Relevant bibliographies by topics / Multiple alignment

Contents

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

Academic literature on the topic 'Multiple alignment'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Multiple alignment"

1

Wheeler, Travis J., and John D. Kececioglu. "Multiple alignment by aligning alignments." Bioinformatics 23, no. 13 (July 1, 2007): i559—i568. http://dx.doi.org/10.1093/bioinformatics/btm226.

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Prerna, Prerna, Pankaj Bhambri, and Dr O. P. Gupta Dr. O.P. Gupta. "Multiple Sequence Alignment of Different Species." Indian Journal of Applied Research 1, no. 7 (October 1, 2011): 78–82. http://dx.doi.org/10.15373/2249555x/apr2012/24.

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WANG, YI, and KUO-BIN LI. "MULTIPLE SEQUENCE ALIGNMENT USING AN EXHAUSTIVE AND GREEDY ALGORITHM." Journal of Bioinformatics and Computational Biology 03, no. 02 (April 2005): 243–55. http://dx.doi.org/10.1142/s021972000500103x.

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We describe an exhaustive and greedy algorithm for improving the accuracy of multiple sequence alignment. A simple progressive alignment approach is employed to provide initial alignments. The initial alignment is then iteratively optimized against an objective function. For any working alignment, the optimization involves three operations: insertions, deletions and shuffles of gaps. The optimization is exhaustive since the algorithm applies the above operations to all eligible positions of an alignment. It is also greedy since only the operation that gives the best improving objective score will be accepted. The algorithms have been implemented in the EGMA (Exhaustive and Greedy Multiple Alignment) package using Java programming language, and have been evaluated using the BAliBASE benchmark alignment database. Although EGMA is not guaranteed to produce globally optimized alignment, the tests indicate that EGMA is able to build alignments with high quality consistently, compared with other commonly used iterative and non-iterative alignment programs. It is also useful for refining multiple alignments obtained by other methods.
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WANG, ZHUOZHI, and KAIZHONG ZHANG. "MULTIPLE RNA STRUCTURE ALIGNMENT." Journal of Bioinformatics and Computational Biology 03, no. 03 (June 2005): 609–26. http://dx.doi.org/10.1142/s0219720005001296.

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Ribonucleic Acid (RNA) structures can be viewed as a special kind of strings where characters in a string can bond with each other. The question of aligning two RNA structures has been studied for a while, and there are several successful algorithms that are based upon different models. In this paper, by adopting the model introduced in Wang and Zhang,19 we propose two algorithms to attack the question of aligning multiple RNA structures. Our methods are to reduce the multiple RNA structure alignment problem to the problem of aligning two RNA structure alignments. Meanwhile, we will show that the framework of sequence center star alignment algorithm can be applied to the problem of multiple RNA structure alignment, and if the triangle inequality is met in the scoring matrix, the approximation ratio of the algorithm remains to be [Formula: see text], where n is the total number of structures.
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Shu, Jian-Jun, Kian Yan Yong, and Weng Kong Chan. "An Improved Scoring Matrix for Multiple Sequence Alignment." Mathematical Problems in Engineering 2012 (2012): 1–9. http://dx.doi.org/10.1155/2012/490649.

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The way for performing multiple sequence alignment is based on the criterion of the maximum-scored information content computed from a weight matrix, but it is possible to have two or more alignments to have the same highest score leading to ambiguities in selecting the best alignment. This paper addresses this issue by introducing the concept of joint weight matrix to eliminate the randomness in selecting the best multiple sequence alignment. Alignments with equal scores are iteratively rescored with the joint weight matrix of increasing level (nucleotide pairs, triplets, and so on) until one single best alignment is eventually found. This method for resolving ambiguity in multiple sequence alignment can be easily implemented by use of the improved scoring matrix.
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Bucka-Lassen, K., O. Caprani, and J. Hein. "Combining many multiple alignments in one improved alignment." Bioinformatics 15, no. 2 (February 1, 1999): 122–30. http://dx.doi.org/10.1093/bioinformatics/15.2.122.

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Edgar, Robert C., and Serafim Batzoglou. "Multiple sequence alignment." Current Opinion in Structural Biology 16, no. 3 (June 2006): 368–73. http://dx.doi.org/10.1016/j.sbi.2006.04.004.

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Kleinjung, J., N. Douglas, and J. Heringa. "Parallelized multiple alignment." Bioinformatics 18, no. 9 (September 1, 2002): 1270–71. http://dx.doi.org/10.1093/bioinformatics/18.9.1270.

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Bacon, David J., and Wayne F. Anderson. "Multiple sequence alignment." Journal of Molecular Biology 191, no. 2 (September 1986): 153–61. http://dx.doi.org/10.1016/0022-2836(86)90252-4.

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Arenas-Díaz, Edgar D., Helga Ochoterena, and Katya Rodríguez-Vázquez. "Multiple Sequence Alignment Using a Genetic Algorithm and GLOCSA." Journal of Artificial Evolution and Applications 2009 (August 27, 2009): 1–10. http://dx.doi.org/10.1155/2009/963150.

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Algorithms that minimize putative synapomorphy in an alignment cannot be directly implemented since trivial cases with concatenated sequences would be selected because they would imply a minimum number of events to be explained (e.g., a single insertion/deletion would be required to explain divergence among two sequences). Therefore, indirect measures to approach parsimony need to be implemented. In this paper, we thoroughly present a Global Criterion for Sequence Alignment (GLOCSA) that uses a scoring function to globally rate multiple alignments aiming to produce matrices that minimize the number of putative synapomorphies. We also present a Genetic Algorithm that uses GLOCSA as the objective function to produce sequence alignments refining alignments previously generated by additional existing alignment tools (we recommend MUSCLE). We show that in the example cases our GLOCSA-guided Genetic Algorithm (GGGA) does improve the GLOCSA values, resulting in alignments that imply less putative synapomorphies.
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Dissertations / Theses on the topic "Multiple alignment"

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Starrett, Dean. "Optimal Alignment of Multiple Sequence Alignments." Diss., The University of Arizona, 2008. http://hdl.handle.net/10150/194840.

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An essential tool in biology is the alignment of multiple sequences. Biologists use multiple sequence alignments for tasks such as predicting protein structure and function, reconstructing phylogenetic trees, and finding motifs. Constructing high-quality multiple alignments is computationally hard, both in theory and in practice, and is typically done using heuristic methods. The majority of state-of-the-art multiple alignment programs employ a form and polish strategy, where in the construction phase, an initial multiple alignment is formed by progressively merging smaller alignments, starting with single sequences. Then in a local-search phase, the resulting alignment is polished by repeatedly splitting it into smaller alignments and re-merging. This merging of alignments, the basic computational problem in the construction and local-search phases of the best multiple alignment heuristics, is called the Aligning Alignments Problem. Under the sum-of-pairs objective for scoring multiple alignments, this problem may seem to be a simple extension of two-sequence alignment. It is proven here, however, that with affine gap costs (which are recognized as necessary to get biologically-informative alignments) the problem is NP-complete when gaps are counted exactly. Interestingly, this form of multiple alignment is polynomial-time solvable when we relax the exact count, showing that exact gap counts themselves are inherently hard in multiple sequence alignment. Unlike general multiple alignment however, we show that Aligning Alignments with affine gap costs and exact counts is tractable in practice, by demonstrating an effective algorithm and a fast implementation. Our software AlignAlign is both time- and space-efficient on biological data. Computational experiments on biological data show instances derived from standard benchmark suites can be optimally aligned with surprising efficiency, and experiments on simulated data show the time and space both scale well.
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Sammeth, Michael. "Integrated multiple sequence alignment." [S.l.] : [s.n.], 2005. http://deposit.ddb.de/cgi-bin/dokserv?idn=98148767X.

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Auer, Jens. "Metaheuristic Multiple Sequence Alignment Optimisation." Thesis, University of Skövde, School of Humanities and Informatics, 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-899.

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The ability to tackle NP-hard problems has been greatly extended by the introduction of Metaheuristics (see Blum & Roli (2003)) for a summary of most Metaheuristics, general problem-independent optimisation algorithms extending the hill-climbing local search approach to escape local minima. One of these algorithms is Iterated Local Search (ILS) (Lourenco et al., 2002; Stützle, 1999a, p. 25ff), a recent easy to implement but powerful algorithm with results comparable or superior to other state-of-the-art methods for many combinatorial optimisation problems, among them the Traveling Salesman (TSP) and Quadratic Assignment Problem (QAP). ILS iteratively samples local minima by modifying the current local minimum and restarting

a local search porcedure on this modified solution. This thesis will show how ILS can be implemented for MSA. After that, ILS will be evaluated and compared to other MSA algorithms by BAliBASE (Thomson et al., 1999), a set of manually refined alignments used in most recent publications of algorithms and in at least two MSA algorithm surveys. The runtime-behaviour will be evaluated using runtime-distributions.

The quality of alignments produced by ILS is at least as good as the best algorithms available and significantly superiour to previously published Metaheuristics for MSA, Tabu Search and Genetic Algorithm (SAGA). On the average, ILS performed best in five out of eight test cases, second for one test set and third for the remaining two. A drawback of all iterative methods for MSA is the long runtime needed to produce good alignments. ILS needs considerably less runtime than Tabu Search and SAGA, but can not compete with progressive or consistency based methods, e. g. ClustalW or T-COFFEE.

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Siu, Wing-yan, and 蕭穎欣. "Multiple structural alignment for proteins." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2008. http://hub.hku.hk/bib/B4068748X.

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Siu, Wing-yan. "Multiple structural alignment for proteins." Click to view the E-thesis via HKUTO, 2008. http://sunzi.lib.hku.hk/hkuto/record/B4068748X.

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Carroll, Hyrum D. "Biologically Relevant Multiple Sequence Alignment." Diss., CLICK HERE for online access, 2008. http://contentdm.lib.byu.edu/ETD/image/etd2623.pdf.

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Nguyen, Ken D. "Multiple Biolgical Sequence Alignment: Scoring Functions, Algorithms, and Evaluations." Digital Archive @ GSU, 2011. http://digitalarchive.gsu.edu/cs_diss/62.

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Aligning multiple biological sequences such as protein sequences or DNA/RNA sequences is a fundamental task in bioinformatics and sequence analysis. These alignments may contain invaluable information that scientists need to predict the sequences' structures, determine the evolutionary relationships between them, or discover drug-like compounds that can bind to the sequences. Unfortunately, multiple sequence alignment (MSA) is NP-Complete. In addition, the lack of a reliable scoring method makes it very hard to align the sequences reliably and to evaluate the alignment outcomes. In this dissertation, we have designed a new scoring method for use in multiple sequence alignment. Our scoring method encapsulates stereo-chemical properties of sequence residues and their substitution probabilities into a tree-structure scoring scheme. This new technique provides a reliable scoring scheme with low computational complexity. In addition to the new scoring scheme, we have designed an overlapping sequence clustering algorithm to use in our new three multiple sequence alignment algorithms. One of our alignment algorithms uses a dynamic weighted guidance tree to perform multiple sequence alignment in progressive fashion. The use of dynamic weighted tree allows errors in the early alignment stages to be corrected in the subsequence stages. Other two algorithms utilize sequence knowledge-bases and sequence consistency to produce biological meaningful sequence alignments. To improve the speed of the multiple sequence alignment, we have developed a parallel algorithm that can be deployed on reconfigurable computer models. Analytically, our parallel algorithm is the fastest progressive multiple sequence alignment algorithm.
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Zola, Jaroslaw. "Parallel server for multiple sequence alignment." Grenoble INPG, 2005. http://www.theses.fr/2005INPG0187.

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Dans ce travail réalisé au cours du Doctorat, nous nous proposons d'étudier des techniques du calcul parallèle (en particulier, les caches web) pour optimiser l'alignement multiple de séquences. Nous développons une méthode générique pour gérer un cache local ou distribué et nous présentons un système de cache décentralisé gardant en mémoire les résultats intermédiaires ainsi que les alignements de séquences. Enfin, nous construisons un serveur parallèle utilisant les techniques précédentes permettant d'aligner plus rapidement des ensembles de séquences de grandes tailles (des milliers de séquences composées elles-mêmes de milliers depaires de bases). Ce serveur est basé sur un algorithme PhylTree, développé au Laboratoire ID-IMAG, qui est un schéma générique qui permet de construire simultanément l'alignement et la phylogénie. Le système de cache a été implémenté, le logiciel est disponible et a été utilisé en dehors du laboratoire pour plusieurs autres applications. Finalement, nous avons proposé également quelques extensions à PhylTree, comme par exemple l'utilisation du recuit simulé pour améliorer l'efficacité de l'analyse phylogénétique
Ln this work we investigate application of parallel processing and web-caching as a method to improve the efficiency of multiple sequence alignment. We develop a generic framework for distributed and local cache implementation, and we design decentralised caching system storing intermediate results of sequence alignment. Finally, we create a parallel server for multiple sequence alignment which utilises above techniques to speedup processing of large sequence sets. The server is based on the PhylTree method which is a generic scheme for multiple sequence alignment with simultaneous phylogeny, developed in the Laboratory ID-IMAG. Ln our work we propose also sorne extensions of PhylTree, like for example the application of simulated annealing to improve the efficiency of phylogenetic analysis
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DeBlasio, Daniel Frank. "Parameter Advising for Multiple Sequence Alignment." Diss., The University of Arizona, 2016. http://hdl.handle.net/10150/612932.

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The problem of aligning multiple protein sequences is essential to many biological analyses, but most standard formulations of the problem are NP-complete. Due to both the difficulty of the problem and its practical importance, there are many heuristic multiple sequence aligners that a researcher has at their disposal. A basic issue that frequently arises is that each of these alignment tools has a multitude of parameters that must be set, and which greatly affect the quality of the alignment produced. Most users rely on the default parameter setting that comes with the aligner, which is optimal on average, but can produce a low-quality alignment for the given inputs. This dissertation develops an approach called parameter advising to find a parameter setting that produces a high-quality alignment for each given input. A parameter advisor aligns the input sequences for each choice in a collection of parameter settings, and then selects the best alignment from the resulting alignments produced. A parameter advisor has two major components: (i) an advisor set of parameter choices that are given to the aligner, and (ii) an accuracy estimator that is used to rank alignments produced by the aligner. Alignment accuracy is measured with respect to a known reference alignment, in practice a reference alignment is not available, and we can only estimate accuracy. We develop a new accuracy estimator that we call called Facet (short for "feature-based accuracy estimator") that computes an accuracy estimate as a linear combination of efficiently-computable feature functions, whose coefficients are learned by solving a large scale linear programming problem. We also develop an efficient approximation algorithm for finding an advisor set of a given cardinality for a fixed estimator, whose cardinality should ideally small, as the aligner is invoked for each parameter choice in the set. Using Facet for parameter advising boosts advising accuracy by almost 20% beyond using a single default parameter choice for the hardest-to-align benchmarks. This dissertation further applies parameter advising in two ways: (i) to ensemble alignment, which uses the advising process on a collection of aligners to choose both the aligner and its parameter settings, and (ii) to adaptive local realignment, which can align different regions of the input sequences with distinct parameter choices to conform to mutation rates as they vary across the lengths of the sequences.
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Guasco, Luciano M. "Multiple sequence alignment correction using constraints." Master's thesis, Faculdade de Ciências e Tecnologia, 2010. http://hdl.handle.net/10362/5143.

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Trabalho apresentado no âmbito do European Master in Computational Logics, como requisito parcial para obtenção do grau de Mestre em Computational Logics
One of the most important fields in bioinformatics has been the study of protein sequence alignments. The study of homologous proteins, related by evolution, shows the conservation of many amino acids because of their functional and structural importance. One particular relationship between the amino acid sites in the same sequence or between different sequences, is protein-coevolution, interest in which has increased as a consequence of mathematical and computational methods used to understand the spatial, functional and evolutionary dependencies between amino acid sites. The principle of coevolution means that some amino acids are related through evolution because mutations in one site can create evolutionary pressures to select compensatory mutations in other sites that are functionally or structurally related. With the actual methods to detect coevolution, specifically mutual information techniques from the information theory field, we show in this work that much of the information between coevolved sites is lost because of mistakes in the multiple sequence alignment of variable regions. Moreover, we show that using these statistical methods to detect coevolved sites in multiple sequence alignments results in a high rate of false positives. Due to the amount of errors in the detection of coevolved site from multiple sequence alignments, we propose in this work a method to improve the detection efficacy of coevolved sites and we implement an algorithm to fix such sites correcting the misalignment produced in those specific locations. The detection part of our work is based on the mutual information between sites that are guessed as having coevolved, due to their high statistical correlation score. With this information we search for possible misalignments on those regions due to the incorrect matching of amino acids during the alignment. The re-alignment part is based on constraint programming techniques, to avoid the combinatorial complexity when one amino acid can be aligned with many others and to avoid inconsistencies in the alignments. In this work, we present a framework to impose constraints over the sequences, and we show how it is possible to compute alignments based on different criteria just by setting constraint between the amino acids. This framework can be applied not only for improving the alignment and detection of coevolved regions, but also to any desired constraints that may be used to express functional or structural relations among the amino acids in multiple sequences. We show also that after we fix these misalignments, using constraints based techniques, the correlation between coevolved sites increases and, in general, the new alignment is closer to the correct alignment than the MSA alignment. Finally, we show possible future research lines with the objective of overcoming some drawbacks detected during this work.
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Books on the topic "Multiple alignment"

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Katoh, Kazutaka, ed. Multiple Sequence Alignment. New York, NY: Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-1036-7.

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Russell, David James. Multiple sequence alignment methods. New York: Humana Press, 2014.

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Russell, David J., ed. Multiple Sequence Alignment Methods. Totowa, NJ: Humana Press, 2014. http://dx.doi.org/10.1007/978-1-62703-646-7.

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DeBlasio, Dan, and John Kececioglu. Parameter Advising for Multiple Sequence Alignment. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-64918-4.

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Nguyen, Ken, Xuan Guo, and Yi Pan. Multiple Biological Sequence Alignment: Scoring Functions, Algorithms and Applications. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2016. http://dx.doi.org/10.1002/9781119273769.

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Perrey, Sören W. Fast approximation to the NP-hard problem of multiple sequence alignment. Palmerston North, N.Z: Faculty of Information and Mathematical Sciences, Massey University, 1996.

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Shannon, Brian. Technical analysis using multiple timeframes: Understand market structure and profit from trend alignment. Centennial, Colo: LifeVest Pub., 2008.

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Schwikowski, Benno. A New algorithmic approach to the construction of multiple alignments and evolutionary trees. Sankt Augustin, Germany: GMD-Forschungszentrum Informationstechnik, 1998.

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DeBlasio, Dan, and John Kececioglu. Parameter Advising for Multiple Sequence Alignment. Springer, 2019.

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Katoh, Kazutaka. Multiple Sequence Alignment: Methods and Protocols. Humana Press, 2020.

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Book chapters on the topic "Multiple alignment"

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Hein, Jotun. "Multiple Alignment." In Molecular Tools for Screening Biodiversity, 334–40. Dordrecht: Springer Netherlands, 1998. http://dx.doi.org/10.1007/978-94-009-0019-6_61.

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Ma, Bin, Zhuozhi Wang, and Kaizhong Zhang. "Alignment between Two Multiple Alignments." In Combinatorial Pattern Matching, 254–65. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-44888-8_19.

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Pirovano, Walter, and Jaap Heringa. "Multiple Sequence Alignment." In Bioinformatics, 143–61. Totowa, NJ: Humana Press, 2008. http://dx.doi.org/10.1007/978-1-60327-159-2_7.

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Miklós, István. "Statistical Multiple Alignment." In Encyclopedia of Algorithms, 2086–90. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-2864-4_400.

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Zimmermann, Karl-Heinz. "Multiple Sequence Alignment." In The Kluwer International Series in Engineering and Computer Science, 75–98. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4419-9210-9_4.

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Bawono, Punto, Maurits Dijkstra, Walter Pirovano, Anton Feenstra, Sanne Abeln, and Jaap Heringa. "Multiple Sequence Alignment." In Methods in Molecular Biology, 167–89. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-6622-6_8.

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DeBlasio, Dan, and John Kececioglu. "Ensemble Multiple Alignment." In Parameter Advising for Multiple Sequence Alignment, 85–102. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-64918-4_7.

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Miklós, István. "Statistical Multiple Alignment." In Encyclopedia of Algorithms, 892–94. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-30162-4_400.

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Kao, Ming-Yang. "Multiple String Alignment." In Encyclopedia of Algorithms, 563. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-30162-4_250.

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Zhang, Shihua. "Multiple Network Alignment." In Encyclopedia of Systems Biology, 1469. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_484.

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Conference papers on the topic "Multiple alignment"

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Rezaei, S., and J. Bai. "Multiple Gene Order Alignment." In 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference. IEEE, 2005. http://dx.doi.org/10.1109/iembs.2005.1615536.

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Tsau, S. K., D. Y. Hong, H. W. Le, C. M. Ming Chang, and C. H. Lin. "Multiple Alignment Stage for the Automatic Precision Alignment System." In 2012 International Symposium on Computer, Consumer and Control (IS3C). IEEE, 2012. http://dx.doi.org/10.1109/is3c.2012.241.

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Naznin, Farhana, Ruhul Sarker, and Daryl Essam. "Iterative progressive alignment method (IPAM) for multiple sequence alignment." In Industrial Engineering (CIE39). IEEE, 2009. http://dx.doi.org/10.1109/iccie.2009.5223562.

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Jain, Brijnesh J., Henning Stehr, Michael Lappe, and Klaus Obermayer. "Multiple alignment of contact maps." In 2009 International Joint Conference on Neural Networks (IJCNN 2009 - Atlanta). IEEE, 2009. http://dx.doi.org/10.1109/ijcnn.2009.5178907.

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Zhang, Jiawei, and Philip S. Yu. "Multiple Anonymized Social Networks Alignment." In 2015 IEEE International Conference on Data Mining (ICDM). IEEE, 2015. http://dx.doi.org/10.1109/icdm.2015.114.

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Myers, Gene, Sanford Selznick, Zheng Zhang, and Webb Miller. "Progressive multiple alignment with constraints." In the first annual international conference. New York, New York, USA: ACM Press, 1997. http://dx.doi.org/10.1145/267521.267758.

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Cazenave, T. "Overestimation for Multiple Sequence Alignment." In 2007 4th Symposium on Computational Intelligence in Bioinformatics and Computational Biology. IEEE, 2007. http://dx.doi.org/10.1109/cibcb.2007.4221218.

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Seeluangsawat, Pasut, and Prabhas Chongstitvatana. "A multiple objective evolutionary algorithm for multiple sequence alignment." In the 2005 conference. New York, New York, USA: ACM Press, 2005. http://dx.doi.org/10.1145/1068009.1068088.

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Farhana Naznin, Morikazu Nakamura, Takeo Okazaki, and Yumiko Nakajima. "An evolutionary progressive multiple sequence alignment." In 2007 IEEE Congress on Evolutionary Computation. IEEE, 2007. http://dx.doi.org/10.1109/cec.2007.4424977.

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Li, Gen, Li Sun, Zhongbao Zhang, Pengxin Ji, Sen Su, and Philip S. Yu. "MC2:Unsupervised Multiple Social Network Alignment." In 2019 IEEE International Conference on Big Data (Big Data). IEEE, 2019. http://dx.doi.org/10.1109/bigdata47090.2019.9005701.

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Reports on the topic "Multiple alignment"

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Seiler, Steven. Individual NSLS-II Multipole Magnet Alignment by Laser Trackers. Office of Scientific and Technical Information (OSTI), October 2013. http://dx.doi.org/10.2172/1505118.

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Shah, Nameeta, Olivier Couronne, Len A. Pennacchio, Michael Brudno, Serafim Batzoglou, E. Wes Bethel, Edward M. Rubin, Bernd Hamann, and Inna Dubchak. Phylo-VISTA: An Interactive Visualization Tool for Multiple DNA Sequence Alignments. Office of Scientific and Technical Information (OSTI), April 2004. http://dx.doi.org/10.2172/832965.

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Gardner, S., and C. Jaing. Interim Report on Multiple Sequence Alignments and TaqMan Signature Mapping to Phylogenetic Trees. Office of Scientific and Technical Information (OSTI), March 2012. http://dx.doi.org/10.2172/1047247.

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Guan, X., and E. C. Uberbacher. A multiple divide-and-conquer (MDC) algorithm for optimal alignments in linear space. Office of Scientific and Technical Information (OSTI), June 1994. http://dx.doi.org/10.2172/10168027.

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Hilbrecht, Margo, David Baxter, Alexander V. Graham, and Maha Sohail. Research Expertise and the Framework of Harms: Social Network Analysis, Phase One. GREO, December 2020. http://dx.doi.org/10.33684/2020.006.

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Abstract:
In 2019, the Gambling Commission announced a National Strategy to Reduce Gambling Harms. Underlying the strategy is the Framework of Harms, outlined in Measuring gambling-related harms: A framework for action. "The Framework" adopts a public health approach to address gambling-related harm in Great Britain across multiple levels of measurement. It comprises three primary factors and nine related subfactors. To advance the National Strategy, all componentsneed to be supported by a strong evidence base. This report examines existing research expertise relevant to the Framework amongacademics based in the UK. The aim is to understand the extent to which the Framework factors and subfactors have been studied in order to identify gaps in expertise and provide evidence for decision making thatisrelevant to gambling harms research priorities. A social network analysis identified coauthor networks and alignment of research output with the Framework. The search strategy was limited to peer-reviewed items and covered the 12-year period from 2008 to 2019. Articles were selected using a Web of Science search. Of the 1417 records identified in the search, the dataset was refined to include only those articles that could be assigned to at least one Framework factor (n = 279). The primary factors and subfactors are: Resources:Work and Employment, Money and Debt, Crime;Relationships:Partners, Families and Friends, Community; and Health:Physical Health, Psychological Distress, and Mental Health. We used Gephi software to create visualisations reflecting degree centrality (number of coauthor networks) so that each factor and subfactor could be assessed for the density of research expertise and patterns of collaboration among coauthors. The findings show considerable variation by framework factor in the number of authors and collaborations, suggesting a need to develop additional research capacity to address under-researched areas. The Health factor subcategory of Mental Health comprised almost three-quarters of all citations, with the Resources factor subcategory of Money and Debt a distant second at 12% of all articles. The Relationships factor, comprised of two subfactors, accounted for less than 10%of total articles. Network density varied too. Although there were few collaborative networks in subfactors such as Community or Work and Employment, all Health subfactors showed strong levels of collaboration. Further, some subfactors with a limited number of researchers such as Partners, Families, and Friends and Money and debt had several active collaborations. Some researchers’ had publications that spanned multiple Framework factors. These multiple-factor researchers usually had a wide range of coauthors when compared to those who specialised (with the exception of Mental Health).Others’ collaborations spanned subfactors within a factor area. This was especially notable forHealth. The visualisations suggest that gambling harms research expertise in the UK has considerable room to grow in order to supporta more comprehensive, locally contextualised evidence base for the Framework. To do so, priority harms and funding opportunities will need further consideration. This will require multi-sector and multidisciplinary collaboration consistent with the public health approach underlying the Framework. Future research related to the present analysis will explore the geographic distribution of research activity within the UK, and research collaborations with harms experts internationally.
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