Relevant bibliographies by topics / Bioinformatics approach

Contents

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

Academic literature on the topic 'Bioinformatics approach'

Author: Grafiati
Published: 4 June 2021
Last updated: 8 February 2022

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Journal articles on the topic "Bioinformatics approach"

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Grant, Gregory. "Bioinformatics — The Machine Learning Approach." Computers & Chemistry 24, no. 1 (2000): 139–41. http://dx.doi.org/10.1016/s0097-8485(00)80015-7.

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Leunissen, J. "Bioinformatics: The Machine Learning Approach." Briefings in Bioinformatics 3, no. 3 (2002): 321–23. http://dx.doi.org/10.1093/bib/3.3.321.

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Radivojac, P. "Protein Structure Prediction: Bioinformatics Approach." Briefings in Bioinformatics 5, no. 2 (2004): 207–9. http://dx.doi.org/10.1093/bib/5.2.207.

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Gough, N. R. "A Bioinformatics Approach to Addiction." Science Signaling 1, no. 5 (2008): ec43-ec43. http://dx.doi.org/10.1126/stke.15ec43.

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Lempicki, Richard. "045 Bioinformatics approach in cryopreservation." Cryobiology 67, no. 3 (2013): 411. http://dx.doi.org/10.1016/j.cryobiol.2013.09.051.

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Ong, Quang, Phuc Nguyen, Nguyen Phuong Thao, and Ly Le. "Bioinformatics Approach in Plant Genomic Research." Current Genomics 17, no. 4 (2016): 368–78. http://dx.doi.org/10.2174/1389202917666160331202956.

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Garhwal, Abhimanyu Singh, and Wei Qi Yan. "BIIGA: Bioinformatics inspired image grouping approach." Multimedia Tools and Applications 78, no. 11 (2018): 14355–77. http://dx.doi.org/10.1007/s11042-018-6817-4.

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Anurogo, Dito, and Arli Aditya Parikesit. "Bioinformatics Approach towards Transcriptomics of Filaggrin." Journal of Agromedicine and Medical Sciences 2, no. 3 (2016): 8. http://dx.doi.org/10.19184/ams.v2i3.3254.

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Filaggrin, or filaments which combines protein, is one of the important structural protein that works for the development, maintenance, and the formation of the skin as an intact barrier. Filaggrin breakdown products regulate the hydration of the skin; contribute to the acidic pH of the skin, which in turn is essential for the activity of various proteases in the stratum corneum desquamation and lipid synthesis. Filaggrin produced by keratinocytes granular as a major precursor called profilaggrin, encoded by the FLG gene, located in the epidermal differentiation complex on chromosome 1 (1q21 locus). The locus contains a group of genes involved in epidermal differentiation. Filaggrin deficiency has some consequences on the organization and function of epidermal with important implications such as increased risk for atopic disease or a microbial infection. FLG mutation, a gene that encodes filaggrin, has been shown to cause ichthyosis vulgaris, increasing the risk of atopic dermatitis and other atopic diseases. This research examined the FLG gene based bioinformatics approach to search for conserved region of representative mammals that encode coding (m) and non-coding (nc) RNAs. Expected mRNA expression can be used as a diagnostic and therapeutic agent against deficiencies and filaggrin mutations.Key words: filaggrin, FLG, profilaggrin, filaggrin deficiency, bioinformatics.
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Uzun, Alper, Surendra Sharma, and James Padbury. "A Bioinformatics Approach to Preterm Birth." American Journal of Reproductive Immunology 67, no. 4 (2012): 273–77. http://dx.doi.org/10.1111/j.1600-0897.2012.01122.x.

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Rakesh, Palepu Narasimha. "A Data Science Approach to Bioinformatics." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (2021): 3860–69. http://dx.doi.org/10.22214/ijraset.2021.37221.

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Computer aided drug design (CADD) which uses the computational advance towards to develop, discover and scrutinize and examine drugs and alike biologically agile molecules. CADD is a specialized stream which uses the computational techniques to mimic drug-receptor interactions. CADD procedures are so much dependent on the tools of bioinformatics, databases & applications. There are so many advantages of computer aided drug discovery; it saves lot of time which is one of the main advantages followed by low cost and more accuracy. CADD required less manpower to work. There are different types of CADD such as ligand and structure based design. Objectives of the Computer aided drug design are to boost up the screening process, to test the rational of drug design, to efficiently screen and to remove hopeless ones as early as possible. In Drug designing the selected molecule should be organic small molecule, complementary in shape to the target and oppositely charged to the biomolecular target. The molecule will interacts and binds with the target which activates or inhibits the function of a biomolecule such as a protein or lipid. The main basic goal in the drug design is to forecast whether a given molecule will bind to target and if thus how strongly. Molecular mechanics techniques also used to provide the semi quantitative prediction of the binding affinity. These techniques use machine learning, linear regression, neural nets or other statistical methods to derive predictive binding affinity equations. Preferably, the computational technique will be able to forecast the affinity prior to a compound is synthesized, saving huge time and cost. Computational techniques have quickened the discovery by decreasing the number of iterations required and have often produced the novel structures.
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Dissertations / Theses on the topic "Bioinformatics approach"

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Keller, Jens. "Clustering biological data using a hybrid approach : Composition of clusterings from different features." Thesis, University of Skövde, School of Humanities and Informatics, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-1078.

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<p>Clustering of data is a well-researched topic in computer sciences. Many approaches have been designed for different tasks. In biology many of these approaches are hierarchical and the result is usually represented in dendrograms, e.g. phylogenetic trees. However, many non-hierarchical clustering algorithms are also well-established in biology. The approach in this thesis is based on such common algorithms. The algorithm which was implemented as part of this thesis uses a non-hierarchical graph clustering algorithm to compute a hierarchical clustering in a top-down fashion. It performs the graph clustering iteratively, with a previously computed cluster as input set. The innovation is that it focuses on another feature of the data in each step and clusters the data according to this feature. Common hierarchical approaches cluster e.g. in biology, a set of genes according to the similarity of their sequences. The clustering then reflects a partitioning of the genes according to their sequence similarity. The approach introduced in this thesis uses many features of the same objects. These features can be various, in biology for instance similarities of the sequences, of gene expression or of motif occurences in the promoter region. As part of this thesis not only the algorithm itself was implemented and evaluated, but a whole software also providing a graphical user interface. The software was implemented as a framework providing the basic functionality with the algorithm as a plug-in extending the framework. The software is meant to be extended in the future, integrating a set of algorithms and analysis tools related to the process of clustering and analysing data not necessarily related to biology.</p><p>The thesis deals with topics in biology, data mining and software engineering and is divided into six chapters. The first chapter gives an introduction to the task and the biological background. It gives an overview of common clustering approaches and explains the differences between them. Chapter two shows the idea behind the new clustering approach and points out differences and similarities between it and common clustering approaches. The third chapter discusses the aspects concerning the software, including the algorithm. It illustrates the architecture and analyses the clustering algorithm. After the implementation the software was evaluated, which is described in the fourth chapter, pointing out observations made due to the use of the new algorithm. Furthermore this chapter discusses differences and similarities to related clustering algorithms and software. The thesis ends with the last two chapters, namely conclusions and suggestions for future work. Readers who are interested in repeating the experiments which were made as part of this thesis can contact the author via e-mail, to get the relevant data for the evaluation, scripts or source code.</p>
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Dean, M. K. "Bioinformatics approach to predicting protein interactions." Thesis, University of Essex, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.275862.

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Campbell, M. P. "A bioinformatics approach to protein-protein interactions." Thesis, University of Essex, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.426014.

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Hervás, Fernàndez Sergi. "Population genomics in Drosophila melanogaster: a bioinformatics approach." Doctoral thesis, Universitat Autònoma de Barcelona, 2018. http://hdl.handle.net/10803/665851.

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High-throughput sequencing technologies are allowing the description of genome-wide variation patterns for an ever-growing number of organisms. However, we still lack a thorough comprehension of the relative amount of different types of genetic variation, their phenotypic effects, and the detection and quantification of distinct selection regimes acting on genomes. The recent compilation of more than one thousand of worldwide wild-derived Drosophila melanogaster genome sequences reassembled using a standardized pipeline (Drosophila Genome Nexus, DGN, Lack et al. 2015, 2016) provides a unique resource to test molecular population genetics hypotheses, and ultimately understand the evolutionary dynamics of genetic variation in the populations. Besides, the increasing amount of genomic data available requires the continuous development and optimization of bioinformatics tools able to handle and analyze such information. Thus, the development and implementation of new biologically-oriented software addressing several steps from data acquisition, filtering, processing, display or analysis to the final reporting step is a constantly growing need, especially in fields dealing with large data sets, such as population genomics. This thesis is conceived as a comprehensive bioinformatics and population genomics project. It is centered in the development and application of bioinformatics tools for the analysis and visualization of nucleotide variation patterns and the detection of selective events in the genome of D. melanogaster, using the DGN data. The main goal is accomplished in three sequential steps: (i) capture the evolutionary properties of the analyzed sequences (i.e., create a catalog of population genetics metrics) and implement a tool for the graphical display of such information; (ii) develop a statistical package for the computation of the diverse selection regimes acting on genomes (positive and purifying selection), and finally (iii) perform an initial population genomics analysis in D. melanogaster using the previously developed tools. The common approach applied to process the data, starting at the assembly of genome sequences and ending up at the estimates of population genetics metrics, allows performing, for the first time, a comprehensive comparison and interpretation of results using samples from five continents. Overall, this work provides a global overview of the nucleotide variation and adaptation patterns along the genome, and a general assessment of the relative impact of the major genomic determinants of genetic variation, in Drosophila meta-populations with different geographical origin.
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Dampier, William Tozeren Aydin. "Analysis of host-pathogen interactions : a bioinformatics approach /." Philadelphia, Pa. : Drexel University, 2010. http://hdl.handle.net/1860/3249.

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Markstedt, Olof. "Kubernetes as an approach for solving bioinformatic problems." Thesis, Uppsala universitet, Institutionen för biologisk grundutbildning, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-330217.

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The cluster orchestration tool Kubernetes enables easy deployment and reproducibility of life science research by utilizing the advantages of the container technology. The container technology allows for easy tool creation, sharing and runs on any Linux system once it has been built. The applicability of Kubernetes as an approach to run bioinformatic workflows was evaluated and resulted in some examples of how Kubernetes and containers could be used within the field of life science and how they should not be used. The resulting examples serves as proof of concepts and the general idea of how implementation is done. Kubernetes allows for easy resource management and includes automatic scheduling of workloads. It scales rapidly and has some interesting components that are beneficial when conducting life science research.
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Hillerton, Thomas. "Predicting adverse drug reactions in cancer treatment using a neural network based approach." Thesis, Högskolan i Skövde, Institutionen för biovetenskap, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-15659.

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Pettersson, Fredrik. "A multivariate approach to computational molecular biology." Doctoral thesis, Umeå : Univ, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-609.

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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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Damasceno, Andreia Goreti Marques. "Mapping UPR elements in male reproductive system: a bioinformatics approach." Master's thesis, Universidade de Aveiro, 2017. http://hdl.handle.net/10773/22006.

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Mestrado em Biomedicina Molecular<br>A Unfolded Protein Response (UPR) é um mecanismo de defesa crucial que protege as células contra o enrolamento incorreto de proteínas, através da ativação de três sensores principais: ATF6, PERK e IRE1. Cada sensor guia a célula em diferentes mecanismos de transdução de sinal culminando na produção de fatores de transcrição que, por sua vez, regulam genes que aumentam a capacidade da célula corrigir a conformação de proteínas mal enoveladas, impedindo, em último caso, a sua agregação. Nos últimos anos a UPR tem sido associada a várias patologias. Na infertilidade masculina, poucos estudos se têm focado na influência dos componentes da UPR, sendo importante numa primeira abordagem, a identificação destes componentes no sistema reprodutor masculino. Através de pesquisa de bases de dados e com abordagens bioinformáticas, com o objetivo de identificar potenciais candidatos associados a fenótipos de infertilidade, foi realizada uma recolha de proteínas UPR no testículo, espermatozoide e plasma seminal. De forma a determinar possíveis alvos envolvidos na infertilidade masculina, as interações proteínaproteína foram analisadas, destacando-se 6 proteínas com elevado grau de interação: HSP90AA1, HSPA5, SEC61A1, VCP, PERK e ATF4. Considerando ainda a sua importância funcional, as proteínas efetoras da via PERK, a GADD34 e a eIF2 foram destacadas para estudos de deteção experimentais. Neste sentido, foi confirmada pela primeira vez a presença das proteínas PERK e GADD34 em espermatozoides humanos. Estes resultados constituem o primeiro passo fundamental para avançar para estudos mais aprofundados relativamente à expressão e níveis de atividade destes candidatos, procurando perceber a contribuição dos mesmos na via de sinalização UPR e a sua eventual desregulação na infertilidade masculina.<br>The unfolded protein response (UPR) is an essential cell defense response against defects in protein folding and it is mainly triggered by the activation of ATF6, PERK and IRE1. Each sensor leads to different signal transduction mechanisms through the production of transcription factors that, in turn, regulate genes that increase the cell's ability to correct conformation of poorly folded proteins, ultimately hindering their aggregation. The past years shed light on the role of the UPR in several diseases. Regarding male infertility, few studies have focused on the implications of UPR components, hence the need to a prior approach concerning the presence of these components on the male reproductive system. Through a database search and using bioinformatics approaches, with the aim of identifying potential candidates associated with infertility phenotypes, a collection of UPR proteins in the testis, spermatozoa and seminal plasma was performed. To determine potential targets to scrutinize possible involvement in male infertility, a protein-protein interaction network analysis was performed, depicting 6 key proteins highly interconnected: HSP90AA1, HSPA5, SEC61A1, VCP, PERK and ATF4. Considering their functional value, the effector proteins of the PERK pathway, GADD34 and eIF2 were highlighted for experimental studies. Thus, the presence of the PERK and GADD34 were confirmed for the first time in human spermatozoa. These results constitute the first fundamental step towards further studies on the expression and activity levels of these candidates and understand their contribution to the UPR signaling pathway and their possible deregulation in male infertility
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Books on the topic "Bioinformatics approach"

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Ye, Shui Qing. Bioinformatics: A practical approach. Chapman & Hall/CRC, 2008.

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Søren, Brunak, ed. Bioinformatics: The machine learning approach. 2nd ed. MIT Press, 2001.

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Søren, Brunak, ed. Bioinformatics: The machine learning approach. MIT Press, 1998.

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Clair, Caroline St. Exploring bioinformatics: A project-based approach. Jones & Bartlett Learning, 2014.

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author, Pevzner Pavel, ed. Bioinformatics algorithms: An active learning approach. Active Learning Publishers, 2014.

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Jonathan, Visick, ed. Introduction to bioinformatics: A project based approach. Jones and Bartlett Publishers, 2009.

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missing], [name. Introduction to bioinformatics: A theoretical and practical approach. Humana Press, 2002.

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Inge, Jonassen, and Taylor W. R, eds. Protein bioinformatics: An algorithmic approach to sequence and structure analysis. J. Wiley & Sons, 2004.

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Vidyasagar, Mathukumalli. Computational Cancer Biology: An Interaction Network Approach. Springer London, 2012.

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Chen, Ming, and Ralf Hofestädt, eds. Approaches in Integrative Bioinformatics. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-41281-3.

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Book chapters on the topic "Bioinformatics approach"

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Lu, Aiping, Dan Li, Yan Liu, and Haiyun Wang. "Application of Bioinformatics to Asthma." In Genomic Approach to Asthma. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8764-6_16.

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Schwab, C. H., S. Handschuh, A. Teckentrup, et al. "A systematic approach to finding new lead structures having biological activity." In Bioinformatics. Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/bfb0033215.

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Schwikowski, Benno, and Martin Vingron. "A clustering approach to Generalized Tree Alignment with application to Alu repeats." In Bioinformatics. Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/bfb0033210.

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Yilmaz-Temel, Hulya, and Fazilet Vardar-Sukan. "An Engineering Approach to Bioinformatics and Its Applications." In Plant Bioinformatics. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67156-7_18.

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Kushwaha, Hemant Ritturaj, and Indira Ghosh. "Bioinformatics Approach for Finding Target Protein in Infectious Disease." In Translational Bioinformatics. Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-5811-7_10.

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Richardson, Jane S. "All-Atom Contacts: A New Approach to Structure Validation." In Structural Bioinformatics. John Wiley & Sons, Inc., 2005. http://dx.doi.org/10.1002/0471721204.ch15.

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Palakolanu, Sudhakar Reddy, Vincent Vadez, Sreenivasulu Nese, and P. B. Kavi Kishor. "Tackling the Heat-Stress Tolerance in Crop Plants: A Bioinformatics Approach." In Agricultural Bioinformatics. Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-1880-7_3.

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Teschendorff, Andrew E., and Martin Widschwendter. "A Network Systems Approach to Identify Functional Epigenetic Drivers in Cancer." In Translational Bioinformatics. Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-7975-4_7.

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Martinez-del-Rincon, Jesus, Jerome Thevenon, Romain Dieny, and Jean-Christophe Nebel. "Bioinformatics-Motivated Approach to Stereo Matching." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-32350-8_11.

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Salvatierra, Karina, and Hector Florez. "Bioinformatics Approach to Analyze Influenza Viruses." In Communications in Computer and Information Science. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00353-1_39.

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Conference papers on the topic "Bioinformatics approach"

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Labusca, Luminita, and Kaveh Mashayekhi. "A Bioinformatics Approach in Discovery Science." In 2012 Third International Conference on Emerging Security Technologies (EST). IEEE, 2012. http://dx.doi.org/10.1109/est.2012.13.

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MASULLI, FRANCESCO. "BICLUSTERING BIOINFORMATICS DATA SETS: A POSSIBILISTIC APPROACH." In Proceedings of the 6th International Workshop on Data Analysis in Astronomy “Livio Scarsi”. WORLD SCIENTIFIC, 2007. http://dx.doi.org/10.1142/9789812779458_0028.

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Revett, Kenneth. "A Bioinformatics Based Approach to Behavioural Biometrics." In 2007 Frontiers in the Convergence of Bioscience and Information Technologies. IEEE, 2007. http://dx.doi.org/10.1109/fbit.2007.143.

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Xu, Guoshi, Yin Luo, Huashan Yu, and Zhuoqun Xu. "An Approach to SOA-Based Bioinformatics Grid." In 2006 IEEE Asia-Pacific Conference on Services Computing (APSCC'06). IEEE, 2006. http://dx.doi.org/10.1109/apscc.2006.25.

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Pollettini, Juliana Tarossi, and Alessandra Alaniz Macedo. "Poster: Chronic disease prevention: A Translational Bioinformatics approach." In 2011 IEEE 1st International Conference on Computational Advances in Bio and Medical Sciences (ICCABS). IEEE, 2011. http://dx.doi.org/10.1109/iccabs.2011.5729911.

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CAMACHO, H., and A. SALHI. "A REDUNDANCY DETECTION APPROACH TO MINING BIOINFORMATICS DATA." In Computer Aided Methods in Optimal Design and Operations. WORLD SCIENTIFIC, 2006. http://dx.doi.org/10.1142/9789812772954_0010.

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Stockinger, Heinz, Marco Pagni, Lorenzo Cerutti, and Laurent Falquet. "Grid Approach to Embarrassingly Parallel CPU-Intensive Bioinformatics Problems." In 2006 Second IEEE International Conference on e-Science and Grid Computing (e-Science'06). IEEE, 2006. http://dx.doi.org/10.1109/e-science.2006.261142.

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Kanrar, Soumen. "Fast load balancing approach for growing clusters by Bioinformatics." In 2016 International conference on Signal Processing, Communication, Power and Embedded System (SCOPES). IEEE, 2016. http://dx.doi.org/10.1109/scopes.2016.7955857.

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Kann, Maricel G. "Invited: A protein domain-centric approach to translational bioinformatics." In 2012 IEEE 2nd International Conference on Computational Advances in Bio and Medical Sciences (ICCABS). IEEE, 2012. http://dx.doi.org/10.1109/iccabs.2012.6182622.

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Wong, R. K., and W. M. Shui. "Utilizing multiple bioinformatics information sources: an XML database approach." In Proceedings 2nd Annual IEEE International Symposium on Bioinformatics and Bioengineering (BIBE 2001). IEEE, 2001. http://dx.doi.org/10.1109/bibe.2001.974414.

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Reports on the topic "Bioinformatics approach"

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Lower, Steven, K. Nanobiogeochemistry of Microbe/Mineral Interactions: A Force Microscopy and Bioinformatics Approach. Office of Scientific and Technical Information (OSTI), 2006. http://dx.doi.org/10.2172/893095.

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Lower, Steven, K. Nanobiogeochemistry of Microbe/Mineral Interactions: A Force Microscopy and Bioinformatics Approach. Office of Scientific and Technical Information (OSTI), 2005. http://dx.doi.org/10.2172/860984.

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