dx.doi.org/10.3390/biom14091067.

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The subcellular localization of messenger RNA (mRNA) not only helps us to understand the localization regulation of gene expression but also helps to understand the relationship between RNA localization pattern and human disease mechanism, which has profound biological and medical significance. Several predictors have been proposed for predicting the subcellular localization of mRNA. However, there is still considerable room for improvement in their predictive performance, especially regarding multi-label prediction. This study proposes a novel multi-label predictor, DRpred, for mRNA subcellul

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Aßfalg, Johannes, Jing Gong, Hans-Peter Kriegel, Alexey Pryakhin, Tiandi Wei, and Arthur Zimek. "Investigating a Correlation between Subcellular Localization and Fold of Proteins." JUCS - Journal of Universal Computer Science 16, no. (5) (2010): 604–21. https://doi.org/10.3217/jucs-016-05-0604.

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When considering the prediction of a structural class for a protein as a classificationproblem, usually a classifier is based on a feature vector x ∊ ℝn, where the features represent certain attributes of the primary sequence or derived properties (e.g., the predicted secondary structure) of a given protein. Since the structure of a protein (i.e., its native conformation) is stable only under specific environmental conditions, it is commonly accepted to assume proteins being evolutionarily adapted to specific subcellular localizations and according to their physicochemical environment. Our sta

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Wu, Ze Yue, and Yue Hui Chen. "Predicting Protein Subcellular Localization Using the Algorithm of Increment of Diversity Combined with Weighted K-Nearest Neighbor." Advanced Materials Research 765-767 (September 2013): 3099–103. http://dx.doi.org/10.4028/www.scientific.net/amr.765-767.3099.

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Protein subcellular localization is an important research field of bioinformatics. In this paper, we use the algorithm of the increment of diversity combined with weighted K nearest neighbor to predict protein in SNL6 which has six subcelluar localizations and SNL9 which has nine subcelluar localizations. We use the increment of diversity to extract diversity finite coefficient as new features of proteins. And the basic classifier is weighted K-nearest neighbor. The prediction ability was evaluated by 5-jackknife cross-validation. Its predicted result is 83.3% for SNL6 and 87.6 % for SNL9. By

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Lin, Yang, Xiaoyong Pan, and Hong-Bin Shen. "lncLocator 2.0: a cell-line-specific subcellular localization predictor for long non-coding RNAs with interpretable deep learning." Bioinformatics 37, no. 16 (2021): 2308–16. http://dx.doi.org/10.1093/bioinformatics/btab127.

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Abstract Motivation Long non-coding RNAs (lncRNAs) are generally expressed in a tissue-specific way, and subcellular localizations of lncRNAs depend on the tissues or cell lines that they are expressed. Previous computational methods for predicting subcellular localizations of lncRNAs do not take this characteristic into account, they train a unified machine learning model for pooled lncRNAs from all available cell lines. It is of importance to develop a cell-line-specific computational method to predict lncRNA locations in different cell lines. Results In this study, we present an updated cel

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Luo, Haiwei. "Predicted Protein Subcellular Localization in Dominant Surface Ocean Bacterioplankton." Applied and Environmental Microbiology 78, no. 18 (2012): 6550–57. http://dx.doi.org/10.1128/aem.01406-12.

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ABSTRACTBacteria consume dissolved organic matter (DOM) through hydrolysis, transport and intracellular metabolism, and these activities occur in distinct subcellular localizations. Bacterial protein subcellular localizations for several major marine bacterial groups were predicted using genomic, metagenomic and metatranscriptomic data sets following modification of MetaP software for use with partial gene sequences. The most distinct pattern of subcellular localization was found forBacteroidetes, whose genomes were substantially enriched with outer membrane and extracellular proteins but depl

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Hao, Zhiming, Xiaohua Li, Taidong Qiao, Rui Du, Guoyun Zhang, and Daiming Fan. "Subcellular Localization of CIAPIN1." Journal of Histochemistry & Cytochemistry 54, no. 12 (2006): 1437–44. http://dx.doi.org/10.1369/jhc.6a6960.2006.

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Anastasio, A., AL Carillo, M. Ciccareli, B. Trimarco, G. Iaccarino, and D. Sorriento. "P440Targeting subcellular GRK2 localization." Cardiovascular Research 103, suppl 1 (2014): S81.2—S81. http://dx.doi.org/10.1093/cvr/cvu091.119.

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Dissertations / Theses on the topic "Subcellular Localization"

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Schlehe, Julia. "Folding, function and subcellular localization of parkin." Diss., kostenfrei, 2008. http://edoc.ub.uni-muenchen.de/9266/.

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Winsnes, Casper. "Automatic Subcellular Protein Localization Using Deep Neural Networks." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-189991.

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Protein localization is an important part in understanding the functionality of a protein. The current method of localizing proteins is to manually annotate microscopy images. This thesis investigates the feasibility of using deep artificial neural networks to automatically classify subcellular protein locations based on immunoflourescent images. We investigate the applicability in both single-label and multi-label classification, as well as cross cell line classification. We show that deep single-label neural networks can be used for protein localization with up to 73% accuracy. We also show

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Hogenboom, Sietske. "Subcellular localization of the human isoprenoid biosynthesis pathway." [S.l. : Amsterdam : s.n.] ; Universiteit van Amsterdam [Host], 2004. http://dare.uva.nl/document/76236.

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Hogan, Angela. "Syntrophin regulates the subcellular localization of diacylglycerol kinase-zeta." Thesis, University of Ottawa (Canada), 2003. http://hdl.handle.net/10393/26490.

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Syntrophins are scaffolding proteins that link signaling molecules to the dystrophin protein complex at the plasma membrane. To further understand the roles of syntrophins a yeast two-hybrid screen of a human brain cDNA library was done using the PDZ domain of the recently identified brain-specific gamma 1-syntrophin, an isoform for which no signaling ligands had yet been identified. This screen yielded ten overlapping clones coding for the C-terminal portion of diacylglycerol kinase-zeta (DGK-zeta), a kinase that phosphorylates the membrane lipid diacylglycerol (DAG) to phosphatidic acid (PA)

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Laquian, Ian R. "Inquiries into the subcellular localization of the glucocorticoid receptor." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape2/PQDD_0018/MQ48162.pdf.

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Manchen, Steven T. "Characterization and subcellular localization of the human BAT3 protein." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp05/MQ62248.pdf.

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Svartz, Jesper. "Leukotriene C₄ synthase : studies on oligomerization and subcellular localization /." Linköping : Univ, 2005. http://www.bibl.liu.se/liupubl/disp/disp2005/med913s.pdf.

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Vargas-Vallejo, Leonardo. "Subcellular localization and signaling of Bruton's tyrosine kinase (Btk) /." Stockholm, 2002. http://diss.kib.ki.se/2002/91-7349-344-9/.

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Stadler, Charlotte. "Towards subcellular localization of the human proteome using bioimaging." Doctoral thesis, KTH, Proteomik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-103616.

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Since the publication of the complete sequence of the human genome in 2003 there has been great interest in exploring the functions of the proteins encoded by the genes. To reveal the function of each and every protein, investigation of protein localization at the subcellular level has become a central focus in this research area, since the localization and function of a protein is closely related. The objective of the studies presented in this doctoral thesis was to systematically explore the human proteome at the subcellular level using bioimaging and to develop techniques for validation of

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Mullins, M. "Subcellular localization of metals in metal tolerant higher plants." Thesis, University of Liverpool, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.384378.

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Books on the topic "Subcellular Localization"

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Richter, Dietmar, ed. Cell Polarity and Subcellular RNA Localization. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-540-40025-7.

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author, Mak M. W., ed. Machine learning for protein subcellular localization prediction. De Gruyter, 2015.

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Kain, James Spencer. Subcellular localization and compartmentalization of the ClpP proteases in B. subtilis. Harvard University, 2008.

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Wong, Peggy Pui Chi. Cloning of SNAP-23, its tissue distribution and subcellular localization in non-neural cells. National Library of Canada = Bibliothèque nationale du Canada, 1999.

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Lee, Si Tuen. Understanding the role of Bcl-2 subcellular localization in the inhibition of apoptosis in rat-1 fibroblasrs. National Library of Canada, 1998.

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Cell Polarity and Subcellular RNA Localization. Island Press, 2001.

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Richter, Dietmar. Cell Polarity and Subcellular RNA Localization. Springer London, Limited, 2012.

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Creasy, Leroy L. Cellular and Subcellular Localization in Plant Metabolism. Springer, 2013.

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Creasy, Leroy L. Cellular and Subcellular Localization in Plant Metabolism. Springer, 2013.

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Creasy, Leroy L. Cellular and Subcellular Localization in Plant Metabolism. Springer, 2013.

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Book chapters on the topic "Subcellular Localization"

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Bross, Peter. "Subcellular Localization." In SpringerBriefs in Molecular Science. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-26088-4_10.

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Quinn, Peter J. "Localization of Vitamin E in Membranes." In Subcellular Biochemistry. Springer US, 1998. http://dx.doi.org/10.1007/978-1-4899-1789-8_14.

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Gil, Anabel, José I. López, and Rafael Pulido. "Assessing PTEN Subcellular Localization." In PTEN. Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4939-3299-3_12.

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Barberis, Elettra, Emilio Marengo, and Marcello Manfredi. "Protein Subcellular Localization Prediction." In Methods in Molecular Biology. Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-1641-3_12.

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Van Kuilenburg, André B. P., Henk Van Lenthe, Ronald J. A. Wanders, and Albert H. Van Gennip. "Subcellular Localization of Dihydropyrimidine Dehydrogenase." In Advances in Experimental Medicine and Biology. Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5381-6_157.

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Nakai, Kenta, and Paul Horton. "Computational Prediction of Subcellular Localization." In Protein Targeting Protocols. Humana Press, 2007. http://dx.doi.org/10.1007/978-1-59745-466-7_29.

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Lim, Wendell A., and Bruce J. Mayer. "Subcellular Localization of Signaling Molecules." In Cell Signaling, 2nd edition, 2nd ed. CRC Press, 2024. http://dx.doi.org/10.1201/9780429298844-5.

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Lee, Han Nim, Hyera Jung, and Taijoon Chung. "Subcellular Localization of PI3P in Arabidopsis." In Methods in Molecular Biology. Springer US, 2020. http://dx.doi.org/10.1007/978-1-0716-0767-1_10.

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Boyce, Brendan F. "Cellular and subcellular localization of aluminum." In Aluminum and renal failure. Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-009-1868-9_12.

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Linz, John E., Josephine M. Wee, and Ludmila V. Roze. "Aflatoxin Biosynthesis: Regulation and Subcellular Localization." In Fungal Biology. Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-1191-2_5.

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Conference papers on the topic "Subcellular Localization"

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Yu, Liang, Xiang Wu, Yan Li, Zhaoyang Huang, and Ziqi Liu. "MTTSCL: protein subcellular localization prediction using multi-task learning." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10821950.

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Zhao, Wenhui, Yixin Zhong, Yi Cao, Wenxing He, Yaou Zhao, and Yuehui Chen. "MSADeepLoc: Subcellular Localization Prediction Using MSA and Protein Language Model." In 2024 7th International Conference on Algorithms, Computing and Artificial Intelligence (ACAI). IEEE, 2024. https://doi.org/10.1109/acai63924.2024.10899712.

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Kong, Ge, Yuanhao Fan, Jianing Wang, and Zhao Yang. "Messenger RNA Subcellular Localization Prediction via Large Language Models and Attention Mechanisms." In 2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2024. https://doi.org/10.1109/smc54092.2024.10831363.

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Lu, Yufei. "CRULoc: A protein subcellular localization prediction model based on BiGRU and CNN." In 2024 7th International Conference on Computer Information Science and Application Technology (CISAT). IEEE, 2024. http://dx.doi.org/10.1109/cisat62382.2024.10695442.

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Juan, Eric Y. T., J. H. Chang, C. H. Li, and B. Y. Chen. "Methods for Protein Subcellular Localization Prediction." In 2011 International Conference on Complex, Intelligent and Software Intensive Systems (CISIS). IEEE, 2011. http://dx.doi.org/10.1109/cisis.2011.91.

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HORTON, PAUL, KEUN-JOON PARK, TAKESHI OBAYASHI, and KENTA NAKAI. "PROTEIN SUBCELLULAR LOCALIZATION PREDICTION WITH WOLF PSORT." In 4th Asia-Pacific Bioinformatics Conference. PUBLISHED BY IMPERIAL COLLEGE PRESS AND DISTRIBUTED BY WORLD SCIENTIFIC PUBLISHING CO., 2005. http://dx.doi.org/10.1142/9781860947292_0007.

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Zhao, Qing, Na Li, and Li Fang. "Prediction of Multi-site Protein Subcellular Localization." In 2020 IEEE Conference on Telecommunications, Optics and Computer Science (TOCS). IEEE, 2020. http://dx.doi.org/10.1109/tocs50858.2020.9339688.

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Mohapatra, Debasis, Sayoni Das, Lopamudra Pattnaik, Swati Meher, Rakshanda Khan, and Subramanyam Sahoo. "Evaluation of Standard Classifiers for Protein Subcellular Localization." In 2020 International Conference on Computer Science, Engineering and Applications (ICCSEA). IEEE, 2020. http://dx.doi.org/10.1109/iccsea49143.2020.9132843.

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Schneckenburger, Herbert, Reinhard Sailer, Michael H. Gschwend, et al. "Uptake, subcellular localization, and phototoxicity of photosensitizing porphyrins." In BiOS Europe '95, edited by Benjamin Ehrenberg, Giulio Jori, and Johan Moan. SPIE, 1996. http://dx.doi.org/10.1117/12.230975.

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AßFALG, JOHANNES, JING GONG, HANS-PETER KRIEGEL, ALEXEY PRYAKHIN, TIANDI WEI, and ARTHUR ZIMEK. "SUPERVISED ENSEMBLES OF PREDICTION METHODS FOR SUBCELLULAR LOCALIZATION." In The 6th Asia-Pacific Bioinformatics Conference. PUBLISHED BY IMPERIAL COLLEGE PRESS AND DISTRIBUTED BY WORLD SCIENTIFIC PUBLISHING CO., 2007. http://dx.doi.org/10.1142/9781848161092_0006.

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Reports on the topic "Subcellular Localization"

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Lapidot, Moshe, and Vitaly Citovsky. molecular mechanism for the Tomato yellow leaf curl virus resistance at the ty-5 locus. United States Department of Agriculture, 2016. http://dx.doi.org/10.32747/2016.7604274.bard.

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Tomato yellow leaf curl virus (TYLCV) is a major pathogen of tomato that causes extensive crop loss worldwide, including the US and Israel. Genetic resistance in the host plant is considered highly effective in the defense against viral infection in the field. Thus, the best way to reduce yield losses due to TYLCV is by breeding tomatoes resistant or tolerant to the virus. To date, only six major TYLCV-resistance loci, termed Ty-1 to Ty-6, have been characterized and mapped to the tomato genome. Among tomato TYLCV-resistant lines containing these loci, we have identified a major recessive quan

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Steffens, John, Eithan Harel, and Alfred Mayer. Coding, Expression, Targeting, Import and Processing of Distinct Polyphenoloxidases in Tissues of Higher Plants. United States Department of Agriculture, 1994. http://dx.doi.org/10.32747/1994.7613008.bard.

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Polyphenol oxidase (PPO) catalyzes the oxidation of phenols to quinones at the expense of O2. PPOs are ubiquitous in higer plants, and their role in oxidative browning of plant tissues causes large annual losses to food production. Despite the importance of PPOs to agriculture, the function(s) of PPOs in higher plants are not understood. Among other roles, PPOs have been proposed to participate in aspects of chloroplast metabolism, based on their occurrence in plastids and high Km for O2. Due to the ability of PPO to catalyze formation of highly reactive quinones, PPOs have also been proposed

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Chamovitz, Daniel A., and Albrecht G. Von Arnim. eIF3 Complexes and the eIF3e Subunit in Arabidopsis Development and Translation Initiation. United States Department of Agriculture, 2009. http://dx.doi.org/10.32747/2009.7696545.bard.

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The original working hypothesis of our proposal was that The “e” subunit of eIF3 has multiple functions from both within the nucleus and in the cytoplasm. Within this model, we further hypothesized that the “e” subunit of eIF3 functions in translation as a repressor. We proposed to test these hypotheses along the following specific aims: 1) Determine the subcellular localization of the interaction between eIF3e and other eIF3 subunits, or the COP9 signalosome. 2) Elucidate the biological significance of the varied subcellular localizations of eIF3e through generating Arabidopsis eIF3e alleles

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Fromm, A., Avihai Danon, and Jian-Kang Zhu. Genes Controlling Calcium-Enhanced Tolerance to Salinity in Plants. United States Department of Agriculture, 2003. http://dx.doi.org/10.32747/2003.7585201.bard.

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The specific objectives of the proposed research were to identify, clone and characterize downstream cellular target(s) of SOS3 in Arabidopsis thaliana, to analyze the Ca2+-binding characteristics of SOS3 and the sos3-1 mutant and their interactions with SOS3 cellular targets to analyze the SOS3 cell-specific expression patterns, and its subcellular localization, and to assess the in vivo role of SOS3 target protein(s) in plant tolerance to salinity stress. In the course of the study, in view of recent opportunities in identifying Ca2+ - responsive genes using microarrays, the group at Weizman

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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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Sessa, Guido, and Gregory B. Martin. molecular link from PAMP perception to a MAPK cascade associated with tomato disease resistance. United States Department of Agriculture, 2012. http://dx.doi.org/10.32747/2012.7597918.bard.

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The research problem: The detection of pathogen-associated molecular patterns (PAMPs) by plant pattern recognition receptors (PRRs) is a key mechanism by which plants activate an effective immune response against pathogen attack. MAPK cascades are important signaling components downstream of PRRs that transduce the PAMP signal to activate various defense responses. Preliminary experiments suggested that the receptor-like cytoplasmickinase (RLCK) Mai5 plays a positive role in pattern-triggered immunity (PTI) and interacts with the MAPKKK M3Kε. We thus hypothesized that Mai5, as other RLCKs, fun

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