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Relevant bibliographies by topics / Position specific scoring matrix

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Academic literature on the topic 'Position specific scoring matrix'

Author: Grafiati

Published: 4 June 2021

Last updated: 11 June 2025

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Journal articles on the topic "Position specific scoring matrix"

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Jong Cheol Jeong, Xiaotong Lin, and Xue-Wen Chen. "On Position-Specific Scoring Matrix for Protein Function Prediction." IEEE/ACM Transactions on Computational Biology and Bioinformatics 8, no. 2 (2011): 308–15. http://dx.doi.org/10.1109/tcbb.2010.93.

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Lin, Chun-Yu, Yung-Chiang Chen, Yu-Shu Lo, and Jinn-Moon Yang. "Inferring homologous protein-protein interactions through pair position specific scoring matrix." BMC Bioinformatics 14, Suppl 2 (2013): S11. http://dx.doi.org/10.1186/1471-2105-14-s2-s11.

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Yao, Yu-Hua, Zhuo-Xing Shi, and Qi Dai. "Apoptosis Protein Subcellular Location Prediction Based on Position-Specific Scoring Matrix." Journal of Computational and Theoretical Nanoscience 11, no. 10 (2014): 2073–78. http://dx.doi.org/10.1166/jctn.2014.3607.

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Waris, Muhammad, Khurshid Ahmad, Muhammad Kabir, and Maqsood Hayat. "Identification of DNA binding proteins using evolutionary profiles position specific scoring matrix." Neurocomputing 199 (July 2016): 154–62. http://dx.doi.org/10.1016/j.neucom.2016.03.025.

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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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Xiao, Rong-Quan, Yan-Zhi Guo, Yu-Hong Zeng, et al. "Using position specific scoring matrix and auto covariance to predict protein subnuclear localization." Journal of Biomedical Science and Engineering 02, no. 01 (2009): 51–56. http://dx.doi.org/10.4236/jbise.2009.21009.

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Luo, Jiesi, Lezheng Yu, Yanzhi Guo, and Menglong Li. "Functional classification of secreted proteins by position specific scoring matrix and auto covariance." Chemometrics and Intelligent Laboratory Systems 110, no. 1 (2012): 163–67. http://dx.doi.org/10.1016/j.chemolab.2011.11.008.

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Li, D., T. Li, P. Cong, W. Xiong, and J. Sun. "A novel structural position-specific scoring matrix for the prediction of protein secondary structures." Bioinformatics 28, no. 1 (2011): 32–39. http://dx.doi.org/10.1093/bioinformatics/btr611.

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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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Xiong, Wenjia, Yanzhi Guo, and Menglong Li. "Prediction of Lipid-Binding Sites Based on Support Vector Machine and Position Specific Scoring Matrix." Protein Journal 29, no. 6 (2010): 427–31. http://dx.doi.org/10.1007/s10930-010-9269-x.

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Dissertations / Theses on the topic "Position specific scoring matrix"

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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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Sandag, Green Arther, and 格林亞瑟. "Enhancing Prediction Protein Function of Transporter Using RBF Network with Position Specific Scoring Matrices and Post Translational Modification Information." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/76789465679948784937.

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碩士<br>元智大學<br>資訊工程學系<br>104<br>Transporters are important transmembrane proteins that involve the cellular entry and exit of ions or molecules throughout the membrane proteins and thereby play important roles to recognize the immune system and energy transducers. Generally, three major classification transporter for membrane transport protein known as channel/pores, electrochemical transporters, and active transporters. In recent years, several studies have been conducted to analyze the transport proteins, especially discrimination class of transporters and their subfamilies have important roles in cell control system, transporting water, chemical and electric signals. Membrane transport proteins tend to forming an intricate system of pumps and channel span, and spanning cell membranes. Hence, discriminating the specific class of transporters and their subfamilies are essential tasks in computational biology and important to help biologist to have better understanding about how the function of protein in transporter proteins. Therefore, in this study attempt has been made to develop a method that used Post Translational Information (PTM) information to identify the function of transporter proteins in major class and family based on Position Specific Scoring Matrix (PSSM) profiles. The experiment results with PSSM feature set combined with PTM information could discriminate the transporter based on the three classes with independent dataset of 444 proteins can achieved an accuracy of 82.13%. Our result show that the performance prediction is better with accuracy improvement of 4%-12% and 7% compared to the previous work and Dipeptide Pair Composition (DPC) method, respectively. While for cross validation of 1904 proteins, PSSM with PTM information achieved an accuracy of 86.63% the improvement of accuracy increased about 10%, better than that gained with just DPC

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Sauer, Tilman. "Evaluierung des phylogenetischen Footprintings und dessen Anwendung zur verbesserten Vorhersage von Transkriptionsfaktor-Bindestellen." Doctoral thesis, 2006. http://hdl.handle.net/11858/00-1735-0000-0006-B383-8.

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Books on the topic "Position specific scoring matrix"

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Brinkmann, Svend. Discussion. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190247249.003.0008.

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Qualitative research was made possible with the split between the subjective and the objective, as it has by and large sought to develop systematic modes of inquiry about everything that does not seem to conform to the practices of inquiry found in the natural sciences. This chapter summarizes and compares the different philosophies treated in this book: positivism, realism, phenomenology, hermeneutics, pragmatism, structuralism, post-structuralism, and global/local. It also constructs a matrix that includes all of these philosophies. It provides a brief discussion on how to “choose” a philosophical position as a qualitative researcher and whether this is a matter of choice at all (or rather a matter of one’s basic view of humanity and the knowledge produced by humans). The four standard phases of a qualitative research project are presented, and for each phase, specific philosophical issues are discussed.

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Book chapters on the topic "Position specific scoring matrix"

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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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Tong, Joo Chuan. "Position-Specific Scoring Matrices (PMMS)." In Encyclopedia of Systems Biology. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_939.

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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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"Position-Specific Weight Matrix (PSWM)." In Encyclopedia of Systems Biology. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_101168.

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"CLUSTAL W (improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice)." In Encyclopedia of Genetics, Genomics, Proteomics and Informatics. Springer Netherlands, 2008. http://dx.doi.org/10.1007/978-1-4020-6754-9_3188.

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Baković, Matijas. "Hrvatski jezik u Bosni i Hercegovini – život na rubu (egzistencija i perspektive)." In Periferno u hrvatskom jeziku, kulturi i društvu / Peryferie w języku chorwackim, kulturze i społeczeństwie. University of Silesia Press, 2021. http://dx.doi.org/10.31261/pn.4038.18.

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The paper will consider the position of the Croatian language in Bosnia and Herzegovina and attempt to answer the question whether Croats living in Bosnia and Herzegovina should be using Standard Croatian as prescribed by the authorities in Zagreb or if they should insist upon their own peculiarities resulting from specific social and political circumstances as well as a hundred years of separation from the homeland. What position should be taken with regards to numerous words of Turkish, Arabic and Persian origin making up the vocabulary of Croats living in Bosnia and Herzegovina, considering the wide variety of words of German, Italian and Hungarian origin characteristic of the language spoken in different parts of Croatia? There are those who believe that the Croatian language in Bosnia and Herzegovina should and must be separate in some of its language solutions from the Croatian language as standardised by the authorities in Zagreb. They subscribe to the view that the Croatian language in Bosnia and Herzegovina, as well as the Croats themselves, are systematically neglected by the homeland, being used only for political point-scoring. On the other hand, the University of Mostar is the only university in Bosnia and Herzegovina teaching in the Croatian language as prescribed by the authorities in Croatia, invoking the unity of the Croatian people and language used by all Croats, regardless of their country of residence. The paper will try and clarify which position is the correct one, whether there can be only one correct position or the solution for Croats in Bosnia and Herzegovina lies in a different direction.

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Barnes, Steven A. "Categorizing Prisoners: The Identities of the Gulag." In Death and Redemption. Princeton University Press, 2011. http://dx.doi.org/10.23943/princeton/9780691151120.003.0004.

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This chapter offers a conceptualization of the identities of Gulag inmates as foisted on them by Soviet authorities and as understood by the prisoners themselves. Identity in the Gulag operated primarily along two axes: who the prisoner was prior to their arrival in the Gulag, and who the prisoner had become while in the Gulag. When a prisoner arrived in the Gulag, they stepped right into a matrix of identity in which they held a specific place defined by the type of crime committed, or their gender, class, or national identity. Nonetheless, the prisoner was not completely precluded from improving their position in the eyes of Soviet authorities.

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Conference papers on the topic "Position specific scoring matrix"

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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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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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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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Liu, Bin, Lei Lin, Xiaolong Wang, Xuan Wang, and Yi Shen. "Protein Long Disordered Region Prediction Based on Profile-Level Disorder Propensities and Position-Specific Scoring Matrixes." In 2009 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2009. http://dx.doi.org/10.1109/bibm.2009.15.

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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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Wani, M. Arif, and Heena Farooq Bhat. "A New Position Specific Scoring Vector Based Approach for Wind Speed Prediction." In 2018 7th International Conference on Renewable Energy Research and Applications (ICRERA). IEEE, 2018. http://dx.doi.org/10.1109/icrera.2018.8566968.

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Bankapur, Sanjay, and Nagamma Patil. "Position-Residue Specific Dynamic Gap Penalty Scoring Strategy for Multiple Sequence Alignment." In CSBio '17: 8th International Conference on Computational Systems-Biology and Bioinformatics. ACM, 2017. http://dx.doi.org/10.1145/3156346.3156354.

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Taju, Semmy Wellem, Nguyen-Quoc-Khanh Le, and Yu-Yen Ou. "Using Deep Learning with Position Specific Scoring Matrices to Identify Efflux Proteins in Membrane and Transport Proteins." In 2016 IEEE 16th International Conference on Bioinformatics and Bioengineering (BIBE). IEEE, 2016. http://dx.doi.org/10.1109/bibe.2016.69.

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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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