Relevant bibliographies by topics / Evolutionary bioinformatics

Contents

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

Academic literature on the topic 'Evolutionary bioinformatics'

Author: Grafiati
Published: 4 June 2021
Last updated: 9 February 2022

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Journal articles on the topic "Evolutionary bioinformatics"

1

Pagel, Mark. "Evolution, Bioinformatics and Evolutionary Bioinformatics Online." Evolutionary Bioinformatics 2 (January 2006): 117693430600200. http://dx.doi.org/10.1177/117693430600200006.

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Pagel, M. "Phylogenetic-evolutionary approaches to bioinformatics." Briefings in Bioinformatics 1, no. 2 (2000): 117–30. http://dx.doi.org/10.1093/bib/1.2.117.

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Sherbakov, Dmitry, Yuri Panchin, and Ancha Baranova. "Extracting Evolutionary Insights Using Bioinformatics." International Journal of Genomics 2013 (2013): 1–2. http://dx.doi.org/10.1155/2013/376235.

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Pal, S. K., S. Bandyopadhyay, and S. S. Ray. "Evolutionary computation in bioinformatics: a review." IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews) 36, no. 5 (2006): 601–15. http://dx.doi.org/10.1109/tsmcc.2005.855515.

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Raymer, Michael L. "Book Review: Evolutionary Computation in Bioinformatics." Genetic Programming and Evolvable Machines 6, no. 2 (2005): 229–30. http://dx.doi.org/10.1007/s10710-005-7581-6.

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Pinho, Jorge, João Luis Sobral, and Miguel Rocha. "Parallel evolutionary computation in bioinformatics applications." Computer Methods and Programs in Biomedicine 110, no. 2 (2013): 183–91. http://dx.doi.org/10.1016/j.cmpb.2012.10.001.

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Ye, Kai, Gert Vriend, and Adriaan P. IJzerman. "Tracing evolutionary pressure." Bioinformatics 24, no. 7 (2008): 908–15. http://dx.doi.org/10.1093/bioinformatics/btn057.

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Li, Shan, Liying Kang, and Xing-Ming Zhao. "A Survey on Evolutionary Algorithm Based Hybrid Intelligence in Bioinformatics." BioMed Research International 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/362738.

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With the rapid advance in genomics, proteomics, metabolomics, and other types of omics technologies during the past decades, a tremendous amount of data related to molecular biology has been produced. It is becoming a big challenge for the bioinformatists to analyze and interpret these data with conventional intelligent techniques, for example, support vector machines. Recently, the hybrid intelligent methods, which integrate several standard intelligent approaches, are becoming more and more popular due to their robustness and efficiency. Specifically, the hybrid intelligent approaches based on evolutionary algorithms (EAs) are widely used in various fields due to the efficiency and robustness of EAs. In this review, we give an introduction about the applications of hybrid intelligent methods, in particular those based on evolutionary algorithm, in bioinformatics. In particular, we focus on their applications to three common problems that arise in bioinformatics, that is, feature selection, parameter estimation, and reconstruction of biological networks.
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Leman, S. C., M. K. Uyenoyama, M. Lavine, and Y. Chen. "The evolutionary forest algorithm." Bioinformatics 23, no. 15 (2007): 1962–68. http://dx.doi.org/10.1093/bioinformatics/btm264.

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Harmon, L. J., J. T. Weir, C. D. Brock, R. E. Glor, and W. Challenger. "GEIGER: investigating evolutionary radiations." Bioinformatics 24, no. 1 (2007): 129–31. http://dx.doi.org/10.1093/bioinformatics/btm538.

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Dissertations / Theses on the topic "Evolutionary bioinformatics"

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Chen, Lei. "Construction of Evolutionary Tree Models for Oncogenesis of Endometrial Adenocarcinoma." Thesis, University of Skövde, School of Humanities and Informatics, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-25.

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<p>Endometrial adenocarcinoma (EAC) is the fourth leading cause of carcinoma in woman worldwide, but not much is known about genetic factors involved in this complex disease. During the EAC process, it is well known that losses and gains of chromosomal regions do not occur completely at random, but partly through some flow of causality. In this work, we used three different algorithms based on frequency of genomic alterations to construct 27 tree models of oncogenesis. So far, no study about applying pathway models to microsatellite marker data had been reported. Data from genome–wide scans with microsatellite markers were classified into 9 data sets, according to two biological approaches (solid tumor cell and corresponding tissue culture) and three different genetic backgrounds provided by intercrossing the susceptible rat BDII strain and two normal rat strains. Compared to previous study, similar conclusions were drawn from tree models that three main important regions (I, II and III) and two subordinate regions (IV and V) are likely to be involved in EAC development. Further information about these regions such as their likely order and relationships was produced by the tree models. A high consistency in tree models and the relationship among p19, Tp53 and Tp53 inducible</p><p>protein genes provided supportive evidence for the reliability of results.</p>
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Birkmeier, Bettina. "Integrating Prior Knowledge into the Fitness Function of an Evolutionary Algorithm for Deriving Gene Regulatory Networks." Thesis, University of Skövde, School of Humanities and Informatics, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-31.

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<p>The topic of gene regulation is a major research area in the bioinformatics community. In this thesis prior knowledge from Gene Ontology in the form of templates is integrated into the fitness function of an evolutionary algorithm to predict gene regulatory networks. The resulting multi-objective fitness functions are then tested with MAPK network data taken from KEGG to evaluate their respective performances. The results are presented and analyzed. However, a clear tendency cannot be observed. The results are nevertheless promising and can provide motivation for further research in that direction. Therefore different ideas and approaches are suggested for future work.</p>
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Mongin, Emmanuel. "An evolutionary approach to long-range regulation." Thesis, McGill University, 2010. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=92333.

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Long-range regulatory regions play important functions in the regulation of transcription and are particularly involved in the precise spatio-temporal expression of target genes. Such regions have specific characteristics, among which is their ability to regulate many target genes that can be located up to 1Mb from the transcription start site. The prediction and functional characterization of such regions remains an open problem. Evolutionary approaches have been developed to detect regulatory regions that are under purifying selection. However, little has been done with regards to the impact of long-range regulation on genome evolution.<br>This thesis focuses on three different aspects of long-range regulation: i/ First we develop a method that predicts regions particularly prone to the fixation of evolutionary breakpoints. We discuss the results obtained in the context of long-range regulation and show that this type of regulation is a major factor shaping vertebrate genomes in evolution. ii/ The second project aims at predicting functional interactions between regulatory regions and target genes based on the observation of evolutionary rearrangements in various vertebrate species. We show how this approach produces a biologically meaningful prediction dataset that will be useful to researchers working on regulation. iii/ Third, we focus on the in vivo characterization of regulatory regions. We present a powerful and reliable enhancer detection pipeline composed of an in silico approach to predict putative enhancers and an in vivo method to functionally characterize the expression specificity of predicted regions in the developing medaka fish.<br>The results presented in this thesis contribute to different areas of research such as a better understanding of evolutionary dynamics related to evolutionary rearrangements and to a better in silico and in vivo characterization of cis-regulatory regions.<br>La régulation longue distance a d'importantes fonctions dans la régulation de la transcription et est particulièrement impliquée dans la régulation spatiale et temporelle des gènes cibles. Ces régions ont des caractèristiques spécifiques telles que la capacité de contrôler different gènes à des distances jusqu'a 1Mb du site d'initiation de la transcription. La prédiction et la caractérisation fonctionelle de ces regions restent un problème d'actualité. Des approches évolutionaires ont été d´eveloppées pour détecter les régions sous pression de sélection. En revanche, peu a été fait en rapport avec l'impact de la régulation de longue distance sur l'évolution du génome.<br>Cette thèse se concentre sur trois differents aspects de la régulation longue distance: i/ Premièrement, nous developpons une méthode de prédiction des regions particulièrement sujettes à la fixation des réarrangements de l'évolution. Nous étudions les résultats obtenus dans le contexte de la régulation longue distance et nous montrons que ce type de régulation est un composant majeur dans le façonnement du génome au cours de l'évolution. ii/ Le second projet à pour but de prédire les interactions fonctionnelles entre les régions de régulation et leur gènes cible à partir de l'observation de réarrangements de l'évolution dans differentes espèces. Nous montrons comment une telle approche produit des resultants biologiquement significatifs qui seront particulièrement utiles aux chercheurs travaillant dans le domaine de la régulation. iii/ Troisièmement, nous nous concentrons sur la caractérisation fonctionnelle in vivo des regions régulatrices. Nous présentons une méthode fiable de détection des enhancers composée d'une approche informatique pour la prédiction de ces régions et d'une approche biologique pour caractériser fonctionnellement les spécificités d'expression de ces régions dans le poisson medaka.<br>Les résultats présentés dans cette thèse contribuent à une meilleure comprehension des dynamiques d'évolution en relation avec la régulation longue distance et une meilleure prédiction et caractérisation fonctionnelle de ces régions régulatrices.
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de, Castro Pereira Vinicius Moll. "Evolutionary dynamics of mobile DNA : bioinformatics and molecular case studies." Thesis, Imperial College London, 2006. http://hdl.handle.net/10044/1/11993.

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McGee, Kate. "Evolutionary Factors Shaping Haplotype and Nucleotide Diversity in Humans and Malaria." NCSU, 2008. http://www.lib.ncsu.edu/theses/available/etd-01102008-104027/.

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Cheaper and more rapid DNA sequencing has led to the accumulation of large amounts of genetic data and has fueled the development of new methods to analyze this data. Using population genetics theory and computational methods we can explore the evolutionary forces that shape genetic variation within and among populations of humans and malaria parasites. Demographic events such as population size change influence current patterns of genetic variation. Accounting for the demographic history of a population is critical in the interpretation of population genetic analyses, particularly in detecting of regions under selection and in making inferences about linkage disequilibrium. Characterizing how recombination rates evolve is critical for the efficient design of association studies and, in turn, the understanding of the genetics behind complex phenotypes. In malaria parasites, recombination is a key element in the creation of a wide array of antigens, which help invade host cells. We examine patterns of genetic variation in humans and malaria and explore how demographic history and recombination rates affect these patterns.
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Nystedt, Björn. "Evolutionary Processes and Genome Dynamics in Host-Adapted Bacteria." Doctoral thesis, Uppsala universitet, Molekylär evolution, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-107720.

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Many bacteria live in close association with other organisms such as plants and animals, with important implications for both health and disease. This thesis investigates bacteria that are well adapted to live inside an animal host, and describes the molecular evolutionary processes underlying host-adaptation, based on bacterial genome comparisons. Insect-transmitted bacteria of the genus Bartonella infect the red blood cells of mammals, and we investigate host adaptation and genome evolution in this genus. In Bartonella, many host-interaction systems are encoded in a highly variable chromosomal segment previously shown to be amplified and packaged into bacteriophage particles. Among all genes imported into the Bartonella ancestor, we identify the short gene cluster encoding these phage particles as the most evolutionary conserved, indicating a strong selective advantage and a role in niche adaptation. We also provide an overview of the remarkable evolutionary dynamics of type IV and type V secretion systems, including a detailed analysis of the type IV secretion system trw. Our results highlight the importance of recombination and gene conversion in the evolution of host-adaptation systems, and reveal how these mutational mechanisms result in strikingly different outcomes depending on the selective constraints. In the insect endosymbionts Buchnera and Blochmannia, we show that genes frameshifted at poly(A) tracts can remain functional due to transcriptional slippage. Selection against poly(A) tracts is very inefficient in these genomes compared to other bacteria, and we discuss why this can lead to increased rates of gene loss. Using the human pathogen Helicobacter pylori as a model, we provide a deeper understanding of why highly expressed genes evolve slowly. This thesis emphasizes the power of using complete genome sequences to study evolutionary processes. In particular, we argue that knowledge about the complex evolution of duplicated gene segments is crucial to understand host adaptation in bacteria.
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Loewe, Laurence. "Evolutionary bioinformatics predicting genetic stability of asexual genomes by global computing /." [S.l.] : [s.n.], 2003. http://deposit.ddb.de/cgi-bin/dokserv?idn=969894201.

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Young, Adrian. "The Evolutionary Feedback between Genetic Conflict and Genome Architecture." Thesis, Harvard University, 2014. http://dissertations.umi.com/gsas.harvard:11482.

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The advent of separate sexes set the stage for dramatic evolutionary innovation across a wide range of taxa. Much of this innovation is attributable to divergent evolutionary interests between now distinct sub-populations of males and females. Trade-offs inherent to these divergent life histories, coupled with a common genome, conspire to limit natural selection's ability to simultaneously maximize the fitness of both sexes. Such conflict between the sexes has therefore largely shaped the history of the genomes of sexual taxa. However, various aspects of the genomic environment&mdash;including genes' spatial distributions, abilities to regulate their expression, and rates of recombination&mdash;also feed back to influence future sex-specific evolutionary trajectories. Using various genomic resources and transcriptome sequences for the lab mouse, I test several theoretical predictions regarding this feedback between genetic conflict and features of genomic organization.
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Jin, Bo. "Evolutionary Granular Kernel Machines." Digital Archive @ GSU, 2007. http://digitalarchive.gsu.edu/cs_diss/15.

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Kernel machines such as Support Vector Machines (SVMs) have been widely used in various data mining applications with good generalization properties. Performance of SVMs for solving nonlinear problems is highly affected by kernel functions. The complexity of SVMs training is mainly related to the size of a training dataset. How to design a powerful kernel, how to speed up SVMs training and how to train SVMs with millions of examples are still challenging problems in the SVMs research. For these important problems, powerful and flexible kernel trees called Evolutionary Granular Kernel Trees (EGKTs) are designed to incorporate prior domain knowledge. Granular Kernel Tree Structure Evolving System (GKTSES) is developed to evolve the structures of Granular Kernel Trees (GKTs) without prior knowledge. A voting scheme is also proposed to reduce the prediction deviation of GKTSES. To speed up EGKTs optimization, a master-slave parallel model is implemented. To help SVMs challenge large-scale data mining, a Minimum Enclosing Ball (MEB) based data reduction method is presented, and a new MEB-SVM algorithm is designed. All these kernel methods are designed based on Granular Computing (GrC). In general, Evolutionary Granular Kernel Machines (EGKMs) are investigated to optimize kernels effectively, speed up training greatly and mine huge amounts of data efficiently.
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Hudson, Corey M. "Informatic approaches to evolutionary systems biology." Thesis, University of Missouri - Columbia, 2014. http://pqdtopen.proquest.com/#viewpdf?dispub=3577951.

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<p> The sheer complexity of evolutionary systems biology requires us to develop more sophisticated tools for analysis, as well as more probing and biologically relevant representations of the data. My research has focused on three aspects of evolutionary systems biology. I ask whether a gene&rsquo;s position in the human metabolic network affects the degree to which natural selection prunes variation in that gene. Using a novel orthology inference tool that uses both sequence similarity and gene synteny, I inferred orthologous groups of genes for the full genomes of 8 mammals. With these orthologs, I estimated the selective constraint (the ratio of non-synonymous to synonymous nucleotide substitutions) on 1190 (or 80.2%) of the genes in the metabolic network using a maximum likelihood model of codon evolution and compared this value to the betweenness centrality of each enzyme (a measure of that enzyme&rsquo;s relative global position in the network). Second, I have focused on the evolution of metabolic systems in the presence of gene and genome duplication. I show that increases in a particular gene&rsquo;s copy number are correlated with limiting metabolic flux in the reaction associated with that gene. Finally, I have investigated the proliferative cell programs present in 6 different cancers (breast, colorectal, gastrointestinal, lung, oral squamous and prostate cancers). I found an overabundance of genes that share expression between cancer and embryonic tissue and that these genes form modular units within regulatory, proteininteraction, and metabolic networks. This despite the fact that these genes, as well as the proteins they encode and reactions they catalyze show little overlap among cancers, suggesting parallel independent reversion to an embryonic pattern of gene expression.</p>
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Books on the topic "Evolutionary bioinformatics"

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Evolutionary bioinformatics. 2nd ed. Springer, 2011.

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Forsdyke, Donald R. Evolutionary Bioinformatics. Springer US, 2006. http://dx.doi.org/10.1007/978-0-387-33419-6.

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Forsdyke, Donald R. Evolutionary Bioinformatics. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28755-3.

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Forsdyke, Donald R. Evolutionary Bioinformatics. Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-7771-7.

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Haubold, Bernhard, and Angelika Börsch-Haubold. Bioinformatics for Evolutionary Biologists. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67395-0.

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Fogel, Gary B. Evolutionary computation in bioinformatics. Morgan Kaufmann, 2003.

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Marchiori, Elena, Jason H. Moore, and Jagath C. Rajapakse, eds. Evolutionary Computation,Machine Learning and Data Mining in Bioinformatics. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-71783-6.

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Pizzuti, Clara, Marylyn D. Ritchie, and Mario Giacobini, eds. Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20389-3.

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Pizzuti, Clara, Marylyn D. Ritchie, and Mario Giacobini, eds. Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01184-9.

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Vanneschi, Leonardo, William S. Bush, and Mario Giacobini, eds. Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37189-9.

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Book chapters on the topic "Evolutionary bioinformatics"

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Forsdyke, Donald R. "Memory – A Phenomenon of Arrangement." In Evolutionary Bioinformatics. Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-7771-7_1.

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Forsdyke, Donald R. "Chargaff’s GC rule." In Evolutionary Bioinformatics. Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-7771-7_10.

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Forsdyke, Donald R. "Homostability." In Evolutionary Bioinformatics. Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-7771-7_11.

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Forsdyke, Donald R. "Conflict Resolution." In Evolutionary Bioinformatics. Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-7771-7_12.

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Forsdyke, Donald R. "Exons and Introns." In Evolutionary Bioinformatics. Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-7771-7_13.

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Forsdyke, Donald R. "Complexity." In Evolutionary Bioinformatics. Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-7771-7_14.

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Forsdyke, Donald R. "Self/Not-Self?" In Evolutionary Bioinformatics. Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-7771-7_15.

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Forsdyke, Donald R. "The Crowded Cytosol." In Evolutionary Bioinformatics. Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-7771-7_16.

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Forsdyke, Donald R. "Rebooting the Genome." In Evolutionary Bioinformatics. Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-7771-7_17.

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Forsdyke, Donald R. "The Fifth Letter." In Evolutionary Bioinformatics. Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-7771-7_18.

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Conference papers on the topic "Evolutionary bioinformatics"

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Vega-Rodríguez, Miguel A., Jose M. Chaves-González, David L. González-Álvarez, and Sergio Santander-Jiménez. "Session details: International workshop on evolutionary computation in bioinformatics." In GECCO '13: Genetic and Evolutionary Computation Conference. ACM, 2013. http://dx.doi.org/10.1145/3253594.

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Widera, Paweł, Jaume Bacardit, Natalio Krasnogor, Carlos García-Martínez, and Manuel Lozano. "Evolutionary symbolic discovery for bioinformatics, systems and synthetic biology." In the 12th annual conference comp. ACM Press, 2010. http://dx.doi.org/10.1145/1830761.1830842.

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Helmy, Tarek, and Zeehasham Rasheed. "Multi-category bioinformatics dataset classification using extreme learning machine." In 2009 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2009. http://dx.doi.org/10.1109/cec.2009.4983354.

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"PROTEIN DOMAIN PHYLOGENIES - Information Theory and Evolutionary Dynamics." In International Conference on Bioinformatics. SciTePress - Science and and Technology Publications, 2010. http://dx.doi.org/10.5220/0002710101140122.

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Krasnogor, Natalio. "Darwin's magic: Evolutionary computation in nanoscience, bioinformatics and systems biology." In 2011 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2011. http://dx.doi.org/10.1109/cec.2011.5949589.

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"Gene expression stability at high evolutionary distances." In Plant Genetics, Genomics, Bioinformatics, and Biotechnology. Novosibirsk ICG SB RAS 2021, 2021. http://dx.doi.org/10.18699/plantgen2021-100.

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Haraldsson, Saemundur O., John R. Woodward, Alexander E. I. Brownlee, Albert V. Smith, and Vilmundur Gudnason. "Genetic improvement of runtime and its fitness landscape in a bioinformatics application." In GECCO '17: Genetic and Evolutionary Computation Conference. ACM, 2017. http://dx.doi.org/10.1145/3067695.3082526.

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STISSING, M., C. N. S. PEDERSEN, T. MAILUND, G. S. BRODAL, and R. FAGERBERG. "COMPUTING THE QUARTET DISTANCE BETWEEN EVOLUTIONARY TREES OF BOUNDED DEGREE." In 5th Asia-Pacific Bioinformatics Conference. PUBLISHED BY IMPERIAL COLLEGE PRESS AND DISTRIBUTED BY WORLD SCIENTIFIC PUBLISHING CO., 2007. http://dx.doi.org/10.1142/9781860947995_0013.

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Safaei, Javad, J´n Manuch, Arvind Gupta, Ladislav Stacho, and Steven Pelech. "Evolutionary Conservation of Human Phosphorylation Sites." In 2011 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2011. http://dx.doi.org/10.1109/bibm.2011.58.

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"CONVEX SHAPE RETRIEVAL FROM EDGE MAPS BY THE USE OF AN EVOLUTIONARY ALGORITHM." In International Conference on Bioinformatics. SciTePress - Science and and Technology Publications, 2010. http://dx.doi.org/10.5220/0002742502210225.

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