Relevant bibliographies by topics / Position Specific Scoring Matrix (PSSM)

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Position Specific Scoring Matrix (PSSM)'

Author: Grafiati
Published: 19 February 2023
Last updated: 20 February 2023

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Journal articles on the topic "Position Specific Scoring Matrix (PSSM)"

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Ali, Farman, Omar Barukab, Ajay B. Gadicha, Shruti Patil, Omar Alghushairy, and Akram Y. Sarhan. "DBP-iDWT: Improving DNA-Binding Proteins Prediction Using Multi-Perspective Evolutionary Profile and Discrete Wavelet Transform." Computational Intelligence and Neuroscience 2022 (September 28, 2022): 1–8. http://dx.doi.org/10.1155/2022/2987407.

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DNA-binding proteins (DBPs) have crucial biotic activities including DNA replication, recombination, and transcription. DBPs are highly concerned with chronic diseases and are used in the manufacturing of antibiotics and steroids. A series of predictors were established to identify DBPs. However, researchers are still working to further enhance the identification of DBPs. This research designed a novel predictor to identify DBPs more accurately. The features from the sequences are transformed by F-PSSM (Filtered position-specific scoring matrix), PSSM-DPC (Position specific scoring matrix-dipeptide composition), and R-PSSM (Reduced position-specific scoring matrix). To eliminate the noisy attributes, we extended DWT (discrete wavelet transform) to F-PSSM, PSSM-DPC, and R-PSSM and introduced three novel descriptors, namely, F-PSSM-DWT, PSSM-DPC-DWT, and R-PSSM-DWT. Onward, the training of the four models were performed using LiXGB (Light eXtreme gradient boosting), XGB (eXtreme gradient boosting, ERT (extremely randomized trees), and Adaboost. LiXGB with R-PSSM-DWT has attained 6.55% higher accuracy on training and 5.93% on testing dataset than the best existing predictors. The results reveal the excellent performance of our novel predictor over the past studies. DBP-iDWT would be fruitful for establishing more operative therapeutic strategies for fatal disease treatment.
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Chen, Teng-Ruei, Sheng-Hung Juan, Yu-Wei Huang, Yen-Cheng Lin, and Wei-Cheng Lo. "A secondary structure-based position-specific scoring matrix applied to the improvement in protein secondary structure prediction." PLOS ONE 16, no. 7 (2021): e0255076. http://dx.doi.org/10.1371/journal.pone.0255076.

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Protein secondary structure prediction (SSP) has a variety of applications; however, there has been relatively limited improvement in accuracy for years. With a vision of moving forward all related fields, we aimed to make a fundamental advance in SSP. There have been many admirable efforts made to improve the machine learning algorithm for SSP. This work thus took a step back by manipulating the input features. A secondary structure element-based position-specific scoring matrix (SSE-PSSM) is proposed, based on which a new set of machine learning features can be established. The feasibility of this new PSSM was evaluated by rigid independent tests with training and testing datasets sharing <25% sequence identities. In all experiments, the proposed PSSM outperformed the traditional amino acid PSSM. This new PSSM can be easily combined with the amino acid PSSM, and the improvement in accuracy was remarkable. Preliminary tests made by combining the SSE-PSSM and well-known SSP methods showed 2.0% and 5.2% average improvements in three- and eight-state SSP accuracies, respectively. If this PSSM can be integrated into state-of-the-art SSP methods, the overall accuracy of SSP may break the current restriction and eventually bring benefit to all research and applications where secondary structure prediction plays a vital role during development. To facilitate the application and integration of the SSE-PSSM with modern SSP methods, we have established a web server and standalone programs for generating SSE-PSSM available at http://10.life.nctu.edu.tw/SSE-PSSM.
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Wang, Qin, Boyuan Wang, Zhenlei Xu, et al. "PSSM-Distil: Protein Secondary Structure Prediction (PSSP) on Low-Quality PSSM by Knowledge Distillation with Contrastive Learning." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 1 (2021): 617–25. http://dx.doi.org/10.1609/aaai.v35i1.16141.

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Protein secondary structure prediction (PSSP) is an essential task in computational biology. To achieve the accurate PSSP, the general and vital feature engineering is to use multiple sequence alignment (MSA) for Position-Specific Scoring Matrix (PSSM) extraction. However, when only low-quality PSSM can be obtained due to poor sequence homology, previous PSSP accuracy (merely around 65%) is far from practical usage for subsequent tasks. In this paper, we propose a novel PSSM-Distil framework for PSSP on low-quality PSSM, which not only enhances the PSSM feature at a lower level but also aligns the feature distribution at a higher level. In practice, the PSSM-Distil first exploits the proteins with high-quality PSSM to achieve a teacher network for PSSP in a full-supervised way. Under the guidance of the teacher network, the low-quality PSSM and corresponding student network with low discriminating capacity are effectively resolved by feature enhancement through EnhanceNet and distribution alignment through knowledge distillation with contrastive learning. Further, our PSSM-Distil supports the input from a pre-trained protein sequence language BERT model to provide auxiliary information, which is designed to address the extremely low-quality PSSM cases, i.e., no homologous sequence. Extensive experiments demonstrate the proposed PSSM-Distil outperforms state-of-the-art models on PSSP by 6% on average and nearly 8% in extremely low-quality cases on public benchmarks, BC40 and CB513.
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Fang, Chun, Tamotsu Noguchi, and Hayato Yamana. "Analysis of evolutionary conservation patterns and their influence on identifying protein functional sites." Journal of Bioinformatics and Computational Biology 12, no. 05 (2014): 1440003. http://dx.doi.org/10.1142/s0219720014400034.

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Evolutionary conservation information included in position-specific scoring matrix (PSSM) has been widely adopted by sequence-based methods for identifying protein functional sites, because all functional sites, whether in ordered or disordered proteins, are found to be conserved at some extent. However, different functional sites have different conservation patterns, some of them are linear contextual, some of them are mingled with highly variable residues, and some others seem to be conserved independently. Every value in PSSMs is calculated independently of each other, without carrying the contextual information of residues in the sequence. Therefore, adopting the direct output of PSSM for prediction fails to consider the relationship between conservation patterns of residues and the distribution of conservation scores in PSSMs. In order to demonstrate the importance of combining PSSMs with the specific conservation patterns of functional sites for prediction, three different PSSM-based methods for identifying three kinds of functional sites have been analyzed. Results suggest that, different PSSM-based methods differ in their capability to identify different patterns of functional sites, and better combining PSSMs with the specific conservation patterns of residues would largely facilitate the prediction.
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Saini, Harsh, Gaurav Raicar, Alok Sharma, et al. "Protein Structural Class Prediction viak-Separated Bigrams Using Position Specific Scoring Matrix." Journal of Advanced Computational Intelligence and Intelligent Informatics 18, no. 4 (2014): 474–79. http://dx.doi.org/10.20965/jaciii.2014.p0474.

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Protein structural class prediction (SCP) is as important task in identifying protein tertiary structure and protein functions. In this study, we propose a feature extraction technique to predict secondary structures. The technique utilizes bigram (of adjacent andk-separated amino acids) information derived from Position Specific Scoring Matrix (PSSM). The technique has shown promising results when evaluated on benchmarked Ding and Dubchak dataset.
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MALDE, KETIL, and ROBERT GIEGERICH. "Calculating PSSM probabilities with lazy dynamic programming." Journal of Functional Programming 16, no. 1 (2005): 75–81. http://dx.doi.org/10.1017/s0956796805005708.

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Position-specific scoring matrices are one way to represent approximate string patterns, which are commonly encountered in the field of bioinformatics. An important problem that arises with their application is calculating the statistical significance of matches. We review the currently most efficient algorithm for this task, and show how it can be implemented in Haskell, taking advantage of the built-in non-strictness of the language. The resulting program turns out to be an instance of dynamic programming, using lists rather the typical dynamic programming matrix.
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Dehzangi, Abdollah, Yosvany López, Sunil Pranit Lal, et al. "PSSM-Suc: Accurately predicting succinylation using position specific scoring matrix into bigram for feature extraction." Journal of Theoretical Biology 425 (July 2017): 97–102. http://dx.doi.org/10.1016/j.jtbi.2017.05.005.

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Khan, Muslim, Maqsood Hayat, Sher Afzal Khan, Saeed Ahmad, and Nadeem Iqbal. "Bi-PSSM: Position specific scoring matrix based intelligent computational model for identification of mycobacterial membrane proteins." Journal of Theoretical Biology 435 (December 2017): 116–24. http://dx.doi.org/10.1016/j.jtbi.2017.09.013.

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Bhat, Heena Farooq, and M. Arif Wani. "Novel PSSM-Based Approaches for Gene Identification Using Support Vector Machine." Journal of Information Technology Research 14, no. 2 (2021): 152–73. http://dx.doi.org/10.4018/jitr.2021040108.

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By understanding the function of each protein encoded in genome, the molecular mechanism of the cell can be recognized. In genome annotation field, several methods or techniques have been developed to locate or predict the patterns of genes in genome sequence. However, recognizing corresponding gene of a given protein sequence using conventional tools is inherently complicated and error prone. This paper first focuses on the issue of gene prediction and its challenges. The authors then present a novel method for identifying genes that involves a two-step process. First the research presents new features extracted from protein sequences using a position specific scoring matrix (PSSM). The PSSM profiles are converted into uniform numeric representation. Then, a new structured approach has been applied on PSSM vector which uses a decision tree-based technique for obtaining rules. Finally, the rules of single class are joined together to form a matrix which is then given as an input to SVM for classification purpose. The rules derived from algorithm correspond to genes. The authors also introduce another approach for predicting genes based on PSSM using SVM. Both the methods have been implemented on genome DNAset dataset. Empirical evaluation shows that PSSM based SAFARI approach produces better results.
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Liang, Yunyun, Sanyang Liu, and Shengli Zhang. "Prediction of Protein Structural Classes for Low-Similarity Sequences Based on Consensus Sequence and Segmented PSSM." Computational and Mathematical Methods in Medicine 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/370756.

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Prediction of protein structural classes for low-similarity sequences is useful for understanding fold patterns, regulation, functions, and interactions of proteins. It is well known that feature extraction is significant to prediction of protein structural class and it mainly uses protein primary sequence, predicted secondary structure sequence, and position-specific scoring matrix (PSSM). Currently, prediction solely based on the PSSM has played a key role in improving the prediction accuracy. In this paper, we propose a novel method called CSP-SegPseP-SegACP by fusing consensus sequence (CS), segmented PsePSSM, and segmented autocovariance transformation (ACT) based on PSSM. Three widely used low-similarity datasets (1189, 25PDB, and 640) are adopted in this paper. Then a 700-dimensional (700D) feature vector is constructed and the dimension is decreased to 224D by using principal component analysis (PCA). To verify the performance of our method, rigorous jackknife cross-validation tests are performed on 1189, 25PDB, and 640 datasets. Comparison of our results with the existing PSSM-based methods demonstrates that our method achieves the favorable and competitive performance. This will offer an important complementary to other PSSM-based methods for prediction of protein structural classes for low-similarity sequences.
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Dissertations / Theses on the topic "Position Specific Scoring Matrix (PSSM)"

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Lyons, James Geoffrey. "Enhanced Feature Extraction from Evolutionary Profiles for Protein Fold Recognition." Thesis, Griffith University, 2016. http://hdl.handle.net/10072/365732.

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Proteins are important biological macromolecules that play important roles in al- most all biological reactions. The function of a protein is dependent on the shape it folds in to, which is in turn dependent on the protein’s amino acid sequence. Ex- perimental approaches for determining a protein’s 3D structure are expensive and time consuming, so computational methods for determining the structure from the amino acid sequence are desired. Methods for directly computing the 3D structure of a protein exist, however they are impractical for large proteins and high resolution models due to the large search space. Instead of trying to directly find the 3D struc- ture from first principles, the primary structure can be compared to proteins with known 3D structure. A ‘fold’ is a way of classifying proteins with the same major secondary structures in the same arrangement and with the same topological con- nections. Protein Fold Recognition (PFR) is an important step towards determining a protein’s structure, simplifying the protein structure prediction problem. This is a multi-class classification problem solvable using machine learning techniques. The PFR problem has been widely studied in the past, with feature extraction approaches including using counts of amino acids and pairs of amino acids, physic- ochemical information, evolutionary information from the Position Specific Scoring Matrix (PSSM), and structural information from its predicted secondary structure. These approaches do work, but with limited success. Current state of the art features use information from the PSSM as well as the predicted secondary structure.<br>Thesis (PhD Doctorate)<br>Doctor of Philosophy (PhD)<br>Griffith School of Engineering<br>Science, Environment, Engineering and Technology<br>Full Text
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Turatsinze, Jean Valéry. "Développement et évaluation de méthodes bioinformatiques pour la détection de séquences cis-régulatrices impliquées dans le développement de la drosophile." Doctoral thesis, Universite Libre de Bruxelles, 2009. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/210053.

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L'objectif de ce travail est de développer et d'évaluer des approches méthodologiques pour la<p>prédiction de séquences cis-régulatrices. Ces approches ont été intégrées dans la suite logicielle<p>RSAT (Regulatory Sequences Analysis Tools). Ces séquences jouent un rôle important dans la<p>régulation de l'expression des gènes. Cette régulation, au niveau transcriptionnel, s'effectue à<p>travers la reconnaissance spécifique entre les facteurs de transcription et leurs sites de fixation<p>(TFBS) au niveau de l'ADN.<p>Nous avons développé et évalué une série d'outils bioinformatiques qui utilisent les matrices<p>position-poids pour prédire les TFBS ainsi que les modules cis-régulateurs (CRM). Nos outils<p>présentent l'avantage d'intégrer les différentes approches déjà proposées par d'autres auteurs tout<p>en proposant des fonctionnalités innovantes.<p>Nous proposons notamment une nouvelle approche pour la prédiction de CRM basé sur la<p>détection de régions significativement enrichies en TFBS. Nous les avons appelés les CRER (pour<p>Cis-Regulatory Elements Enriched Regions). Un autre aspect essentiel de toute notre approche<p>réside dans le fait que nous proposons des mesures statistiques rigoureuses pour estimer<p>théoriquement et empiriquement le risque associé aux différentes prédictions. Les méthodes de<p>prédictions de séquences cis-regulatrices prédisent en effet un taux de fausses prédictions<p>généralement élevé. Nous intégrons un calcul des P-valeurs associées à toutes les prédictions.<p>Nous proposons ainsi une mesure fiable de la probabilité de faux positifs.<p>Nous avons appliqué nos outils pour une évaluation systématique de l'effet du modèle de<p>background sur la précision des prédictions à partir de la base de données de TRANSFAC. Nos<p>résultats suggèrent une grande variabilité pour les modèles qui optimisent la précision des<p>prédictions. Il faut choisir le modèle de background au cas par cas selon la matrice considérée.<p>Nous avons ensuite évalué la qualité des matrices de tous les facteurs de transcription de<p>drosophile de la base de données ORegAnno, c'est à dire leur pouvoir de discrimination entre les<p>TFBS et les séquences génomiques. Nous avons ainsi collecté des matrices des facteurs de<p>transcription de drosophile de bonne qualité.<p>A partir des matrices de drosophile que nous avons collectées, nous avons entamé une analyse<p>préliminaire multi-genome de prédictions de TFBS et de CRM dans la région de lʼenhancer dorsocentral<p>(DCE) du complexe achaete-scute de drosophile. Les gènes de ce complexe jouent un<p>rôle important dans la détermination des cellules système nerveux périphérique de drosophile. Il a<p>été prouvé expérimentalement qu'il existe un lien direct entre le phénotype du système nerveux<p>périphérique et les séquences cis-régulateurs des gènes de ce complexe.<p>Les outils que nous avons développés durant ce projet peuvent s'appliquer à la prédiction des<p>séquences de régulation dans les génomes de tous les organismes.<br>Doctorat en Sciences<br>info:eu-repo/semantics/nonPublished
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Huang, Hsuan-Yu, and 黃璿宇. "Improving Prediction of Protein Solvent Accessibility with Modified Position Specific Scoring Matrix." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/75636299301265584158.

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碩士<br>國立成功大學<br>電機工程學系碩博士班<br>96<br>Predicting protein tertiary structures directly from one-dimensional sequences still remains a challenging problem in life science. The process of protein folding is driven to the solvent aversion of some of the residues. Therefore, prediction of protein solvent accessibility is an important step for tertiary structure prediction. Traditionally, predicting solvent accessibility is regarded as either a two- (“exposed” or “buried”) or three-state (“exposed”, “intermediate” or “buried”) classification problem. However, the states of solvent accessibility are not well-defined in real protein structures. Thus, recent studies have started to directly predict the accessible surface area (ASA) based on various regression techniques. Most ASA predictors encoded residues into feature vectors, which can be incorporated with general regression tools for ASA prediction. Recently, position specific scoring matrix (PSSM) has been demonstrated helpful for ASA prediction and wildly used in the encoding process. In this study, we propose a systematic method to enhance the PSSM-based encoding scheme for ASA prediction. This method accumulates the PSSM values of similar residues to generate novel features. An iterative feature selection is designed to ensure the grouped residues have similar physicochemical properties and similar ASA propensities. In addition, we incorporate the proposed encoding scheme with support vector regression (SVR) to construct an ASA predictor. The performance of our predictor is evaluated by comparion with five existing predictors. Experimental results show that the proposed predictor achieved a mean absolute error (MAE) of 14.2~14.8%, which is better than the 14.9~19.0% MAE of other predictors. These results demonstrate that the features generated by the proposed encoding scheme are informative for protein ASA prediction.
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Book chapters on the topic "Position Specific Scoring Matrix (PSSM)"

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Fang, Chun, Tamotsu Noguchi, Hayato Yamana, and Fuzhen Sun. "Identifying Protein Short Linear Motifs by Position-Specific Scoring Matrix." In Lecture Notes in Computer Science. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41009-8_22.

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Song, Chaohong. "Prediction of Bacterial Toxins by Feature Representation of Position Specific Scoring Matrix and IB1 Classifier Fusion." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19853-3_95.

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Singh, Vineet, Alok Sharma, Abel Chandra, Abdollah Dehzangi, Daichi Shigemizu, and Tatsuhiko Tsunoda. "Computational Prediction of Lysine Pupylation Sites in Prokaryotic Proteins Using Position Specific Scoring Matrix into Bigram for Feature Extraction." In PRICAI 2019: Trends in Artificial Intelligence. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-29894-4_39.

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"Position-Specific Scoring Matrix (PSSM)." In Encyclopedia of Genetics, Genomics, Proteomics and Informatics. Springer Netherlands, 2008. http://dx.doi.org/10.1007/978-1-4020-6754-9_13293.

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"Position-Specific Scoring Matrix (PSSM)." In Encyclopedia of Systems Biology. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_101167.

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Conference papers on the topic "Position Specific Scoring Matrix (PSSM)"

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Shen, Wen-Jun, and Hau-San Wong. "OWA-PSSM — A position specific scoring matrix based method integrated with OWA weights for HLA-DR peptide binding prediction." In 2012 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2012. http://dx.doi.org/10.1109/bibm.2012.6392705.

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Wang, Qin, Jun Wei, Boyuan Wang, Zhen Li, Sheng Wang, and Shuguang Cui. "Adaptive Residue-wise Profile Fusion for Low Homologous Protein Secondary Structure Prediction Using External Knowledge." 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.2021/490.

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Protein secondary structure prediction (PSSP) is essential for protein function analysis. However, for low homologous proteins, the PSSP suffers from insufficient input features. In this paper, we explicitly import external self-supervised knowledge for low homologous PSSP under the guidance of residue-wise (amino acid wise) profile fusion. In practice, we firstly demonstrate the superiority of profile over Position-Specific Scoring Matrix (PSSM) for low homologous PSSP. Based on this observation, we introduce the novel self-supervised BERT features as the pseudo profile, which implicitly involves the residue distribution in all native discovered sequences as the complementary features. Furthermore, a novel residue-wise attention is specially designed to adaptively fuse different features (i.e., original low-quality profile, BERT based pseudo profile), which not only takes full advantage of each feature but also avoids noise disturbance. Besides, the feature consistency loss is proposed to accelerate the model learning from multiple semantic levels. Extensive experiments confirm that our method outperforms state-of-the-arts (i.e., 4.7% for extremely low homologous cases on BC40 dataset).
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Kelley, Lawrence A., Robert M. MacCallum, and Michael J. E. Sternberg. "Recognition of remote protein homologies using three-dimensional information to generate a position specific scoring matrix in the program 3D-PSSM." In the third annual international conference. ACM Press, 1999. http://dx.doi.org/10.1145/299432.299486.

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Yang, Runtao, Chengjin Zhang, and Lina Zhang. "PSSM-PROREP: A Flexible Web Server for Generating Various Position Specific Score Matrix-derived Protein Representations*." In 2018 IEEE International Conference on Information and Automation (ICIA). IEEE, 2018. http://dx.doi.org/10.1109/icinfa.2018.8812463.

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Wani, M. Arif, Heena Farooq Bhat, and Tariq Rashid Jan. "Position Specific Scoring Matrix and Synergistic Multiclass SVM for Identification of Genes." In 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 2018. http://dx.doi.org/10.1109/icmla.2018.00192.

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Zhang, Lina, Chengjin Zhang, Rui Gao, and Runtao Yang. "Incorporating g-gap dipeptide composition and position specific scoring matrix for identifying antioxidant proteins." In 2015 IEEE 28th Canadian Conference on Electrical and Computer Engineering (CCECE). IEEE, 2015. http://dx.doi.org/10.1109/ccece.2015.7129155.

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