Relevant bibliographies by topics / Biological data

Academic literature on the topic 'Biological data'

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Published: 4 June 2021
Last updated: 25 May 2024

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Journal articles on the topic "Biological data"

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Hashmi, Faiz. "Elementary approach towards Biological Data Mining." International Journal of Trend in Scientific Research and Development Volume-2, Issue-1 (December 31, 2017): 1109–14. http://dx.doi.org/10.31142/ijtsrd7198.

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Wong, Bang. "Visualizing biological data." Nature Methods 9, no. 12 (December 2012): 1131. http://dx.doi.org/10.1038/nmeth.2258.

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Zaki, Mohammed J., Naren Ramakrishnan, and Srinivasan Parthasarathy. "Biological Data Mining." Scientific Programming 16, no. 1 (2008): 3. http://dx.doi.org/10.1155/2008/897294.

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Li, Peter. "Biological Data Extinction." OMICS: A Journal of Integrative Biology 7, no. 1 (January 2003): 49–50. http://dx.doi.org/10.1089/153623103322006599.

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Sakamoto, Ryoichi, and Shumpei Kojima. "Review of dolphinfish biological and fishing data in Japanese waters." Scientia Marina 63, no. 3-4 (December 30, 1999): 375–85. http://dx.doi.org/10.3989/scimar.1999.63n3-4375.

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NISHIDA, Kozo. "Biological Data and Visualization." Journal of the Visualization Society of Japan 40, no. 156 (2020): 2. http://dx.doi.org/10.3154/jvs.40.156_2.

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Kritchevsky, David. "Commentary: Monitoring biological data." Accountability in Research 1, no. 2 (October 1990): 85–86. http://dx.doi.org/10.1080/08989629008573778.

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Lacroix, Z. "Biological data integration: wrapping data and tools." IEEE Transactions on Information Technology in Biomedicine 6, no. 2 (June 2002): 123–28. http://dx.doi.org/10.1109/titb.2002.1006299.

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Abraham, Michael H., Joelle M. R. Gola, Rachel Kumarsingh, J. Enrique Cometto-Muniz, and William S. Cain. "Connection between chromatographic data and biological data." Journal of Chromatography B: Biomedical Sciences and Applications 745, no. 1 (August 2000): 103–15. http://dx.doi.org/10.1016/s0378-4347(00)00130-4.

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Ziegel, Eric R., and E. Roberts. "Sequential Data in Biological Experiments." Technometrics 36, no. 2 (May 1994): 230. http://dx.doi.org/10.2307/1270256.

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Dissertations / Theses on the topic "Biological data"

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Rundqvist, David. "Grouping Biological Data." Thesis, Linköping University, Department of Computer and Information Science, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-6327.

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Today, scientists in various biomedical fields rely on biological data sources in their research. Large amounts of information concerning, for instance, genes, proteins and diseases are publicly available on the internet, and are used daily for acquiring knowledge. Typically, biological data is spread across multiple sources, which has led to heterogeneity and redundancy.

The current thesis suggests grouping as one way of computationally managing biological data. A conceptual model for this purpose is presented, which takes properties specific for biological data into account. The model defines sub-tasks and key issues where multiple solutions are possible, and describes what approaches for these that have been used in earlier work. Further, an implementation of this model is described, as well as test cases which show that the model is indeed useful.

Since the use of ontologies is relatively new in the management of biological data, the main focus of the thesis is on how semantic similarity of ontological annotations can be used for grouping. The results of the test cases show for example that the implementation of the model, using Gene Ontology, is capable of producing groups of data entries with similar molecular functions.

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Hasegawa, Takanori. "Reconstructing Biological Systems Incorporating Multi-Source Biological Data via Data Assimilation Techniques." 京都大学 (Kyoto University), 2015. http://hdl.handle.net/2433/195985.

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Jakonienė, Vaida. "Integration of biological data /." Linköping : Linköpings universitet, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-7484.

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Jakonienė, Vaida. "Integration of Biological Data." Doctoral thesis, Linköpings universitet, IISLAB - Laboratoriet för intelligenta informationssystem, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-7484.

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Data integration is an important procedure underlying many research tasks in the life sciences, as often multiple data sources have to be accessed to collect the relevant data. The data sources vary in content, data format, and access methods, which often vastly complicates the data retrieval process. As a result, the task of retrieving data requires a great deal of effort and expertise on the part of the user. To alleviate these difficulties, various information integration systems have been proposed in the area. However, a number of issues remain unsolved and new integration solutions are needed. The work presented in this thesis considers data integration at three different levels. 1) Integration of biological data sources deals with integrating multiple data sources from an information integration system point of view. We study properties of biological data sources and existing integration systems. Based on the study, we formulate requirements for systems integrating biological data sources. Then, we define a query language that supports queries commonly used by biologists. Also, we propose a high-level architecture for an information integration system that meets a selected set of requirements and that supports the specified query language. 2) Integration of ontologies deals with finding overlapping information between ontologies. We develop and evaluate algorithms that use life science literature and take the structure of the ontologies into account. 3) Grouping of biological data entries deals with organizing data entries into groups based on the computation of similarity values between the data entries. We propose a method that covers the main steps and components involved in similarity-based grouping procedures. The applicability of the method is illustrated by a number of test cases. Further, we develop an environment that supports comparison and evaluation of different grouping strategies. The work is supported by the implementation of: 1) a prototype for a system integrating biological data sources, called BioTRIFU, 2) algorithms for ontology alignment, and 3) an environment for evaluating strategies for similarity-based grouping of biological data, called KitEGA.
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Dost, Banu. "Optimization algorithms for biological data." Diss., [La Jolla] : University of California, San Diego, 2010. http://wwwlib.umi.com/cr/ucsd/fullcit?p3397170.

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Thesis (Ph. D.)--University of California, San Diego, 2010.
Title from first page of PDF file (viewed March 23, 2010). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (p. 149-159).
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Schmidberger, Markus. "Parallel Computing for Biological Data." Diss., lmu, 2009. http://nbn-resolving.de/urn:nbn:de:bvb:19-104921.

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BERNARDINI, GIULIA. "COMBINATORIAL METHODS FOR BIOLOGICAL DATA." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2021. http://hdl.handle.net/10281/305220.

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Lo scopo di questa tesi è di elaborare e analizzare metodi rigorosi dal punto di vista matematico per l’analisi di due tipi di dati biologici: dati relativi a pan-genomi e filogenesi. Con il termine “pan-genoma” si indica, in generale, un insieme di sequenze genomiche strettamente correlate (tipicamente appartenenti a individui della stessa specie) che si vogliano utilizzare congiuntamente come sequenze di riferimento per un’intera popolazione. Una filogenesi, invece, rappresenta le relazioni evolutive in un gruppo di entità, che siano esseri viventi, geni, lingue naturali, manoscritti antichi o cellule tumorali. Con l’eccezione di uno dei risultati presentati in questa tesi, relativo all’analisi di filogenesi tumorali, il taglio della dissertazione è prevalentemente teorico: lo scopo è studiare gli aspetti combinatori dei problemi affrontati, più che fornire soluzioni efficaci in pratica. Una conoscenza approfondita degli aspetti teorici di un problema, del resto, permette un'analisi matematicamente rigorosa delle soluzioni già esistenti, individuandone i punti deboli e quelli di forza, fornendo preziosi dettagli sul loro funzionamento e aiutando a decidere quali problemi vadano ulteriormente investigati. Oltretutto, è spesso il caso che nuovi risultati teorici (algoritmi, strutture dati o riduzioni ad altri problemi più noti) si possano direttamente applicare o adattare come soluzione ad un problema pratico, o come minimo servano ad ispirare lo sviluppo di nuovi metodi efficaci in pratica. La prima parte della tesi è dedicata a nuovi metodi per eseguire delle operazioni fondamentali su un testo elastico-degenerato, un oggetto computazionale che codifica in maniera compatta un insieme di testi simili tra loro, come, ad esempio, un pan-genoma. Nello specifico, si affrontano il problema di cercare una sequenza di lettere in un testo elastico-degenerato, sia in maniera esatta che tollerando un numero prefissato di errori, e quello di confrontare due testi degenerati. Nella seconda parte si considerano sia filogenesi tumorali, che ricostruiscono per l'appunto l'evoluzione di un tumore, sia filogenesi "classiche", che rappresentano, ad esempio, la storia evolutiva delle specie viventi. In particolare, si presentano nuove tecniche per confrontare due o più filogenesi tumorali, necessarie per valutare i risultati di diversi metodi che ricostruiscono le filogenesi stesse, e una nuova e più efficiente soluzione a un problema di lunga data relativo a filogenesi "classiche", consistente nel determinare se sia possibile sistemare, in presenza di dati mancanti, un insieme di specie in un albero filogenetico che abbia determinate proprietà.
The main goal of this thesis is to develop new algorithmic frameworks to deal with (i) a convenient representation of a set of similar genomes and (ii) phylogenetic data, with particular attention to the increasingly accurate tumor phylogenies. A “pan-genome” is, in general, any collection of genomic sequences to be analyzed jointly or to be used as a reference for a population. A phylogeny, in turn, is meant to describe the evolutionary relationships among a group of items, be they species of living beings, genes, natural languages, ancient manuscripts or cancer cells. With the exception of one of the results included in this thesis, related to the analysis of tumor phylogenies, the focus of the whole work is mainly theoretical, the intent being to lay firm algorithmic foundations for the problems by investigating their combinatorial aspects, rather than to provide practical tools for attacking them. Deep theoretical insights on the problems allow a rigorous analysis of existing methods, identifying their strong and weak points, providing details on how they perform and helping to decide which problems need to be further addressed. In addition, it is often the case where new theoretical results (algorithms, data structures and reductions to other well-studied problems) can either be directly applied or adapted to fit the model of a practical problem, or at least they serve as inspiration for developing new practical tools. The first part of this thesis is devoted to methods for handling an elastic-degenerate text, a computational object that compactly encodes a collection of similar texts, like a pan-genome. Specifically, we attack the problem of matching a sequence in an elastic-degenerate text, both exactly and allowing a certain amount of errors, and the problem of comparing two degenerate texts. In the second part we consider both tumor phylogenies, describing the evolution of a tumor, and “classical” phylogenies, representing, for instance, the evolutionary history of the living beings. In particular, we present new techniques to compare two or more tumor phylogenies, needed to evaluate the results of different inference methods, and we give a new, efficient solution to a longstanding problem on “classical” phylogenies: to decide whether, in the presence of missing data, it is possible to arrange a set of species in a phylogenetic tree that enjoys specific properties.
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Chakraborty, Ushashi. "Finding the Most Predictive Data Source in Biological Data." Thesis, North Dakota State University, 2013. https://hdl.handle.net/10365/26567.

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Classification can be used to predict unknown functions of proteins by using known function information. In some cases, multiple sets of data are available for classification where prediction is only part of the problem, and knowing the most reliable source for prediction is also relevant. Our goal is to develop classification techniques to find the most predictive of the multiple data sets that we have in this project. We use existing classification techniques like linear and quadratic classifications and statistical relevance measures like posterior and log p analysis in our proposed algorithm, which is able to find the data set that is expected to give the best prediction. The proposed algorithm is used on experimental readings during cell cycle of yeast and it predicts the genes that participate in cell-cycle regulation and the type of experiment that provides evidence of cell cycle involvement for any particular gene.
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Gel, Moreno Bernat. "Dissemination and visualisation of biological data." Doctoral thesis, Universitat Politècnica de Catalunya, 2014. http://hdl.handle.net/10803/283143.

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With the recent advent of various waves of technological advances, the amount of biological data being generated has exploded. As a consequence of this data deluge, new challenges have emerged in the field of biological data management. In order to maximize the knowledge extracted from the huge amount of biological data produced it is of great importance for the research community that data dissemination and visualisation challenges are tackled. Opening and sharing our data and working collaboratively will benefit the scientific community as a whole and to move towards that end, new developements, tools and techniques are needed. Nowadays, many small research groups are capable of producing important and interesting datasets. The release of those datasets can greatly increase their scientific value. In addition, the development of new data analysis algorithms greatly benefits from the availability of a big corpus of annotated datasets for training and testing purposes, giving new and better algorithms to biomedical sciences in return. None of these would be feasible without large amounts of biological data made freely and publicly available. Dissemination The Distributed Annotation System (DAS) is a protocol designed to publish and integrate annotations on biological entities in a distributed way. DAS is structured as a client-server system where the client retrieves data from one or more servers and to further process and visualise. Nowadays, setting up a DAS server imposes some requirements not met by many research groups. With the aim of removing the hassle of setting up a DAS server, a new software platform has been developed: easyDAS. easyDAS is a hosted platform to automatically create DAS servers. Using a simple web interface the user can upload a data file, describe its contents and a new DAS server will be automatically created and data will be publicly available to DAS clients. Visualisation One of the most broadly used visualization paradigms for genomic data are genomic browsers. A genomic browser is capable of displaying different sets of features positioned relative to a sequence. It is possible to explore the sequence and the features by moving around and zooming in and out. When this project was started, in 2007, all major genome browsers offered quite an static experience. It was possible to browse and explore data, but is was done through a set of buttons to the genome a certain amount of bases to left or right or zooming in and out. From an architectural point of view, all web-based genome browsers were very similar: they all had a relatively thin clien-side part in charge of showing images and big backend servers taking care of everything else. Every change in the display parameters made by the user triggered a request to the server, impacting the perceived responsiveness. We created a new prototype genome browser called GenExp, an interactive web-based browser with canvas based client side data rendering. It offers fluid direct interaction with the genome representation and it's possible to use the mouse drag it and use the mouse wheel to change the zoom level. GenExp offers also some quite unique features, such as its multi-window capabilities that allow a user to create an arbitrary number of independent or linked genome windows and its ability to save and share browsing sessions. GenExp is a DAS client and all data is retrieved from DAS sources. It is possible to add any available DAS data source including all data in Ensembl, UCSC and even the custom ones created with easyDAS. In addition, we developed a javascript DAS client library, jsDAS. jsDAS is a complete DAS client library that will take care of everything DAS related in a javascript application. jsDAS is javascript library agnostic and can be used to add DAS capabilities to any web application. All software developed in this thesis is freely available under an open source license.
Les recents millores tecnològiques han portat a una explosió en la quantitat de dades biològiques que es generen i a l'aparició de nous reptes en el camp de la gestió de les dades biològiques. Per a maximitzar el coneixement que podem extreure d'aquestes ingents quantitats de dades cal que solucionem el problemes associats al seu anàlisis, i en particular a la seva disseminació i visualització. La compartició d'aquestes dades de manera lliure i gratuïta pot beneficiar en gran mesura a la comunitat científica i a la societat en general, però per a fer-ho calen noves eines i tècniques. Actualment, molts grups són capaços de generar grans conjunts de dades i la seva publicació en pot incrementar molt el valor científic. A més, la disponibilitat de grans conjunts de dades és necessària per al desenvolupament de nous algorismes d'anàlisis. És important, doncs, que les dades biològiques que es generen siguin accessibles de manera senzilla, estandaritzada i lliure. Disseminació El Sistema d'Anotació Distribuïda (DAS) és un protocol dissenyat per a la publicació i integració d'anotacions sobre entitats biològiques de manera distribuïda. DAS segueix una esquema de client-servidor, on el client obté dades d'un o més servidors per a combinar-les, processar-les o visualitzar-les. Avui dia, però, crear un servidor DAS necessita uns coneixements i infraestructures que van més enllà dels recursos de molts grups de recerca. Per això, hem creat easyDAS, una plataforma per a la creació automàtica de servidors DAS. Amb easyDAS un usuari pot crear un servidor DAS a través d'una senzilla interfície web i amb només alguns clics. Visualització Els navegadors genomics són un dels paradigmes de de visualització de dades genòmiques més usats i permet veure conjunts de dades posicionades al llarg d'una seqüència. Movent-se al llarg d'aquesta seqüència és possibles explorar aquestes dades. Quan aquest projecte va començar, l'any 2007, tots els grans navegadors genomics oferien una interactivitat limitada basada en l'ús de botons. Des d'un punt de vista d'arquitectura tots els navegadors basats en web eren molt semblants: un client senzill encarregat d'ensenyar les imatges i un servidor complex encarregat d'obtenir les dades, processar-les i generar les imatges. Així, cada canvi en els paràmetres de visualització requeria una nova petició al servidor, impactant molt negativament en la velocitat de resposta percebuda. Vam crear un prototip de navegador genòmic anomenat GenExp. És un navegador interactiu basat en web que fa servir canvas per a dibuixar en client i que ofereix la possibilitatd e manipulació directa de la respresentació del genoma. GenExp té a més algunes característiques úniques com la possibilitat de crear multiples finestres de visualització o la possibilitat de guardar i compartir sessions de navegació. A més, com que és un client DAS pot integrar les dades de qualsevol servidor DAS com els d'Ensembl, UCSC o fins i tot aquells creats amb easyDAS. A més, hem desenvolupat jsDAS, la primera llibreria de client DAS completa escrita en javascript. jsDAS es pot integrar en qualsevol aplicació DAS per a dotar-la de la possibilitat d'accedir a dades de servidors DAS. Tot el programari desenvolupat en el marc d'aquesta tesis està lliurement disponible i sota una llicència de codi lliure.
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Droop, Alastair Philip. "Correlation Analysis of Multivariate Biological Data." Thesis, University of York, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.507622.

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Books on the topic "Biological data"

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Jake, Chen, and Lonardi Stefano, eds. Biological data mining. Boca Raton, FL: Chapman & Hall/CRC, 2010.

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Jake, Chen, and Lonardi Stefano, eds. Biological data mining. Boca Raton: Chapman & Hall/CRC, 2010.

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BOHS Technology Committee. Working Party on Biological Monitoring., ed. Biological monitoring reference data. Leeds, England: H and H Scientific Consultants, 1992.

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Roberts, E. A. Sequential Data in Biological Experiments. Dordrecht: Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-011-3120-9.

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Maglaveras, Nicos, Ioanna Chouvarda, Vassilis Koutkias, and Rüdiger Brause, eds. Biological and Medical Data Analysis. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11946465.

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Oliveira, José Luís, Víctor Maojo, Fernando Martín-Sánchez, and António Sousa Pereira, eds. Biological and Medical Data Analysis. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11573067.

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Barreiro, José María, Fernando Martín-Sánchez, Víctor Maojo, and Ferran Sanz, eds. Biological and Medical Data Analysis. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/b104033.

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1958-, Doods H. N., and Meel, J. C. A. van 1949-, eds. Receptor data for biological experiments. Chichester: Ellis Horwood, 1991.

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Dolph, Schluter, ed. The analysis of biological data. Greenwood Village, Colo: Roberts and Co. Publishers, 2009.

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C, Fry John, ed. Biological data analysis: A practical approach. Oxford: IRL Press at Oxford University Press, 1993.

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Book chapters on the topic "Biological data"

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Shekhar, Shashi, and Hui Xiong. "Biological Data Mining." In Encyclopedia of GIS, 56. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_101.

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Ashour, Amira S., Nilanjan Dey, and Dac-Nhuong Le. "Biological Data Mining:." In Mining Multimedia Documents, 161–72. Boca Raton : CRC Press, [2017]: Chapman and Hall/CRC, 2017. http://dx.doi.org/10.1201/b21638-12.

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Ashour, Amira S., Nilanjan Dey, and Dac-Nhuong Le. "Biological Data Mining:." In Mining Multimedia Documents, 161–72. Taylor & Francis Group, 6000 Broken Sound Parkway NW, Suite 300, Boca Raton, FL 33487-2742: CRC Press, 2017. http://dx.doi.org/10.1201/9781315399744-13.

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Kim, Ju Han. "Biological Network Analysis." In Genome Data Analysis, 233–46. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-1942-6_13.

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Kopetz, Hermann. "Data in Biological Systems." In Data, Information, and Time, 53–57. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-96329-3_9.

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Hu, Yuh-Jyh. "Biological Sequence Data Mining." In Principles of Data Mining and Knowledge Discovery, 228–40. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44794-6_19.

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Pedersen, Edvard, and Lars Ailo Bongo. "Big Biological Data Management." In Computer Communications and Networks, 265–77. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-44881-7_13.

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Gargaud, Muriel, Ricardo Amils, Carlos Briones, Henderson James Cleaves, and Felipe Gomez. "Chemical and Biological Data." In Encyclopedia of Astrobiology, 2705–12. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-44185-5_5071.

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Gargaud, M., R. Amils, C. Briones, J. Cleaves, and F. Gomez. "Chemical and Biological Data." In Encyclopedia of Astrobiology, 1825–30. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-11274-4_5071.

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Gargaud, Muriel, Ricardo Amils, Carlos Briones, Henderson James Cleaves II, and Felipe Gomez. "Chemical and Biological Data." In Encyclopedia of Astrobiology, 1–7. Berlin, Heidelberg: Springer Berlin Heidelberg, 2022. http://dx.doi.org/10.1007/978-3-642-27833-4_5071-4.

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Conference papers on the topic "Biological data"

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Chen, Huaming, Jun Shen, Lei Wang, and Chi-Hung Chi. "Towards Biological Sequence Data Service with Insights." In 2018 IEEE International Conference on Big Data (Big Data). IEEE, 2018. http://dx.doi.org/10.1109/bigdata.2018.8622194.

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Baralis, Elena, and Alessandro Fiori. "Exploring Heterogeneous Biological Data Sources." In 2008 19th International Conference on Database and Expert Systems Applications (DEXA). IEEE, 2008. http://dx.doi.org/10.1109/dexa.2008.116.

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Eltabakh, Mohamed Y., Mourad Ouzzani, Walid G. Aref, Ahmed K. Elmagarmid, Yasin Laura-Silva, Muhammad U. Arshad, David Salt, and Ivan Baxter. "Managing Biological Data using bdbms." In 2008 IEEE 24th International Conference on Data Engineering (ICDE 2008). IEEE, 2008. http://dx.doi.org/10.1109/icde.2008.4497631.

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Hammer, Barbara, Alexander Hasenfuss, Frank-Michael Schleif, Thomas Villmann, Marc Strickert, and Udo Seiffert. "Intuitive Clustering of Biological Data." In 2007 International Joint Conference on Neural Networks. IEEE, 2007. http://dx.doi.org/10.1109/ijcnn.2007.4371244.

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O'Donoghue, Seán I. "Visualising biological data: Current perspectives." In 2013 INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL MODELS FOR LIFE SCIENCES. AIP, 2013. http://dx.doi.org/10.1063/1.4825252.

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Fulton, Tom K. "Biological noise on seismic data." In SEG Technical Program Expanded Abstracts 1993. Society of Exploration Geophysicists, 1993. http://dx.doi.org/10.1190/1.1822539.

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Fulton, T. K. "Biological Noise On Seismic Data." In Offshore Technology Conference. Offshore Technology Conference, 1993. http://dx.doi.org/10.4043/7132-ms.

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"DATA INTEGRATION IN BIOLOGICAL MODELS." In 10th International Conference on Enterprise Information Systems. SciTePress - Science and and Technology Publications, 2008. http://dx.doi.org/10.5220/0001667603890392.

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Alzuru, Icaro, Aditi Malladi, Andrea Matsunaga, Mauricio Tsugawa, and Fortes Jose A.B. "Human-Machine Information Extraction Simulator for Biological Collections." In 2019 IEEE International Conference on Big Data (Big Data). IEEE, 2019. http://dx.doi.org/10.1109/bigdata47090.2019.9005601.

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Tata, S., J. S. Friedman, and A. Swaroop. "Declarative Querying for Biological Sequences." In 22nd International Conference on Data Engineering (ICDE'06). IEEE, 2006. http://dx.doi.org/10.1109/icde.2006.47.

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Reports on the topic "Biological data"

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Simeonova, Valeriya. How to Analyze Compromised Data from Biological Experiments? "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, April 2019. http://dx.doi.org/10.7546/crabs.2019.04.09.

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Hofmann, Eileen E. Multi-Dimensional Data Assimilation for Physical-Biological Models. Fort Belvoir, VA: Defense Technical Information Center, September 1999. http://dx.doi.org/10.21236/ada630557.

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Hofmann, Eileen E. Multi-Dimensional Data Assimilation for Physical-Biological Models. Fort Belvoir, VA: Defense Technical Information Center, July 2000. http://dx.doi.org/10.21236/ada380222.

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Chaudhary, Aashish. OPEN SOURCE SCALABLE DATA SERVICES AND DATA FUSION FOR BIOLOGICAL AND ENVIRONMENTAL SCIENCES. Office of Scientific and Technical Information (OSTI), March 2020. http://dx.doi.org/10.2172/1602442.

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Gelmont, Boris, Alexei Bykhovski, and Tatiana Globus. New Concepts for Detection Biological Targets: Terahertz Signature Data Base Generation. Fort Belvoir, VA: Defense Technical Information Center, August 2008. http://dx.doi.org/10.21236/ada499606.

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Langston, Michael A. Scalable Computational Methods for the Analysis of High-Throughput Biological Data. Office of Scientific and Technical Information (OSTI), September 2012. http://dx.doi.org/10.2172/1050046.

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Pearman, T. R. R., A. Bates, K. Robert, A. Callaway, C. Lo Iacono, R. Hall, and V. A I Huvenne. The influence of the scale of enquiry and estimated biological parameters on the biological signal obtained from underwater video data. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2017. http://dx.doi.org/10.4095/305910.

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Lapota, David, and Stephen H. Lieberman. Biological Environmental Arctic Project (BEAP) Preliminary Data (Arctic West Summer 1986 Cruise). Fort Belvoir, VA: Defense Technical Information Center, November 1986. http://dx.doi.org/10.21236/ada179818.

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Chris Sander, PhD. Data Exchange Format for Biological Pathway Databases (BioPAX) Workshop - Final Technical Report. Office of Scientific and Technical Information (OSTI), July 2004. http://dx.doi.org/10.2172/826395.

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Bravo, Adriana, James Gibbs, Jennifer Griffiths, Ian J. Harrison, and Ana Porzecanski. What Is Biodiversity? Analyzing Data to Compare and Conserve Spider Communities. American Museum of Natural History, 2012. http://dx.doi.org/10.5531/cbc.ncep.0013.

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Abstract:
In this exercise, students will classify and analyze data on spider communities to explore the concept of biological diversity and experience its application to decision-making in biological conservation. This exercise was adapted to further develop data analysis skills. Specifically, this exercise asks students to 1) create an appropriate and informative graph, 2) interpret trends and patterns in the graph, 3) understand and correctly solve equations, and 4) make well-reasoned conclusions from data.
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