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Journal articles on the topic 'Database searching'

Author: Grafiati
Published: 4 June 2021
Last updated: 31 July 2024

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1

BRENNER, S. "Practical database searching." Trends in Biotechnology 16 (November 1998): 9–12. http://dx.doi.org/10.1016/s0167-7799(98)00128-0.

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2

Tokizane, Soichi. "Education of Database Searching." Joho Chishiki Gakkaishi 18, no. 5 (2008): 436–38. http://dx.doi.org/10.2964/jsik.18-5-12.

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3

WOOD, NEIL, ROBIN PHILIPP, and JOHN COLLEY. "Online Medical Database Searching." Occupational Medicine 38, no. 3 (1988): 73–76. http://dx.doi.org/10.1093/occmed/38.3.73.

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4

LEWITTER, F. "Text-based database searching." Trends in Biotechnology 16 (November 1998): 3–5. http://dx.doi.org/10.1016/s0167-7799(98)00126-7.

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5

ALTSCHUL, S. "Fundamentals of database searching." Trends in Biotechnology 16 (November 1998): 7–9. http://dx.doi.org/10.1016/s0167-7799(98)00127-9.

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6

Khan, Akhtar J. "Limitations of Database Searching." Ophthalmology 97, no. 8 (August 1990): 961. http://dx.doi.org/10.1016/s0161-6420(90)32462-4.

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7

Badia, Giovanna. "Identifying “best bets” for searching in chemical engineering." Journal of Documentation 74, no. 1 (January 8, 2018): 80–98. http://dx.doi.org/10.1108/jd-09-2016-0112.

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Purpose Performing efficient literature searches and subscribing to the most comprehensive databases for interdisciplinary fields can be challenging since the literature is typically indexed in numerous databases to different extents. Comparing databases will help information professionals make appropriate choices when teaching, literature searching, creating online subject guides, and deciding which databases to renew when faced with fiscal challenges. The purpose of this paper is to compare databases for searching the chemical engineering literature. Design/methodology/approach This paper compares journal indexing and search recall across seven databases that cover the chemical engineering literature in order to determine which database and database pair provide the most comprehensive coverage in this area. It also summarizes published, database comparison methods to aid information professionals in undertaking their own comparative assessments. Findings SciFinder, Scopus, and Web of Science, listed alphabetically, were the leading databases for searching the chemical engineering literature. SciFinder-Scopus and SciFinder-Web of Science were the top two database pairs. No single database or pair provided 100 percent complete coverage of the literature examined. Searching a second database increased the recall of results by an average of 17.6 percent. Practical implications The findings are useful since they identify “best bets” for performing an efficient search of the chemical engineering literature. Information professionals can also use the methods discussed to compare databases for any discipline or search topic. Originality/value This paper builds on the previous literature by using a dual approach to compare the coverage of the chemical engineering literature across multiple databases. To the author’s knowledge, comparing databases in the field of chemical engineering has not been reported in the literature thus far.
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8

Armstrong, Annie R. "Student perceptions of federated searching vs single database searching." Reference Services Review 37, no. 3 (August 14, 2009): 291–303. http://dx.doi.org/10.1108/00907320910982785.

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9

Tao, Peng, and Zhou Jia-Ju. "Pharmacophore-based 3D Database Searching." Acta Physico-Chimica Sinica 19, no. 09 (2003): 886–88. http://dx.doi.org/10.3866/pku.whxb20030923.

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10

Pisciotta, Henry. "DATABASE SEARCHING WITH GATEWAY SOFTWARE." Art Documentation: Journal of the Art Libraries Society of North America 4, no. 1 (April 1985): 3–5. http://dx.doi.org/10.1086/adx.4.1.27947378.

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11

Chen, Jingling, Dagomir Kaszlikowski, L. C. Kwek, and C. H. Oh. "Searching a database under decoherence." Physics Letters A 306, no. 5-6 (January 2003): 296–305. http://dx.doi.org/10.1016/s0375-9601(02)01637-7.

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12

Palvia, Prashant. "Batched searching in database organizations." Information Sciences 45, no. 1 (June 1988): 23–37. http://dx.doi.org/10.1016/0020-0255(88)90006-0.

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13

van Brakel, Pieter A. "Teaching online searching in a LAN environment." Electronic Library 11, no. 4/5 (April 1, 1993): 289–93. http://dx.doi.org/10.1108/eb045248.

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Tertiary programmes for teaching online searching consist typically of the components of an online search system, different categories of databases, overview of database hosts and their search facilities, methods to create search strategies and command languages, to name but a few. Practical experience, an integral component, is gained by searching interactively on one or more database hosts, where the emphasis is on search techniques rather than the intrinsic characteristics of the databases of the specific system. The extent of students' hands‐on experience invariably depends on the teaching unit's budget, which may preclude extensive ‘live’ exposure. However, the technical facilities and shared resources of a local area network (LAN) are likely to have a significant effect on the traditional teaching methods of online searching. It is now possible, in a LAN environment, to integrate the various information retrieval activities, for example creating and searching personal or local databases, utilising these for indexing, abstracting and thesaurus building, searching locally on CDROM databases which simulate the search facilities and command languages of commercial database hosts and, when the need arises, accessing their external ‘online’ counterparts. This article will demonstrate how the limited concept of ‘online searching’ is broadened when a LAN and local databases are utilised in the online teaching process.
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14

Martin, Yvonne C. "3D database searching in drug design." Journal of Medicinal Chemistry 35, no. 12 (June 1992): 2145–54. http://dx.doi.org/10.1021/jm00090a001.

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15

Dumais, Susan T., and Deborah G. Schmitt. "Iterative Searching in an Online Database." Proceedings of the Human Factors Society Annual Meeting 35, no. 5 (September 1991): 398–402. http://dx.doi.org/10.1177/154193129103500534.

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16

Mercado, Heidi. "Library instruction and online database searching." Reference Services Review 27, no. 3 (September 1999): 259–65. http://dx.doi.org/10.1108/00907329910283395.

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17

White, Adrian, Karl-Ludwig Resch, and Edzard Ernst. "Searching for Acupuncture Trials: Which Database?" Acupuncture in Medicine 13, no. 2 (November 1995): 97–99. http://dx.doi.org/10.1136/aim.13.2.97.

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Evidence based medicine depends on sensitive, precise and informative searches for references to controlled clinical trials. Five databases: Medline, Centralised Information Service for Complementary Medicine (CISCOM), Science Citation Index (SCI), British Library and Embase, were compared for their effectiveness as literature search tools by conducting test searches for acupuncture trials in a single sample year, 1992. CISCOM showed the greatest sensitivity, followed by Medline and SCI; all three usually provided abstracts, but CISCOM did not provide authors’ addresses. Other databases had features that might make them useful for particular purposes. SCI, British Library and Embase covered a different range of journals from Medline, including acupuncture specialist journals, but of these three only SCI contained an additional relevant reference; it also had the advantages of secondary references and associated correspondence. Indexing of Medline appeared to be more effective for acupuncture than for some other complementary and orthodox medical subjects. It was concluded that the CISCOM search was the most sensitive, and that Medline and SCI had other benefits which were significant.
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18

Kendrick, Aubrey W. "Computer database searching and business librarians." Online Review 12, no. 1 (January 1988): 39–46. http://dx.doi.org/10.1108/eb024264.

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19

Studwell, Bobbie. "Seeking Uniformity in Cross-Database Searching." Legal Reference Services Quarterly 9, no. 3-4 (January 3, 1991): 241–55. http://dx.doi.org/10.1300/j113v09n03_11.

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20

Vogt, Jürgen, and Natalja Vogt. "Structure searching in the MOGADOC database." Journal of Molecular Structure 695-696 (June 2004): 237–41. http://dx.doi.org/10.1016/j.molstruc.2003.11.052.

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21

McDonald, S., L. Taylor, and C. E. Adams. "Are you searching the right database?" Schizophrenia Research 41, no. 1 (January 2000): 175. http://dx.doi.org/10.1016/s0920-9964(00)90723-0.

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22

Beavis, Ronald C., and David Fenyö. "Database searching with mass-spectrometric information." Trends in Biotechnology 18 (July 2000): 22–27. http://dx.doi.org/10.1016/s0167-7799(00)00009-3.

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23

Ceresnak, Roman, Michal Kvet, and Karol Matiasko. "Searching Algorithm in a nonrelational database." Journal of Digital Science 4, no. 2 (December 28, 2022): 20–29. http://dx.doi.org/10.33847/2686-8296.4.2_2.

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The problem of the data growth and it is storing to the nonrelational data-bases is related to their decreasing efficiency of searching. Nowadays, a very popular database in memory will help us with decreasing the efficiency of the operation searching in this paper. This paper examines the data search-ing in applications hosted in cloud service Amazon with using of nonrela-tional database DynamoDB. It develops new procedures to provide faster response to user and to obtain the data using of nonrelational database Dy-namoDB, that will provide the demanded data and subsequently, it will transfer them to the memory. The given procedure is based on two methods. The first method is a recognition of values, to which the user refers and the provision of this data to the database in memory. The second method is re-lated to the automatic storing of the data transferred to the database in memory. We perform various experiments in the paper, which are showing us a border of efficiency respectively inefficiency from a time perspective.
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24

Yates, John R. "Database searching using mass spectrometry data." Electrophoresis 19, no. 6 (May 1998): 893–900. http://dx.doi.org/10.1002/elps.1150190604.

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25

Thompson, Annie Figueroa. "Bibliographie instruction and computer database searching." Information Processing & Management 26, no. 4 (January 1990): 562. http://dx.doi.org/10.1016/0306-4573(90)90079-h.

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26

Gitelman, Lisa. "Searching and Thinking About Searching JSTOR." Representations 127, no. 1 (2014): 73–82. http://dx.doi.org/10.1525/rep.2014.127.1.73.

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Digital resources are helping to change the ways scholars and students work, but they must also be helping to shape the work that gets done. Taking JSTOR as an example, we might ask about the discursive power of the database. How is using an online resource for research acceding to unnoticed assumptions that underlie the construction of that resource?
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27

Laulederkind, S. J. F., M. Tutaj, M. Shimoyama, G. T. Hayman, T. F. Lowry, R. Nigam, V. Petri, et al. "Ontology searching and browsing at the Rat Genome Database." Database 2012 (March 20, 2012): bas016. http://dx.doi.org/10.1093/database/bas016.

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28

Abhijnhan, Akhil, Zhenya Surcheva, Judy Wright, and Clive E. Adams. "Searching a biomedical bibliographic database from Bulgaria: the ABS database." Health Information and Libraries Journal 24, no. 3 (September 2007): 200–203. http://dx.doi.org/10.1111/j.1471-1842.2007.00723.x.

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29

Dolama, Mustafa Vahabzadeh, and Akbar Ghaffarpour Rahbar. "Improvement of the Performance of Database Access Operations in Cellular Networks." Journal of Telecommunications and Information Technology, no. 3 (September 30, 2012): 73–82. http://dx.doi.org/10.26636/jtit.2012.3.1281.

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Reducing the traffic volume of location updating is a critical issue for tracking mobile users in a cellular network. Besides, when user x wants to communicate with user y, the location of user y must be extracted from databases. Therefore, one or more databases must be accessed for updating, recording, deleting, and searching. Thus, the most important criterion of a location tracking algorithm is to provide a small database access time. In this paper, we propose a new location tracking scheme, called Virtual Overlap Region with Forwarding Pointer (VF), and compare the number of database accesses required for updating, deleting, and searching operations for the proposed scheme and other approaches proposed for cellular networks. Our VF scheme like Overlap Region scheme reduces the updating information when a user frequently moves in boundaries of LAs. Unlike Overlap Region, the VF can reduce number of database accesses for searching users’ information.
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30

Milev, P. "Application of real-time dictionary instead of full-text index for searching in web publications." Trakia Journal of Sciences 17, Suppl.1 (2019): 160–64. http://dx.doi.org/10.15547/tjs.2019.s.01.026.

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The article examines topics of searching in web publications. In this sense, the paper presents features of database models, that are suitable for storing such information. In these cases, full-text indexes are mostly used for the implementation of algorithms for searching in the content of web publications. The main purpose of the article is to explore the possibilities of realization of such a database and to present a conceptual data model for searching in web publications using a real-time dictionary. To achieve its goal, the article uses various scientific methods, including study, analysis, research, modeling and experimentation. The results of this paper support the main hypothesis of the study, namely defining the advantages of using a real-time dictionary for searching in web publications. The conclusion highlights the possibilities for improvement of searching in databases, that store web publications.
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31

Mcdonald, Steve, Libby Taylor, and Clive Adams. "Searching the right database. A comparison of four databases for psychiatry journals." Health Libraries Review 16, no. 3 (September 1999): 151–56. http://dx.doi.org/10.1046/j.1365-2532.1999.00222.x.

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32

Bolesta, Wojciech. "Analysis of query execution speed in the selected NoSQL databases." Journal of Computer Sciences Institute 7 (September 30, 2018): 138–41. http://dx.doi.org/10.35784/jcsi.662.

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The scientific article deals with a comparison of the query speed of two NoSQL databases. Described databases will be MongoDB and CouchDB. The work presents speed comparisons of such queries as adding data to the database, editing database data, deleting data from the database, and searching data in the database. Also a general comparison of bases will be presented, with the answer to the question of which of the tested NoSQL databases is faster.
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33

Zaniewicz, Norbert, and Andrzej Salamończyk. "Comparison of MongoDB, Neo4j and ArangoDB databases using the developed data generator for NoSQL databases." Studia Informatica. System and information technology 26, no. 1 (November 7, 2022): 61–72. http://dx.doi.org/10.34739/si.2022.26.04.

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NoSQL databases are gaining more and more popularity and are an important alternative to relational databases. Examples of such databases are Neo4j, MongoDB and ArangoDB. These databases are described in this article, compared with each other, and the performance results for adding an object to the database, deleting an object, searching and populating the database are presented. Results show that the fastest database is MongoDB, except for one measurement of removal.
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34

Lutes, Barbara. "Using online databases for terminology searching." Terminology 2, no. 2 (January 1, 1995): 187–217. http://dx.doi.org/10.1075/term.2.2.02lut.

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Online bibliographic databases would seem to be a promising source of multilingual terminology for translators, terminologists, and other language professionals, although not designed for this purpose. This paper will explore this hypothesis, discussing the types and degree of multilingualism in representative databases, the different information-seeking needs of language professionals, and describing search strategies which can be used for term look-up. Results of extensive trial searching and some in-depth examples will be used to assess the practical feasibility of this use of databases, with special attention paid to reliability. Results of trial searching were generally positive, although cost might prove prohibitive. Using bibliographic databases may be particularly useful in solving difficult terminological problems for which context information is essential. Annex 1 provides information on a selection of useful multilingual online databases; annex 2 lists addresses of selected online database providers.
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35

Kertesz-Farkas, Attila, Beata Reiz, Michael P. Myers, and Sandor Pongor. "Database Searching in Mass Spectrometry Based Proteomics." Current Bioinformatics 7, no. 2 (May 1, 2012): 221–30. http://dx.doi.org/10.2174/157489312800604354.

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36

Ye, Yuzhen, and Adam Godzik. "Database searching by flexible protein structure alignment." Protein Science 13, no. 7 (July 2004): 1841–50. http://dx.doi.org/10.1110/ps.03602304.

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37

Ge, Jianye, Ranajit Chakraborty, Arthur Eisenberg, and Bruce Budowle. "Comparisons of Familial DNA Database Searching Strategies." Journal of Forensic Sciences 56, no. 6 (November 2011): 1448–56. http://dx.doi.org/10.1111/j.1556-4029.2011.01867.x.

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38

Spink, Amanda, and Maurice Leatherbury. "NAME AUTHORITY FILES AND HUMANITIES DATABASE SEARCHING." Online and CD-Rom Review 18, no. 3 (March 1994): 143–48. http://dx.doi.org/10.1108/eb024487.

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39

LaBorie, Tim, and Leslie Donnelly. "Vending Database Searching with Public Access Terminals." Library Hi Tech 4, no. 2 (February 1986): 7–10. http://dx.doi.org/10.1108/eb047641.

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40

Chaffin, Nancy, Kevin Cullen, and George R. Jaramillo. "Cross-Database Searching: The Implementation of MetaLib." Technical Services Quarterly 22, no. 4 (July 7, 2005): 39–53. http://dx.doi.org/10.1300/j124v22n04_03.

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41

Griffith, Renate, Rachada Chanphen, Scott P. Leach, and Paul A. Keller. "New anti-Malarial compounds from database searching." Bioorganic & Medicinal Chemistry Letters 12, no. 4 (February 2002): 539–42. http://dx.doi.org/10.1016/s0960-894x(01)00811-3.

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42

Stokes, Peter, and BA Hons. "Electronic database searching skills for nurse prescribers." Nurse Prescriber 1, no. 10 (October 2004): 1–6. http://dx.doi.org/10.1017/s1467115804001889.

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SummaryThe increased emphasis on evidence-based practice within healthcare has compelled nurses to obtain up to date research information to assist them in clinical decision-making. Electronic databases are a crucial resource for finding this information, but a successful search strategy is vital if relevant information is not to be missed. This article highlights the key steps in the searching process with examples from three databases on the OVID and Dialog Datastar platforms.
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43

Updegrove, Natalie A. "Database searching: Information retrieval for nutrition professionals." Journal of Nutrition Education 22, no. 5 (October 1990): 241–47. http://dx.doi.org/10.1016/s0022-3182(12)80731-x.

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44

Christie, Bradley D., Douglas R. Henry, W. Todd Wipke, and Thomas E. Moock. "Database structure and searching in MACCS-3D." Tetrahedron Computer Methodology 3, no. 6 (January 1990): 653–64. http://dx.doi.org/10.1016/0898-5529(90)90164-4.

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45

Banholzer, P., and M. E. Grabenstein. "WAIS searching of the current contents database." Vistas in Astronomy 39 (January 1995): 266. http://dx.doi.org/10.1016/0083-6656(95)91013-7.

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46

van de Streek, Jacco, and Sam Motherwell. "Searching the Cambridge Structural Database for polymorphs." Acta Crystallographica Section B Structural Science 61, no. 5 (September 23, 2005): 504–10. http://dx.doi.org/10.1107/s0108768105020021.

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In order to identify all pairs of polymorphs in the Cambridge Structural Database (CSD), a method was devised to automatically compare two crystal structures. The comparison is based on simulated powder diffraction patterns, but with special provisions to deal with differences in unit-cell volumes caused by temperature or pressure. Among the 325 000 crystal structures in the Cambridge Structural Database, 35 000 pairs of crystal structures of the same chemical compound were identified and compared. A total of 7300 pairs of polymorphs were identified, of which 154 previously were unknown.
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47

Liu, I.-Hsien, Chuan-Gang Liu, Cheng-Jui Chang, and Jung-Shian Li. "Client Searching Privacy Protection in Encrypted Database." Proceedings of International Conference on Artificial Life and Robotics 23 (February 2, 2018): 457–60. http://dx.doi.org/10.5954/icarob.2018.os8-11.

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48

Vergoulis, Thanasis, Ilias Kanellos, Nikos Kostoulas, Georgios Georgakilas, Timos Sellis, Artemis Hatzigeorgiou, and Theodore Dalamagas. "mirPub: a database for searching microRNA publications." Bioinformatics 31, no. 9 (December 20, 2014): 1502–4. http://dx.doi.org/10.1093/bioinformatics/btu819.

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49

Keane, T. M., and T. J. Naughton. "DSEARCH: sensitive database searching using distributed computing." Bioinformatics 21, no. 8 (November 25, 2004): 1705–6. http://dx.doi.org/10.1093/bioinformatics/bti163.

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50

Bates, Susan. "Chemical Structure searching in Derwent online database." World Patent Information 19 (January 1997): 33–41. http://dx.doi.org/10.1016/s0172-2190(97)82768-8.

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