Relevant bibliographies by topics / Computational approaches

Academic literature on the topic 'Computational approaches'

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Published: 11 March 2023

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Journal articles on the topic "Computational approaches"

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McClelland, James L., and David C. Plaut. "Computational approaches to cognition: top-down approaches." Current Opinion in Neurobiology 3, no. 2 (April 1993): 209–16. http://dx.doi.org/10.1016/0959-4388(93)90212-h.

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Rustad, J. R., W. Dzwinel, and D. A. Yuen. "Computational Approaches to Nanomineralogy." Reviews in Mineralogy and Geochemistry 44, no. 1 (January 1, 2001): 191–216. http://dx.doi.org/10.2138/rmg.2001.44.06.

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Stephan, Klaas Enno, and Christoph Mathys. "Computational approaches to psychiatry." Current Opinion in Neurobiology 25 (April 2014): 85–92. http://dx.doi.org/10.1016/j.conb.2013.12.007.

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Roman Čerešňák, Karol Matiaško, and Adam Dudáš. "Various Approaches Proposed for Eliminating Duplicate Data in a System." Communications - Scientific letters of the University of Zilina 23, no. 4 (October 1, 2021): A223—A232. http://dx.doi.org/10.26552/com.c.2021.4.a223-a232.

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The growth of big data processing market led to an increase in the overload of computation data centers, change of methods used in storing the data, communication between the computing units and computational time needed to process or edit the data. Methods of distributed or parallel data processing brought new problems related to computations with data which need to be examined. Unlike the conventional cloud services, a tight connection between the data and the computations is one of the main characteristics of the big data services. The computational tasks can be done only if relevant data are available. Three factors, which influence the speed and efficiency of data processing are - data duplicity, data integrity and data security. We are motivated to study the problems related to the growing time needed for data processing by optimizing these three factors in geographically distributed data centers.
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Hemmo, Meir, and Orly Shenker. "The Multiple-Computations Theorem and the Physics of Singling Out a Computation." Monist 105, no. 2 (March 9, 2022): 175–93. http://dx.doi.org/10.1093/monist/onab030.

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Abstract The problem of multiple-computations discovered by Hilary Putnam presents a deep difficulty for functionalism (of all sorts, computational and causal). We describe in outline why Putnam’s result, and likewise the more restricted result we call the Multiple-Computations Theorem, are in fact theorems of statistical mechanics. We show why the mere interaction of a computing system with its environment cannot single out a computation as the preferred one amongst the many computations implemented by the system. We explain why nonreductive approaches to solving the multiple-computations problem, and in particular why computational externalism, are dualistic in the sense that they imply that nonphysical facts in the environment of a computing system single out the computation. We discuss certain attempts to dissolve Putnam’s unrestricted result by appealing to systems with certain kinds of input and output states as a special case of computational externalism, and show why this approach is not workable without collapsing to behaviorism. We conclude with some remarks about the nonphysical nature of mainstream approaches to both statistical mechanics and the quantum theory of measurement with respect to the singling out of partitions and observables.
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Garg, Diksha, Ankita Jiwan, and Shailendra Singh. "Computational Approaches for Variant Identification." International Journal of Computer Applications 165, no. 8 (May 17, 2017): 18–24. http://dx.doi.org/10.5120/ijca2017913970.

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Brogi. "Computational Approaches for Drug Discovery." Molecules 24, no. 17 (August 22, 2019): 3061. http://dx.doi.org/10.3390/molecules24173061.

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HOU, Yan-Yan. "Computational approaches to microRNA discovery." Hereditas (Beijing) 30, no. 6 (July 4, 2008): 687–96. http://dx.doi.org/10.3724/sp.j.1005.2008.00687.

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Cochrane, Peter, David W. Kuecker, and Carl H. Smith. "Learning and Geometry: Computational Approaches." Mathematical Gazette 81, no. 490 (March 1997): 183. http://dx.doi.org/10.2307/3618830.

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Yousef, Malik, Naim Najami, Loai Abedallah, and Waleed Khalifa. "Computational Approaches for Biomarker Discovery." Journal of Intelligent Learning Systems and Applications 06, no. 04 (2014): 153–61. http://dx.doi.org/10.4236/jilsa.2014.64012.

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Dissertations / Theses on the topic "Computational approaches"

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Banks, Eric 1976. "Computational approaches to gene finding." Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/81523.

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Shutova, Ekaterina. "Computational approaches to figurative language." Thesis, University of Cambridge, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.609681.

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Wynn, Hamish Henry. "Computational approaches to peptidomimetic design." Thesis, University of Cambridge, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.621332.

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Traore, Seydou. "Computational approaches toward protein design." Thesis, Toulouse, INSA, 2014. http://www.theses.fr/2014ISAT0033/document.

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Le Design computationnel de protéines, en anglais « Computational Protein Design » (CPD), est un champ derecherche récent qui vise à fournir des outils de prédiction pour compléter l'ingénierie des protéines. En effet,outre la compréhension théorique des propriétés physico-chimiques fondamentales et fonctionnelles desprotéines, l’ingénierie des protéines a d’importantes applications dans un large éventail de domaines, y comprisdans la biomédecine, la biotechnologie, la nanobiotechnologie et la conception de composés respectueux del’environnement. Le CPD cherche ainsi à accélérer le design de protéines dotées des propriétés désirées enpermettant le traitement d’espaces de séquences de large taille tout en limitant les coûts financier et humain auniveau expérimental.Pour atteindre cet objectif, le CPD requière trois ingrédients conçus de manière appropriée: 1) une modélisationréaliste du système à remodeler; 2) une définition précise des fonctions objectives permettant de caractériser lafonction biochimique ou la propriété physico-chimique cible; 3) et enfin des méthodes d'optimisation efficacespour gérer de grandes tailles de combinatoire.Dans cette thèse, nous avons abordé le CPD avec une attention particulière portée sur l’optimisationcombinatoire. Dans une première série d'études, nous avons appliqué pour la première fois les méthodesd'optimisation de réseaux de fonctions de coût à la résolution de problèmes de CPD. Nous avons constaté qu’encomparaison des autres méthodes existantes, nos approches apportent une accélération du temps de calcul parplusieurs ordres de grandeur sur un large éventail de cas réels de CPD comprenant le design de la stabilité deprotéines ainsi que de complexes protéine-protéine et protéine-ligand. Un critère pour définir l'espace demutations des résidus a également été introduit afin de biaiser les séquences vers celles attendues par uneévolution naturelle en prenant en compte des propriétés structurales des acides aminés. Les méthodesdéveloppées ont été intégrées dans un logiciel dédié au CPD afin de les rendre plus facilement accessibles à lacommunauté scientifique
Computational Protein Design (CPD) is a very young research field which aims at providing predictive tools to complementprotein engineering. Indeed, in addition to the theoretical understanding of fundamental properties and function of proteins,protein engineering has important applications in a broad range of fields, including biomedical applications, biotechnology,nanobiotechnology and the design of green reagents. CPD seeks at accelerating the design of proteins with wanted propertiesby enabling the exploration of larger sequence space while limiting the financial and human costs at experimental level.To succeed this endeavor, CPD requires three ingredients to be appropriately conceived: 1) a realistic modeling of the designsystem; 2) an accurate definition of objective functions for the target biochemical function or physico-chemical property; 3)and finally an efficient optimization framework to handle large combinatorial sizes.In this thesis, we addressed CPD problems with a special focus on combinatorial optimization. In a first series of studies, weapplied for the first time the Cost Function Network optimization framework to solve CPD problems and found that incomparison to other existing methods, it brings several orders of magnitude speedup on a wide range of real CPD instancesthat include the stability design of proteins, protein-protein and protein-ligand complexes. A tailored criterion to define themutation space of residues was also introduced in order to constrain output sequences to those expected by natural evolutionthrough the integration of some structural properties of amino acids in the protein environment. The developed methods werefinally integrated into a CPD-dedicated software in order to facilitate its accessibility to the scientific community
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Moreno, Nascimento Érica Cristina. "Understanding Acetylcholinesterase Inhibitors: Computational Modeling Approaches." Doctoral thesis, Universitat Jaume I, 2017. http://hdl.handle.net/10803/406125.

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La presente Tesis Doctoral constituye un estudio teórico sobre el proceso de inhibición de la acetilcolinesterasa por moléculas que bloquean el sitio activo de la proteína. Se han estudiado un conjunto de 44 inhibidores, dividido en 8 clases distintas, por método QM/MM, docking molecular, análisis estadístico utilizando los métodos de análisis multivariados de datos, reconocimiento de patrones y de algoritmos de agrupamiento. Se ha estudiado la interacción y se ha calculado la energía libre de enlace (Gbind) utilizando los métodos híbridos QM/MM MD asociados a los métodos de la FEP y del BIE. De esta forma, podemos entender la interacción entre los residuos de la enzima, las moléculas de agua estructurales y los diversos inhibidores, a nivel atómico, que son fundamentales para el diseño de nuevos inhibidores y drogas con aplicaciones directas en la enfermedad de Alzheimer.
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Pérez, Llamas Christian 1976. "Computational approaches for integrative cancer genomics." Doctoral thesis, Universitat Pompeu Fabra, 2015. http://hdl.handle.net/10803/328729.

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Given the complexity and heterogeneity of cancer, the development of new high-throughput wide-genome technologies has open new possibilities for its study. Several projects around the globe are exploiting these technologies for generating unprecedented amount of data for cancer genomes. Its analysis, integration and exploration are still a key challenge in the field. In this dissertation, we first present Gitools, a tool for accessing databases in biology, analysing high-throughput data, and visualising multi-dimensional results with interactive heatmaps. Then, we show IntOGen, the methodology employed for collection and organization of the data, the methods used for its analysis, and how the results and analysis were made available to other researchers. Finally, we compare several methods for impact prediction of non-synonymous mutations, showing that new tools specifically designed for cancer outperform those traditionally used for general diseases, and also the need for using other sources of information for better prediction of cancer mutations.
Davant de la complexitat i heterogeneitat del cancer, el desenvolupament de noves tecnologies per l'estudi de genomes, ha obert noves posibilitats. Diversos projectes al voltant del mon les fan servir per generar quantitats de dades de genomes de cancer mai vistes abans. En aquest treball, primer presentem Gitools, una eina que permet obtenir dades de bases de dades en biologia, anal itzar dades genomiques, i visual itzar els resul tats multidimensionals mitjançant mapes de calor interactius. Després mostrem IntOGen, les metodologies per obtenir i organitzar les dades, els metodes per el seu analisi, i com es van possar a disposició d'altres investigadors. Finalment, comparem diversos metods de predicció de l'impacte de les mutacions no sinonimes, que ens mostra com nou metods desenvolupats per cancer funcionen millor que els utilitzats tradicionalment per enfermetats generals, aixis com la necesitat de recorrer a altres fonts d'informació per tenir millor prediccions per mutacions de cancer.
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Cid, Samper Fernando 1991. "Computational approaches to characterize RNP granules." Doctoral thesis, Universitat Pompeu Fabra, 2020. http://hdl.handle.net/10803/668449.

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Ribonucleoprotein granules (RNP granules) are liquid-liquid phase separated complexes composed mainly by proteins and RNA. They are responsible of many processes involved in RNA regulation. Alterations in the dynamics of these proteinRNA complexes are associated with the appearance of several neurodegenerative disorders such as Amyotrophic Lateral Sclerosis ALS or Fragile X Tremor Ataxia Syndrome FXTAS. Yet, many aspects of their organization as well as the specific roles of the RNA on the formation and function of these complexes are still unknown. In order to study RNP granules structure and formation, we integrated several state of the art high-throughput datasets. This includes protein and RNA composition obtained from RNP pull-downs, protein-RNA interaction data from eCLIP experiments and transcriptome-wide secondary structure information (produced by PARS). We used network analysis and clustering algorithms to understand the fundamental properties of granule RNAs. By integrating these properties, we produced a model to identify scaffolding RNA. Scaffolding RNAs are able to recruit many protein components into RNP granules. We found that the main protein components of stress granules (a kind of RNP granules) are connected through protein-RNA interactions. We also analyzed the contribution of RNA-RNA interactions and RNA post-transcriptional modifications on the granule internal organization. We applied these findings to understand the biochemical pathophysiology of FXTAS disease, employing as well some novel experimental data. In FXTAS, a mutation on the FMR1 gene produces a 5´microsatellite repetition that enhances its scaffolding ability. This mutated mRNA is able to sequester some important proteins into nuclear RNP granules, such as TRA2A (i.e. a splicing factor), impeding their normal function and therefore producing some symptoms associated with the progress of the disease. The better understanding of the principles governing granules formation and structure will enable to develop novel therapies (e.g. aptamers) to mitigate the development of several neurodegenerative diseases.
Los gránulos ribonucleoproteicos (gránulos RNP, por sus siglas en inglés) son complejos producidos mediante separación líquido-líquido y están constituidos principalmente por proteínas y ARN. Son responsables de numerosos procesos involucrados con la regulación del ARN. Alteraciones en la dinámica de estos complejos de proteínas y ARN están asociadas con la aparición de diversas enfermedades neurodegenerativas como el ELA o FXTAS. Sin embargo, todavía se desconocen muchos aspectos relativos a su organización interna así como las contribuciones específicas del RNA en la formación y funcionamiento de estos complejos. A fin de estudiar la estructura y formación de los gránulos RNP, hemos integrado varias bases de datos de alto rendimiento de reciente aparición. Esto incluye datos sobre la composición proteica y en ARN de los RNP, sobre la interacción de proteínas y ARN extraída de experimentos de eCLIP y sobre la estructura secundaria del transcriptoma (producida mediante PARS). Todos estos datos han sido procesados para comprender las propiedades fundamentales de los ARNs que integran los gránulos, mediante el empleo de métodos computacionales como el análisis de redes o algoritmos de agrupamiento. De esta manera, hemos producido un modelo que integra varias de estas propiedades e identifica candidatos denominados ARNs de andamiaje. Definimos ARNs de andamiaje como moléculas de ARN con una alta propensión a formar gránulos y reclutar un gran número de componentes proteicos a los gránulos RNP. También hemos encontrado que las interacciones proteína-ARN conectan los principales componentes proteicos de consenso de los gránulos de estrés (un tipo específico de gránulos RNP). También hemos estudiado la contribución de las interacciones ARN-ARN y las modificaciones post-transcriptionales del RNA en la organización interna del gránulo. Hemos aplicado estos resultados para la comprensión de la fisiopatología molecular de FXTAS, empleando también algunos datos experimentales originales. En FXTAS, una mutación en el gen FMR1 produce una repetición de microsatélite en 5´ que incrementa su capacidad como ARN de andamiaje. Este mARN mutado es capaz de secuestrar algunas proteínas importantes como TRA2A (un factor de ayuste alternativo) en gránulos RNP nucleares, impidiendo su normal funcionamiento y por consiguiente produciendo algunos síntomas asociados con el progreso de la enfermedad. Una mejor comprensión de los principios que gobiernan la formación y estructura de los gránulos puede permitir desarrollar nuevas terapias (ej: aptámeros) para mitigar el desarrollo de diversas enfermedades neurodegenerativas.
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Fang, Jianzhong. "Computational approaches to visual object detection." Thesis, University of Nottingham, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.416393.

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Vartapetiance, Anna. "Computational approaches for verbal deception detection." Thesis, University of Surrey, 2015. http://epubs.surrey.ac.uk/807037/.

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Deception exists in all aspects of life and is particularly evident on the Web. Deception includes child sexual predators grooming victims online, medical news headlines with little medical evidence or scientific rigour, individuals claiming others’ work as their own, and systematic deception of company shareholders and institutional investors leading to corporate collapses. This thesis explores the potential for automatic detection of deception. We investigate the nature of deception and the related cues, focusing in particular on Verbal Cues, and concluding that they cannot be readily generalised. We demonstrate how deception-specific features, based on sound hypotheses, can overcome related limitations by presenting approaches for three different examples of deception – namely Child Sexual Predator Detection (SPD), Authorship Identification (AI) and Intrinsic Plagiarism Detection (IPD). We further show how our approaches result in competitive levels of reliability. For SPD we develop our approach largely based on the commonality of requests for key personal information. To address AI, we introduce approaches based on a frequency-mean-variance and a frequency-only framework in order to detect strong associations between co-occurring patterns of a limited number of stopwords. Our IPD approaches are based on simple commonality of words at document level and usage of proper nouns; document sections lacking commonality can be identified as plagiarised. The frameworks of the International Workshop on Uncovering Plagiarism, Authorship, and Social Software Misuse (PAN) competitions provided an independent evaluation of the approaches. The SPD approach obtained an F1 score of 0.48. F1 scores of 0.47, 0.53 and 0.57 were achieved in AI tasks for PAN2012, 2013 and 2014 respectively. IPD yielded an overall accuracy of 91%. Through post-competition adaptations we also show how to improve the approaches and the scores and demonstrate the importance of suitable datasets and how most approaches are not easily transferable between various types of deception.
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Kaimal, Vivek. "Computational approaches to study microRNA networks." University of Cincinnati / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1298041682.

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Books on the topic "Computational approaches"

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1943-, Kueker D. W., and Smith Carl H. 1950-, eds. Learning and geometry: Computational approaches. Boston: Birkhäuser, 1996.

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R, Brent Michael, ed. Computational approaches to language acquisition. Cambridge, Mass: MIT Press, 1997.

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Cozzini, Pietro, and Glen E. Kellogg, eds. Computational Approaches to Nuclear Receptors. Cambridge: Royal Society of Chemistry, 2012. http://dx.doi.org/10.1039/9781849735353.

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Walsh, Aron, Alexey A. Sokol, and C. Richard A. Catlow, eds. Computational Approaches to Energy Materials. Oxford, UK: John Wiley & Sons Ltd, 2013. http://dx.doi.org/10.1002/9781118551462.

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Náray-Szabó, Gábor, and Arieh Warshel, eds. Computational Approaches to Biochemical Reactivity. Dordrecht: Springer Netherlands, 2002. http://dx.doi.org/10.1007/0-306-46934-0.

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Beckmann, Arnold, Ulrich Berger, Benedikt Löwe, and John V. Tucker, eds. Logical Approaches to Computational Barriers. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11780342.

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Wipff, Georges, ed. Computational Approaches in Supramolecular Chemistry. Dordrecht: Springer Netherlands, 1994. http://dx.doi.org/10.1007/978-94-011-1058-7.

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Kueker, David W., and Carl H. Smith, eds. Learning and Geometry: Computational Approaches. Boston, MA: Birkhäuser Boston, 1996. http://dx.doi.org/10.1007/978-1-4612-4088-4.

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Helbich, Marco, Jamal Jokar Arsanjani, and Michael Leitner, eds. Computational Approaches for Urban Environments. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-11469-9.

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Amman, Hans, Berc Rustem, and Andrew Whinston, eds. Computational Approaches to Economic Problems. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4757-2644-2.

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Book chapters on the topic "Computational approaches"

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Endres-Niggemeyer, Brigitte, Kai Haseloh, Jens Müller, Simone Peist, Irene Santini de Sigel, Alexander Sigel, Elisabeth Wansorra, Jan Wheeler, and Brünja Wollny. "Computational Approaches." In Summarizing Information, 297–365. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-72025-3_5.

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Eckert, Michael. "Computational Approaches." In SpringerBriefs in History of Science and Technology, 61–74. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31863-5_6.

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Catlow, C. Richard A., Alexey A. Sokol, and Aron Walsh. "Computational Techniques." In Computational Approaches to Energy Materials, 1–28. Oxford, UK: John Wiley & Sons Ltd, 2013. http://dx.doi.org/10.1002/9781118551462.ch1.

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Zhang, Fan, Xiaogang Wu, and Jake Y. Chen. "Computational Biomarker Discovery." In Approaches in Integrative Bioinformatics, 355–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41281-3_13.

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Cranford, Steven W., and Markus J. Buehler. "Computational Approaches and Simulation." In Biomateriomics, 213–63. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-1611-7_6.

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Wishart, David S. "Computational Approaches to Metabolomics." In Methods in Molecular Biology, 283–313. Totowa, NJ: Humana Press, 2009. http://dx.doi.org/10.1007/978-1-60327-194-3_14.

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Flower, Darren R., Helen McSparron, Martin J. Blythe, Christianna Zygouri, Debra Taylor, Pingping Guan, Shouzhan Wan, et al. "Computational Vaccinology: Quantitative Approaches." In Novartis Foundation Symposia, 102–25. Chichester, UK: John Wiley & Sons, Ltd, 2008. http://dx.doi.org/10.1002/0470090766.ch8.

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Way, Eileen Cornell. "Computational Approaches to Metaphor." In Knowledge Representation and Metaphor, 150–73. Dordrecht: Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-015-7941-4_6.

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David, Traum. "Computational Approaches to Dialogue." In The Routledge Handbook of Language and Dialogue, 143–61. New York, NY : Routledge, [2017] | Series: Routledge Handbooks in Linguistics: Routledge, 2017. http://dx.doi.org/10.4324/9781315750583-10.

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Cohen, Ronald A. "Computational Approaches to Attention." In The Neuropsychology of Attention, 891–930. Boston, MA: Springer US, 2013. http://dx.doi.org/10.1007/978-0-387-72639-7_27.

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Conference papers on the topic "Computational approaches"

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Mullins, Joshua G., and Sankaran Mahadevan. "Computational Effort vs. Accuracy Tradeoff in Uncertainty Quantification." In 16th AIAA Non-Deterministic Approaches Conference. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2014. http://dx.doi.org/10.2514/6.2014-1178.

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Molina-Cristobal, Arturo, Xin Chen, Marin D. Guenov, Atif Riaz, and Albert S. van Heerden. "Interactive Uncertainty Allocation and Trade-off at Early-stage Aircraft Computational Design." In 2018 AIAA Non-Deterministic Approaches Conference. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2018. http://dx.doi.org/10.2514/6.2018-2170.

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Ciccarelli, Francesca. "Computational approaches in cancer genomics." In GECCO '17: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3071178.3106410.

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Williamson, John, Antti Oulasvirta, Otmar Hilliges, and Per Ola Kristensson. "Computational Approaches to Interaction Design." In CHI '17: CHI Conference on Human Factors in Computing Systems. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3027063.3027105.

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Boudin, Marina. "Computational Approaches for Drug Repositioning." In CIKM '20: The 29th ACM International Conference on Information and Knowledge Management. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3340531.3418510.

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Miller, Christopher A. "Computational approaches to interface design." In the 2nd international conference. New York, New York, USA: ACM Press, 1997. http://dx.doi.org/10.1145/238218.238313.

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SERGIOLI, GIUSEPPE, ANTONIO LEDDA, and ROBERTO GIUNTINI. "BINARY GATES IN THREE VALUED QUANTUM COMPUTATIONAL LOGICS." In Quantum Mechanics and Quantum Information: Physical, Philosophical and Logical Approaches. WORLD SCIENTIFIC, 2016. http://dx.doi.org/10.1142/9789813146280_0004.

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CARRILLO, MICHELLE W., RUSSELL A. WILKE, and MARYLYN D. RITCHIE. "COMPUTATIONAL APPROACHES FOR PHARMACOGENOMICS: SESSION INTRODUCTION." In Proceedings of the Pacific Symposium. WORLD SCIENTIFIC, 2005. http://dx.doi.org/10.1142/9789812701626_0050.

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SKOLNICK, JEFFREY, and MICHAL BRYLINSKI. "NOVEL COMPUTATIONAL APPROACHES TO DRUG DISCOVERY." In From Quantum Information to Bio-Informatics. WORLD SCIENTIFIC, 2010. http://dx.doi.org/10.1142/9789814304061_0027.

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WISHART, DAVID S., and RUSSELL GREINER. "COMPUTATIONAL APPROACHES TO METABOLOMICS: AN INTRODUCTION." In Proceedings of the Pacific Symposium. WORLD SCIENTIFIC, 2006. http://dx.doi.org/10.1142/9789812772435_0011.

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Reports on the topic "Computational approaches"

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Tarr, Michael J., and David J. Kriegman. Computational Approaches to Human Shape Representation. Fort Belvoir, VA: Defense Technical Information Center, December 1994. http://dx.doi.org/10.21236/ada298894.

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Wood, C. C., J. S. George, D. M. Schmidt, C. J. Aine, J. Sanders, and J. Belliveau. Human brain mapping: Experimental and computational approaches. Office of Scientific and Technical Information (OSTI), November 1998. http://dx.doi.org/10.2172/674923.

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Yavas, Hakan. Mechanical behavior of nanotwinned materials – experimental and computational approaches. Office of Scientific and Technical Information (OSTI), December 2016. http://dx.doi.org/10.2172/1417976.

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Rouse, William B., and William B. Johnson. Computational Approaches for Analyzing Tradeoffs Between Training and Aiding. Fort Belvoir, VA: Defense Technical Information Center, May 1990. http://dx.doi.org/10.21236/ada221436.

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Gelfand, Michele. Culture and the Contagion of Conflict: Social Science and Computational Approaches. Fort Belvoir, VA: Defense Technical Information Center, August 2015. http://dx.doi.org/10.21236/ada621201.

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Lawrence, Charles E., Lee Newberg, LeeAnn McCue, and Williams Thomspon. Bayesian computational approaches for gene regulation studies of bioethanol and biohydrogen production. Office of Scientific and Technical Information (OSTI), March 2012. http://dx.doi.org/10.2172/1183981.

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Van Dongen, Hans P. Homeostatic and Circadian Modulation of Cognition: Integrating Mathematical and Computational Modeling Approaches. Fort Belvoir, VA: Defense Technical Information Center, August 2012. http://dx.doi.org/10.21236/ada579501.

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STUECKER, JOHN N., JOSEPH CESARANO, III, ERICA LORRANE CORRAL, KIM ANN SHOLLENBERGER, R. ALLEN ROACH, JOHN R. TORCZYNSKI, EDWARD V. THOMAS, and DAVID J. VAN ORNUM. Filling Source Feedthrus with Alumina/Molybdenum CND50 Cermet: Experimental, Theoretical, and Computational Approaches. Office of Scientific and Technical Information (OSTI), July 2001. http://dx.doi.org/10.2172/783092.

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Hardy, Robert Douglas, David Joseph Holcomb, Glen L. Gettemy, Arlo Frederick Fossum, Raul R. Rivas, David R. Bronowski, and Dale S. Preece. Geomechanics of penetration : experimental and computational approaches : final report for LDRD project 38718. Office of Scientific and Technical Information (OSTI), October 2004. http://dx.doi.org/10.2172/920840.

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Wing Hei Cheng, Cecily, Matthew Hai Heng Chung, and Joseph Chi Fung Ng. Structural Dynamics of Amyloid-β Aggregation in Alzheimer’s Disease: Computational and Experimental Approaches. Journal of Young Investigators, December 2016. http://dx.doi.org/10.22186/jyi.31.6.44-50.

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