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Journal articles on the topic 'Subcellular localization prediction'

Author: Grafiati

Published: 4 June 2021

Last updated: 15 February 2022

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1

Lertampaiporn, Supatcha, Sirapop Nuannimnoi, Tayvich Vorapreeda, Nipa Chokesajjawatee, Wonnop Visessanguan, and Chinae Thammarongtham. "PSO-LocBact: A Consensus Method for Optimizing Multiple Classifier Results for Predicting the Subcellular Localization of Bacterial Proteins." BioMed Research International 2019 (November 19, 2019): 1–11. http://dx.doi.org/10.1155/2019/5617153.

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Several computational approaches for predicting subcellular localization have been developed and proposed. These approaches provide diverse performance because of their different combinations of protein features, training datasets, training strategies, and computational machine learning algorithms. In some cases, these tools may yield inconsistent and conflicting prediction results. It is important to consider such conflicting or contradictory predictions from multiple prediction programs during protein annotation, especially in the case of a multiclass classification problem such as subcellular localization. Hence, to address this issue, this work proposes the use of the particle swarm optimization (PSO) algorithm to combine the prediction outputs from multiple different subcellular localization predictors with the aim of integrating diverse prediction models to enhance the final predictions. Herein, we present PSO-LocBact, a consensus classifier based on PSO that can be used to combine the strengths of several preexisting protein localization predictors specially designed for bacteria. Our experimental results indicate that the proposed method can resolve inconsistency problems in subcellular localization prediction for both Gram-negative and Gram-positive bacterial proteins. The average accuracy achieved on each test dataset is over 98%, higher than that achieved with any individual predictor.

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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 (July 2, 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 localization are not efficiently taken into account. In this paper, we propose an integrated method for predicting protein subcellular localizations with both single site and multiple sites. Methods: Firstly, we extend the Multi-Label Radial Basis Function (ML-RBF) method to the regularized version, and augment the first layer of ML-RBF to take local correlations between subcellular localization into account. Secondly, we embed the modified ML-RBF into a multi-label Error-Correcting Output Codes (ECOC) method in order to further consider the subcellular localization dependency. We name our method ML-rRBF-ECOC. Finally, the performance of ML-rRBF-ECOC is evaluated on three benchmark datasets. Results: The results demonstrate that ML-rRBF-ECOC has highly competitive performance to the related multi-label learning method and some state-of-the-art methods for predicting protein subcellular localizations with multiple sites. Considering dependency between subcellular localizations can contribute to the improvement of prediction performance. Conclusion: This also indicates that correlations between different subcellular localizations really exist. Our method at least plays a complementary role to existing methods for predicting protein subcellular localizations with multiple sites.

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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 and effective.

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4

Yu, Chin-Sheng, Yu-Ching Chen, Chih-Hao Lu, and Jenn-Kang Hwang. "Prediction of protein subcellular localization." Proteins: Structure, Function, and Bioinformatics 64, no. 3 (June 2, 2006): 643–51. http://dx.doi.org/10.1002/prot.21018.

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5

Yang, Xiao-Fei, Yuan-Ke Zhou, Lin Zhang, Yang Gao, and Pu-Feng Du. "Predicting LncRNA Subcellular Localization Using Unbalanced Pseudo-k Nucleotide Compositions." Current Bioinformatics 15, no. 6 (November 11, 2020): 554–62. http://dx.doi.org/10.2174/1574893614666190902151038.

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Background: Long non-coding RNAs (lncRNAs) are transcripts with a length more than 200 nucleotides, functioning in the regulation of gene expression. More evidence has shown that the biological functions of lncRNAs are intimately related to their subcellular localizations. Therefore, it is very important to confirm the lncRNA subcellular localization. Methods: In this paper, we proposed a novel method to predict the subcellular localization of lncRNAs. To more comprehensively utilize lncRNA sequence information, we exploited both kmer nucleotide composition and sequence order correlated factors of lncRNA to formulate lncRNA sequences. Meanwhile, a feature selection technique which was based on the Analysis Of Variance (ANOVA) was applied to obtain the optimal feature subset. Finally, we used the support vector machine (SVM) to perform the prediction. Results: The AUC value of the proposed method can reach 0.9695, which indicated the proposed predictor is an efficient and reliable tool for determining lncRNA subcellular localization. Furthermore, the predictor can reach the maximum overall accuracy of 90.37% in leave-one-out cross validation, which clearly outperforms the existing state-of- the-art method. Conclusion: It is demonstrated that the proposed predictor is feasible and powerful for the prediction of lncRNA subcellular. To facilitate subsequent genetic sequence research, we shared the source code at https://github.com/NicoleYXF/lncRNA.

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Semwal, Rahul, and Pritish Kumar Varadwaj. "HumDLoc: Human Protein Subcellular Localization Prediction Using Deep Neural Network." Current Genomics 21, no. 7 (October 22, 2020): 546–57. http://dx.doi.org/10.2174/1389202921999200528160534.

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Aims: To develop a tool that can annotate subcellular localization of human proteins. Background: With the progression of high throughput human proteomics projects, an enormous amount of protein sequence data has been discovered in the recent past. All these raw sequence data require precise mapping and annotation for their respective biological role and functional attributes. The functional characteristics of protein molecules are highly dependent on the subcellular localization/ compartment. Therefore, a fully automated and reliable protein subcellular localization prediction system would be very useful for current proteomic research. Objective: To develop a machine learning-based predictive model that can annotate the subcellular localization of human proteins with high accuracy and precision. Methods: In this study, we used the PSI-CD-HIT homology criterion and utilized the sequence-based features of protein sequences to develop a powerful subcellular localization predictive model. The dataset used to train the HumDLoc model was extracted from a reliable data source, Uniprot knowledge base, which helps the model to generalize on the unseen dataset. Result : The proposed model, HumDLoc, was compared with two of the most widely used techniques: CELLO and DeepLoc, and other machine learning-based tools. The result demonstrated promising predictive performance of HumDLoc model based on various machine learning parameters such as accuracy (≥97.00%), precision (≥0.86), recall (≥0.89), MCC score (≥0.86), ROC curve (0.98 square unit), and precision-recall curve (0.93 square unit). Conclusion: In conclusion, HumDLoc was able to outperform several alternative tools for correctly predicting subcellular localization of human proteins. The HumDLoc has been hosted as a web-based tool at https://bioserver.iiita.ac.in/HumDLoc/.

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Li, Bo, Lijun Cai, Bo Liao, Xiangzheng Fu, Pingping Bing, and Jialiang Yang. "Prediction of Protein Subcellular Localization Based on Fusion of Multi-view Features." Molecules 24, no. 5 (March 6, 2019): 919. http://dx.doi.org/10.3390/molecules24050919.

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The prediction of protein subcellular localization is critical for inferring protein functions, gene regulations and protein-protein interactions. With the advances of high-throughput sequencing technologies and proteomic methods, the protein sequences of numerous yeasts have become publicly available, which enables us to computationally predict yeast protein subcellular localization. However, widely-used protein sequence representation techniques, such as amino acid composition and the Chou’s pseudo amino acid composition (PseAAC), are difficult in extracting adequate information about the interactions between residues and position distribution of each residue. Therefore, it is still urgent to develop novel sequence representations. In this study, we have presented two novel protein sequence representation techniques including Generalized Chaos Game Representation (GCGR) based on the frequency and distributions of the residues in the protein primary sequence, and novel statistics and information theory (NSI) reflecting local position information of the sequence. In the GCGR + NSI representation, a protein primary sequence is simply represented by a 5-dimensional feature vector, while other popular methods like PseAAC and dipeptide adopt features of more than hundreds of dimensions. In practice, the feature representation is highly efficient in predicting protein subcellular localization. Even without using machine learning-based classifiers, a simple model based on the feature vector can achieve prediction accuracies of 0.8825 and 0.7736 respectively for the CL317 and ZW225 datasets. To further evaluate the effectiveness of the proposed encoding schemes, we introduce a multi-view features-based method to combine the two above-mentioned features with other well-known features including PseAAC and dipeptide composition, and use support vector machine as the classifier to predict protein subcellular localization. This novel model achieves prediction accuracies of 0.927 and 0.871 respectively for the CL317 and ZW225 datasets, better than other existing methods in the jackknife tests. The results suggest that the GCGR and NSI features are useful complements to popular protein sequence representations in predicting yeast protein subcellular localization. Finally, we validate a few newly predicted protein subcellular localizations by evidences from some published articles in authority journals and books.

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8

Wang, Zhen, Quan Zou, Yi Jiang, Ying Ju, and Xiangxiang Zeng. "Review of Protein Subcellular Localization Prediction." Current Bioinformatics 9, no. 3 (February 11, 2014): 331–42. http://dx.doi.org/10.2174/1574893609666140212000304.

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9

Nair B J, Bipin, and Ashik P.v. "Plant and Animal sub cellular component localization prediction using multiple combination of various machine learning approaches." International Journal of Engineering & Technology 7, no. 1.9 (March 1, 2018): 221. http://dx.doi.org/10.14419/ijet.v7i1.9.9828.

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Membrane proteins are encoded in the genome and functionally important in the living organisms. Information on subcellular localization of cellular proteins has a significant role in the function of cell organelles. Discovery of drug target and system biology between localization and biological function are highly correlated. Therefore, we are predicting the localization of protein using various machine learning approaches. The prediction system based on the integration of the outcome of five sequence based sub-classifiers. The subcellular localization prediction of the final result is based on protein profile vector, which is a result of the sub-classifiers.

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10

YANG, YANG, and BAO-LIANG LU. "PROTEIN SUBCELLULAR MULTI-LOCALIZATION PREDICTION USING A MIN-MAX MODULAR SUPPORT VECTOR MACHINE." International Journal of Neural Systems 20, no. 01 (February 2010): 13–28. http://dx.doi.org/10.1142/s0129065710002206.

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Prediction of protein subcellular localization is an important issue in computational biology because it provides important clues for the characterization of protein functions. Currently, much research has been dedicated to developing automatic prediction tools. Most, however, focus on mono-locational proteins, i.e., they assume that proteins exist in only one location. It should be noted that many proteins bear multi-locational characteristics and carry out crucial functions in biological processes. This work aims to develop a general pattern classifier for predicting multiple subcellular locations of proteins. We use an ensemble classifier, called the min-max modular support vector machine (M3-SVM), to solve protein subcellular multi-localization problems; and, propose a module decomposition method based on gene ontology (GO) semantic information for M3-SVM. The amino acid composition with secondary structure and solvent accessibility information is adopted to represent features of protein sequences. We apply our method to two multi-locational protein data sets. The M3-SVMs show higher accuracy and efficiency than traditional SVMs using the same feature vectors. And the GO decomposition also helps to improve prediction accuracy. Moreover, our method has a much higher rate of accuracy than existing subcellular localization predictors in predicting protein multi-localization.

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11

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 comparing its results with other methods, it indicates the new approach is feasible and effective.

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12

Wang, Xiao, Hui Li, Rong Wang, Qiuwen Zhang, Weiwei Zhang, and Yong Gan. "MultiP-Apo: A Multilabel Predictor for Identifying Subcellular Locations of Apoptosis Proteins." Computational Intelligence and Neuroscience 2017 (2017): 1–10. http://dx.doi.org/10.1155/2017/9183796.

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Apoptosis proteins play an important role in the mechanism of programmed cell death. Predicting subcellular localization of apoptosis proteins is an essential step to understand their functions and identify drugs target. Many computational prediction methods have been developed for apoptosis protein subcellular localization. However, these existing works only focus on the proteins that have one location; proteins with multiple locations are either not considered or assumed as not existing when constructing prediction models, so that they cannot completely predict all the locations of the apoptosis proteins with multiple locations. To address this problem, this paper proposes a novel multilabel predictor named MultiP-Apo, which can predict not only apoptosis proteins with single subcellular location but also those with multiple subcellular locations. Specifically, given a query protein, GO-based feature extraction method is used to extract its feature vector. Subsequently, the GO feature vector is classified by a new multilabel classifier based on the label-specific features. It is the first multilabel predictor ever established for identifying subcellular locations of multilocation apoptosis proteins. As an initial study, MultiP-Apo achieves an overall accuracy of 58.49% by jackknife test, which indicates that our proposed predictor may become a very useful high-throughput tool in this area.

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Yoon, Yongwook, and Gary Geunbae Lee. "Subcellular Localization Prediction through Boosting Association Rules." IEEE/ACM Transactions on Computational Biology and Bioinformatics 9, no. 2 (March 2012): 609–18. http://dx.doi.org/10.1109/tcbb.2011.131.

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14

Kumar, Ravindra, and Sandeep Kumar Dhanda. "Bird Eye View of Protein Subcellular Localization Prediction." Life 10, no. 12 (December 14, 2020): 347. http://dx.doi.org/10.3390/life10120347.

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Proteins are made up of long chain of amino acids that perform a variety of functions in different organisms. The activity of the proteins is determined by the nucleotide sequence of their genes and by its 3D structure. In addition, it is essential for proteins to be destined to their specific locations or compartments to perform their structure and functions. The challenge of computational prediction of subcellular localization of proteins is addressed in various in silico methods. In this review, we reviewed the progress in this field and offered a bird eye view consisting of a comprehensive listing of tools, types of input features explored, machine learning approaches employed, and evaluation matrices applied. We hope the review will be useful for the researchers working in the field of protein localization predictions.

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15

Shen, Yinan, Yijie Ding, Jijun Tang, Quan Zou, and Fei Guo. "Critical evaluation of web-based prediction tools for human protein subcellular localization." Briefings in Bioinformatics 21, no. 5 (November 6, 2019): 1628–40. http://dx.doi.org/10.1093/bib/bbz106.

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Abstract Human protein subcellular localization has an important research value in biological processes, also in elucidating protein functions and identifying drug targets. Over the past decade, a number of protein subcellular localization prediction tools have been designed and made freely available online. The purpose of this paper is to summarize the progress of research on the subcellular localization of human proteins in recent years, including commonly used data sets proposed by the predecessors and the performance of all selected prediction tools against the same benchmark data set. We carry out a systematic evaluation of several publicly available subcellular localization prediction methods on various benchmark data sets. Among them, we find that mLASSO-Hum and pLoc-mHum provide a statistically significant improvement in performance, as measured by the value of accuracy, relative to the other methods. Meanwhile, we build a new data set using the latest version of Uniprot database and construct a new GO-based prediction method HumLoc-LBCI in this paper. Then, we test all selected prediction tools on the new data set. Finally, we discuss the possible development directions of human protein subcellular localization. Availability: The codes and data are available from http://www.lbci.cn/syn/.

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SOMMER, BJÖRN, BENJAMIN KORMEIER, PAVEL S. DEMENKOV, PATRIZIO ARRIGO, KLAUS HIPPE, ÖZGÜR ATES, ALEXEY V. KOCHETOV, VLADIMIR A. IVANISENKO, NIKOLAY A. KOLCHANOV, and RALF HOFESTÄDT. "SUBCELLULAR LOCALIZATION CHARTS: A NEW VISUAL METHODOLOGY FOR THE SEMI-AUTOMATIC LOCALIZATION OF PROTEIN-RELATED DATA SETS." Journal of Bioinformatics and Computational Biology 11, no. 01 (February 2013): 1340005. http://dx.doi.org/10.1142/s0219720013400052.

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The CELLmicrocosmos PathwayIntegration (CmPI) was developed to support and visualize the subcellular localization prediction of protein-related data such as protein-interaction networks. From the start it was possible to manually analyze the localizations by using an interactive table. It was, however, quite complicated to compare and analyze the different localization results derived from data integration as well as text-mining-based databases. The current software release provides a new interactive visual workflow, the Subcellular Localization Charts. As an application case, a MUPP1-related protein-protein interaction network is localized and semi-automatically analyzed. It will be shown that the workflow was dramatically improved and simplified. In addition, it is now possible to use custom protein-related data by using the SBML format and get a view of predicted protein localizations mapped onto a virtual cell model.

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Yao, Yuhua, Manzhi Li, Huimin Xu, Shoujiang Yan, Pingan He, Qi Dai, Zhaohui Qi, and Bo Liao. "Protein Subcellular Localization Prediction based on PSI-BLAST Profile and Principal Component Analysis." Current Proteomics 16, no. 5 (July 2, 2019): 402–14. http://dx.doi.org/10.2174/1570164616666190126155744.

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Background: Prediction of protein subcellular location is a meaningful task which attracts much attention in recent years. Particularly, the number of new protein sequences yielded by the highthroughput sequencing technology in the post genomic era has increased explosively. Objective: Protein subcellular localization prediction based solely on sequence data remains to be a challenging problem of computational biology. Methods: In this paper, three sets of evolutionary features are derived from the position-specific scoring matrix, which has shown great potential in other bioinformatics problems. A fusion model is built up by the optimal parameters combination. Finally, principal component analysis and support vector machine classifier is applied to predict protein subcellular localization on NNPSL dataset and Cell- PLoc 2.0 dataset. Results: Our experimental results show that the proposed method remarkably improved the prediction accuracy, and the features derived from PSI-BLAST profile only are appropriate for protein subcellular localization prediction.

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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." Journal of Bioinformatics and Computational Biology 07, no. 02 (April 2009): 269–85. http://dx.doi.org/10.1142/s0219720009004072.

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In the past decade, many automated prediction methods for the subcellular localization of proteins have been proposed, utilizing a wide range of principles and learning approaches. Based on an experimental evaluation of different methods and their theoretical properties, we propose to combine a well-balanced set of existing approaches to new, ensemble-based prediction methods. The experimental evaluation shows that our ensembles improve substantially over the underlying base methods.

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19

Zhao, Lingling, Junjie Wang, Mahieddine Mohammed Nabil, and Jun Zhang. "Deep Forest-based Prediction of Protein Subcellular Localization." Current Gene Therapy 18, no. 5 (November 12, 2018): 268–74. http://dx.doi.org/10.2174/1566523218666180913110949.

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20

Gao, Qing-Bin, Zhi-Chao Jin, Cheng Wu, Ya-Lin Sun, Jia He, and Xiang He. "Feature Extraction Techniques for Protein Subcellular Localization Prediction." Current Bioinformatics 4, no. 2 (May 1, 2009): 120–28. http://dx.doi.org/10.2174/157489309788184765.

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21

Yao, Yuhua, Huimin Xu, Pingan He, and Qi Dai. "Recent Advances on Prediction of Protein Subcellular Localization." Mini-Reviews in Organic Chemistry 12, no. 6 (December 24, 2015): 481–92. http://dx.doi.org/10.2174/1570193x13666151218191932.

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Mintz-Oron, S., A. Aharoni, E. Ruppin, and T. Shlomi. "Network-based prediction of metabolic enzymes' subcellular localization." Bioinformatics 25, no. 12 (May 28, 2009): i247—i1252. http://dx.doi.org/10.1093/bioinformatics/btp209.

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23

Restrepo-Montoya, Daniel, Carolina Vizcaíno, Luis F. Niño, Marisol Ocampo, Manuel E. Patarroyo, and Manuel A. Patarroyo. "Validating subcellular localization prediction tools with mycobacterial proteins." BMC Bioinformatics 10, no. 1 (2009): 134. http://dx.doi.org/10.1186/1471-2105-10-134.

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Bhasin, M., A. Garg, and G. P. S. Raghava. "PSLpred: prediction of subcellular localization of bacterial proteins." Bioinformatics 21, no. 10 (February 4, 2005): 2522–24. http://dx.doi.org/10.1093/bioinformatics/bti309.

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Ogul, Hasan, and Erkan U. Mumcuoglu. "Subcellular Localization Prediction with New Protein Encoding Schemes." IEEE/ACM Transactions on Computational Biology and Bioinformatics 4, no. 2 (April 2007): 227–32. http://dx.doi.org/10.1109/tcbb.2007.070209.

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26

Nair, Rajesh, and Burkhard Rost. "Mimicking Cellular Sorting Improves Prediction of Subcellular Localization." Journal of Molecular Biology 348, no. 1 (April 2005): 85–100. http://dx.doi.org/10.1016/j.jmb.2005.02.025.

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Chen, Xingjian, Xuejiao Hu, Wenxin Yi, Xiang Zou, and Wei Xue. "Prediction of Apoptosis Protein Subcellular Localization with Multilayer Sparse Coding and Oversampling Approach." BioMed Research International 2019 (January 30, 2019): 1–9. http://dx.doi.org/10.1155/2019/2436924.

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The prediction of apoptosis protein subcellular localization plays an important role in understanding the progress in cell proliferation and death. Recently computational approaches to this issue have become very popular, since the traditional biological experiments are so costly and time-consuming that they cannot catch up with the growth rate of sequence data anymore. In order to improve the prediction accuracy of apoptosis protein subcellular localization, we proposed a sparse coding method combined with traditional feature extraction algorithm to complete the sparse representation of apoptosis protein sequences, using multilayer pooling based on different sizes of dictionaries to integrate the processed features, as well as oversampling approach to decrease the influences caused by unbalanced data sets. Then the extracted features were input to a support vector machine to predict the subcellular localization of the apoptosis protein. The experiment results obtained by Jackknife test on two benchmark data sets indicate that our method can significantly improve the accuracy of the apoptosis protein subcellular localization prediction.

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Wattanapornprom, Warin, Chinae Thammarongtham, Apiradee Hongsthong, and Supatcha Lertampaiporn. "Ensemble of Multiple Classifiers for Multilabel Classification of Plant Protein Subcellular Localization." Life 11, no. 4 (March 30, 2021): 293. http://dx.doi.org/10.3390/life11040293.

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The accurate prediction of protein localization is a critical step in any functional genome annotation process. This paper proposes an improved strategy for protein subcellular localization prediction in plants based on multiple classifiers, to improve prediction results in terms of both accuracy and reliability. The prediction of plant protein subcellular localization is challenging because the underlying problem is not only a multiclass, but also a multilabel problem. Generally, plant proteins can be found in 10–14 locations/compartments. The number of proteins in some compartments (nucleus, cytoplasm, and mitochondria) is generally much greater than that in other compartments (vacuole, peroxisome, Golgi, and cell wall). Therefore, the problem of imbalanced data usually arises. Therefore, we propose an ensemble machine learning method based on average voting among heterogeneous classifiers. We first extracted various types of features suitable for each type of protein localization to form a total of 479 feature spaces. Then, feature selection methods were used to reduce the dimensions of the features into smaller informative feature subsets. This reduced feature subset was then used to train/build three different individual models. In the process of combining the three distinct classifier models, we used an average voting approach to combine the results of these three different classifiers that we constructed to return the final probability prediction. The method could predict subcellular localizations in both single- and multilabel locations, based on the voting probability. Experimental results indicated that the proposed ensemble method could achieve correct classification with an overall accuracy of 84.58% for 11 compartments, on the basis of the testing dataset.

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Li, Shi-Hao, Zheng-Xing Guan, Dan Zhang, Zi-Mei Zhang, Jian Huang, Wuritu Yang, and Hao Lin. "Recent Advancement in Predicting Subcellular Localization of Mycobacterial Protein with Machine Learning Methods." Medicinal Chemistry 16, no. 5 (August 7, 2020): 605–19. http://dx.doi.org/10.2174/1573406415666191004101913.

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Mycobacterium tuberculosis (MTB) can cause the terrible tuberculosis (TB), which is reported as one of the most dreadful epidemics. Although many biochemical molecular drugs have been developed to cope with this disease, the drug resistance—especially the multidrug-resistant (MDR) and extensively drug-resistance (XDR)—poses a huge threat to the treatment. However, traditional biochemical experimental method to tackle TB is time-consuming and costly. Benefited by the appearance of the enormous genomic and proteomic sequence data, TB can be treated via sequence-based biological computational approach-bioinformatics. Studies on predicting subcellular localization of mycobacterial protein (MBP) with high precision and efficiency may help figure out the biological function of these proteins and then provide useful insights for protein function annotation as well as drug design. In this review, we reported the progress that has been made in computational prediction of subcellular localization of MBP including the following aspects: 1) Construction of benchmark datasets. 2) Methods of feature extraction. 3) Techniques of feature selection. 4) Application of several published prediction algorithms. 5) The published results. 6) The further study on prediction of subcellular localization of MBP.

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Rahman, Julia, Nazrul Islam Mondal, Khaled Ben Islam, and Al Mehedi Hasan. "Feature Fusion Based SVM Classifier for Protein Subcellular Localization Prediction." Journal of Integrative Bioinformatics 13, no. 1 (March 1, 2016): 23–33. http://dx.doi.org/10.1515/jib-2016-288.

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Summary For the importance of protein subcellular localization in different branch of life science and drug discovery, researchers have focused their attentions on protein subcellular localization prediction. Effective representation of features from protein sequences plays most vital role in protein subcellular localization prediction specially in case of machine learning technique. Single feature representation like pseudo amino acid composition (PseAAC), physiochemical property model (PPM), amino acid index distribution (AAID) contains insufficient information from protein sequences. To deal with such problem, we have proposed two feature fusion representations AAIDPAAC and PPMPAAC to work with Support Vector Machine classifier, which fused PseAAC with PPM and AAID accordingly. We have evaluated performance for both single and fused feature representation of Gram-negative bacterial dataset. We have got at least 3% more actual accuracy by AAIDPAAC and 2% more locative accuracy by PPMPAAC than single feature representation.

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Kaleel, Manaz, Yandan Zheng, Jialiang Chen, Xuanming Feng, Jeremy C. Simpson, Gianluca Pollastri, and Catherine Mooney. "SCLpred-EMS: subcellular localization prediction of endomembrane system and secretory pathway proteins by Deep N-to-1 Convolutional Neural Networks." Bioinformatics 36, no. 11 (March 6, 2020): 3343–49. http://dx.doi.org/10.1093/bioinformatics/btaa156.

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Abstract Motivation The subcellular location of a protein can provide useful information for protein function prediction and drug design. Experimentally determining the subcellular location of a protein is an expensive and time-consuming task. Therefore, various computer-based tools have been developed, mostly using machine learning algorithms, to predict the subcellular location of proteins. Results Here, we present a neural network-based algorithm for protein subcellular location prediction. We introduce SCLpred-EMS a subcellular localization predictor powered by an ensemble of Deep N-to-1 Convolutional Neural Networks. SCLpred-EMS predicts the subcellular location of a protein into two classes, the endomembrane system and secretory pathway versus all others, with a Matthews correlation coefficient of 0.75–0.86 outperforming the other state-of-the-art web servers we tested. Availability and implementation SCLpred-EMS is freely available for academic users at http://distilldeep.ucd.ie/SCLpred2/. Contact catherine.mooney@ucd.ie

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Chi, Sang-Mun. "Multi-Label Combination for Prediction of Protein Subcellular Localization." Journal of the Korea Institute of Information and Communication Engineering 18, no. 7 (July 31, 2014): 1749–56. http://dx.doi.org/10.6109/jkiice.2014.18.7.1749.

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Zhao, Qing, Dong Wang, Yuehui Chen, and Xumi Qu. "Multisite protein subcellular localization prediction based on entropy density." Bio-Medical Materials and Engineering 26, s1 (August 17, 2015): S2003—S2009. http://dx.doi.org/10.3233/bme-151504.

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Fang, Gang, Guirong Tao, and Shemin Zhang. "A Research on Bioinformatics Prediction of Protein Subcellular Localization." Current Bioinformatics 4, no. 3 (September 1, 2009): 177–82. http://dx.doi.org/10.2174/157489309789071084.

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Wei, Leyi, Yijie Ding, Ran Su, Jijun Tang, and Quan Zou. "Prediction of human protein subcellular localization using deep learning." Journal of Parallel and Distributed Computing 117 (July 2018): 212–17. http://dx.doi.org/10.1016/j.jpdc.2017.08.009.

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Almagro Armenteros, Jose Juan, Casper Kaae Sønderby, Søren Kaae Sønderby, Henrik Nielsen, and Ole Winther. "DeepLoc: prediction of protein subcellular localization using deep learning." Bioinformatics 33, no. 24 (September 19, 2017): 4049. http://dx.doi.org/10.1093/bioinformatics/btx548.

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Sprenger, Josefine, J. Lynn Fink, and Rohan D. Teasdale. "Evaluation and comparison of mammalian subcellular localization prediction methods." BMC Bioinformatics 7, Suppl 5 (2006): S3. http://dx.doi.org/10.1186/1471-2105-7-s5-s3.

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Boden, M., and J. Hawkins. "Prediction of subcellular localization using sequence-biased recurrent networks." Bioinformatics 21, no. 10 (March 3, 2005): 2279–86. http://dx.doi.org/10.1093/bioinformatics/bti372.

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Hua, S., and Z. Sun. "Support vector machine approach for protein subcellular localization prediction." Bioinformatics 17, no. 8 (August 1, 2001): 721–28. http://dx.doi.org/10.1093/bioinformatics/17.8.721.

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Almagro Armenteros, José Juan, Casper Kaae Sønderby, Søren Kaae Sønderby, Henrik Nielsen, and Ole Winther. "DeepLoc: prediction of protein subcellular localization using deep learning." Bioinformatics 33, no. 21 (July 7, 2017): 3387–95. http://dx.doi.org/10.1093/bioinformatics/btx431.

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Liu, J., S. Kang, C. Tang, L. B. M. Ellis, and T. Li. "Meta-prediction of protein subcellular localization with reduced voting." Nucleic Acids Research 35, no. 15 (July 11, 2007): e96-e96. http://dx.doi.org/10.1093/nar/gkm562.

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Laurila, Kirsti, and Mauno Vihinen. "PROlocalizer: integrated web service for protein subcellular localization prediction." Amino Acids 40, no. 3 (September 2, 2010): 975–80. http://dx.doi.org/10.1007/s00726-010-0724-y.

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Shen, JinCheng, Zhang Song, and ZhiRong Sun. "GO molecular function coding based protein subcellular localization prediction." Chinese Science Bulletin 52, no. 16 (August 2007): 2240–45. http://dx.doi.org/10.1007/s11434-007-0336-4.

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Ejsmont, Radoslaw K., Pawel Golik, and Piotr P. Stepien. "Prediction of the structure of the common perimitochondrial localization signal of nuclear transcripts in yeast." Acta Biochimica Polonica 54, no. 1 (March 20, 2007): 55–61. http://dx.doi.org/10.18388/abp.2007_3269.

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Many nuclear genes encoding mitochondrial proteins require specific localization of their mRNAs to the vicinity of mitochondria for proper expression. Studies in Saccharomyces cerevisiae have shown that the cis-acting signal responsible for subcellular localization of mRNAs is localized in the 3' UTR of the transcript. In this paper we present an in silico approach for prediction of a common perimitochondrial localization signal of nuclear transcripts encoding mitochondrial proteins. We computed a consensus structure for this signal by comparison of 3' UTR models for about 3000 yeast transcripts with known localization. Our studies show a short stem-loop structure which appears in most mRNAs localized to the vicinity of mitochondria. The degree of similarity of a given 3' UTR to our consensus structure strongly correlates with experimentally determined perimitochondrial localization of the mRNA, therefore we believe that the structure we predicted acts as a subcellular localization signal. Since our algorithm operates on structures, it seems to be more reliable than sequence-based algorithms. The good predictive value of our model is supported by statistical analysis.

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Savojardo, Castrense, Niccolò Bruciaferri, Giacomo Tartari, Pier Luigi Martelli, and Rita Casadio. "DeepMito: accurate prediction of protein sub-mitochondrial localization using convolutional neural networks." Bioinformatics 36, no. 1 (June 20, 2019): 56–64. http://dx.doi.org/10.1093/bioinformatics/btz512.

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Abstract Motivation The correct localization of proteins in cell compartments is a key issue for their function. Particularly, mitochondrial proteins are physiologically active in different compartments and their aberrant localization contributes to the pathogenesis of human mitochondrial pathologies. Many computational methods exist to assign protein sequences to subcellular compartments such as nucleus, cytoplasm and organelles. However, a substantial lack of experimental evidence in public sequence databases hampered so far a finer grain discrimination, including also intra-organelle compartments. Results We describe DeepMito, a novel method for predicting protein sub-mitochondrial cellular localization. Taking advantage of powerful deep-learning approaches, such as convolutional neural networks, our method is able to achieve very high prediction performances when discriminating among four different mitochondrial compartments (matrix, outer, inner and intermembrane regions). The method is trained and tested in cross-validation on a newly generated, high-quality dataset comprising 424 mitochondrial proteins with experimental evidence for sub-organelle localizations. We benchmark DeepMito towards the only one recent approach developed for the same task. Results indicate that DeepMito performances are superior. Finally, genomic-scale prediction on a highly-curated dataset of human mitochondrial proteins further confirms the effectiveness of our approach and suggests that DeepMito is a good candidate for genome-scale annotation of mitochondrial protein subcellular localization. Availability and implementation The DeepMito web server as well as all datasets used in this study are available at http://busca.biocomp.unibo.it/deepmito. A standalone version of DeepMito is available on DockerHub at https://hub.docker.com/r/bolognabiocomp/deepmito. DeepMito source code is available on GitHub at https://github.com/BolognaBiocomp/deepmito Supplementary information Supplementary data are available at Bioinformatics online.

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Yang, Hongbin, Xiao Li, Yingchun Cai, Qin Wang, Weihua Li, Guixia Liu, and Yun Tang. "In silico prediction of chemical subcellular localization via multi-classification methods." MedChemComm 8, no. 6 (2017): 1225–34. http://dx.doi.org/10.1039/c7md00074j.

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Li, Tao, and Qian Zhong Li. "Prediction of Apoptosis Proteins Subcellular Location Using Evolutionary Profiles and Motifs Information." Advanced Materials Research 647 (January 2013): 600–606. http://dx.doi.org/10.4028/www.scientific.net/amr.647.600.

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Apoptosis proteins are very important for regulating the balance between cell proliferation and death. Because the function of apoptosis protein is closely related to its subcellular location, it is desirable to explore their function by predicting the subcellular location of apoptosis protein. In this paper, based on evolutionary profiles and motifs information of protein sequences, an approach for predicting apoptosis proteins subcellular location is presented by using support vector machine (SVM). When the method is applied to three data sets (98 apoptosis proteins dataset, 225 apoptosis proteins dataset and 317 apoptosis proteins dataset), the overall accuracies of our method on the three data sets reach 95.9%, 86.7% and 91.8% in the jackknife test, respectively. The higher predictive success rates indicate that the proposed method is very useful for apoptosis proteins subcellular localization.

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GUO, JIAN, XIAN PU, YUANLIE LIN, and HOWARD LEUNG. "PROTEIN SUBCELLULAR LOCALIZATION BASED ON PSI-BLAST AND MACHINE LEARNING." Journal of Bioinformatics and Computational Biology 04, no. 06 (December 2006): 1181–95. http://dx.doi.org/10.1142/s0219720006002405.

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Subcellular location is an important functional annotation of proteins. An automatic, reliable and efficient prediction system for protein subcellular localization is necessary for large-scale genome analysis. This paper describes a protein subcellular localization method which extracts features from protein profiles rather than from amino acid sequences. The protein profile represents a protein family, discards part of the sequence information that is not conserved throughout the family and therefore is more sensitive than the amino acid sequence. The amino acid compositions of whole profile and the N-terminus of the profile are extracted, respectively, to train and test the probabilistic neural network classifiers. On two benchmark datasets, the overall accuracies of the proposed method reach 89.1% and 68.9%, respectively. The prediction results show that the proposed method perform better than those methods based on amino acid sequences. The prediction results of the proposed method are also compared with Subloc on two redundance-reduced datasets.

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Yan, Zichao, Eric Lécuyer, and Mathieu Blanchette. "Prediction of mRNA subcellular localization using deep recurrent neural networks." Bioinformatics 35, no. 14 (July 2019): i333—i342. http://dx.doi.org/10.1093/bioinformatics/btz337.

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Abstract Motivation Messenger RNA subcellular localization mechanisms play a crucial role in post-transcriptional gene regulation. This trafficking is mediated by trans-acting RNA-binding proteins interacting with cis-regulatory elements called zipcodes. While new sequencing-based technologies allow the high-throughput identification of RNAs localized to specific subcellular compartments, the precise mechanisms at play, and their dependency on specific sequence elements, remain poorly understood. Results We introduce RNATracker, a novel deep neural network built to predict, from their sequence alone, the distributions of mRNA transcripts over a predefined set of subcellular compartments. RNATracker integrates several state-of-the-art deep learning techniques (e.g. CNN, LSTM and attention layers) and can make use of both sequence and secondary structure information. We report on a variety of evaluations showing RNATracker’s strong predictive power, which is significantly superior to a variety of baseline predictors. Despite its complexity, several aspects of the model can be isolated to yield valuable, testable mechanistic hypotheses, and to locate candidate zipcode sequences within transcripts. Availability and implementation Code and data can be accessed at https://www.github.com/HarveyYan/RNATracker. Supplementary information Supplementary data are available at Bioinformatics online.

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Wang, Xiao, Hui Li, Qiuwen Zhang, and Rong Wang. "Predicting Subcellular Localization of Apoptosis Proteins Combining GO Features of Homologous Proteins and Distance Weighted KNN Classifier." BioMed Research International 2016 (2016): 1–8. http://dx.doi.org/10.1155/2016/1793272.

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Apoptosis proteins play a key role in maintaining the stability of organism; the functions of apoptosis proteins are related to their subcellular locations which are used to understand the mechanism of programmed cell death. In this paper, we utilize GO annotation information of apoptosis proteins and their homologous proteins retrieved from GOA database to formulate feature vectors and then combine the distance weighted KNN classification algorithm with them to solve the data imbalance problem existing in CL317 data set to predict subcellular locations of apoptosis proteins. It is found that the number of homologous proteins can affect the overall prediction accuracy. Under the optimal number of homologous proteins, the overall prediction accuracy of our method on CL317 data set reaches 96.8% by Jackknife test. Compared with other existing methods, it shows that our proposed method is very effective and better than others for predicting subcellular localization of apoptosis proteins.

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