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Emerging approaches in literature-based discovery: techniques and performance review

  • Yakub Sebastian (a1), Eu-Gene Siew (a2) and Sylvester O. Orimaye (a3)

Literature-based discovery systems aim at discovering valuable latent connections between previously disparate research areas. This is achieved by analyzing the contents of their respective literatures with the help of various intelligent computational techniques. In this paper, we review the progress of literature-based discovery research, focusing on understanding their technical features and evaluating their performance. The present literature-based discovery techniques can be divided into two general approaches: the traditional approach and the emerging approach. The traditional approach, which dominate the current research landscape, comprises mainly of techniques that rely on utilizing lexical statistics, knowledge-based and visualization methods in order to address literature-based discovery problems. On the other hand, we have also observed the births of new trends and unprecedented paradigm shifts among the recently emerging literature-based discovery approach. These trends are likely to shape the future trajectory of the next generation literature-based discovery systems.

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Andronis C., Sharma A., Deftereos S., Virvilis V., Konstanti O., Persidis A. & Persidis A. 2012. Mining scientific and clinical databases to identify novel uses for existing drugs. In Drug Repositioning: Bringing New Life to Shelved Assets and Existing Drugs, Michael J. Barrat & Donald E. Frail (eds). Wiley, 137.
Bassecoulard E. & Zitt M. 2004. Patents and publications. In Handbook of Quantitative Science and Technology Research, Henk F. Moed, Wolfgang Glänzel, & Ulrich Schmoch (eds). Springer, 665694.
Bekhuis T. 2006. Conceptual biology, hypothesis discovery, and text mining: Swanson’s legacy. Biomedical Digital Libraries 3(1), 1.
Berry M. W. & Castellanos M. 2004. Survey of text mining. Computing Reviews 45(9), 548.
Blei D. M., Ng A. Y. & Jordan M. I. 2003. Latent dirichlet allocation. Journal of Machine Learning Research 3, 9931022.
Bornmann L. & Mutz R. 2015. Growth rates of modern science: a bibliometric analysis based on the number of publications and cited references. Journal of the Association for Information Science and Technology 66(11), 22152222.
Boyack K. W. & Klavans R. 2010. Co-citation analysis, bibliographic coupling, and direct citation: which citation approach represents the research front most accurately? Journal of the American Society for Information Science and Technology 61(12), 23892404.
Boyack K. W., Small H. & Klavans R. 2013. Improving the accuracy of co-citation clustering using full text. Journal of the American Society for Information Science and Technology 64(9), 17591767.
Brin S. & Page L. 2012. Reprint of: the anatomy of a large-scale hypertextual web search engine. Computer Networks 56(18), 38253833.
Callon M., Courtial J.-P., Turner W. A. & Bauin S. 1983. From translations to problematic networks: an introduction to co-word analysis. Social Science Information 22(2), 191235.
Cameron D., Bodenreider O., Yalamanchili H., Danh T., Vallabhaneni S., Thirunarayan K., Sheth A. P. & Rindflesch T. C. 2013. A graph-based recovery and decomposition of Swanson’s hypothesis using semantic predications. Journal of Biomedical Informatics 46(2), 238251.
Cameron D. H. 2014. A Context-Driven Subgraph Model for Literature-Based Discovery. PhD thesis, Wright State University.
Cameron D., Kavuluru R., Rindflesch T. C., Sheth A. P., Thirunarayan K. & Bodenreider O. 2015. Context-driven automatic subgraph creation for literature-based discovery. Journal of Biomedical Informatics 54, 141157.
Chang J. & Blei D. M. 2010. Hierarchical relational models for document networks. The Annals of Applied Statistics 4(1), 124150.
Chen C. 2012. Predictive effects of structural variation on citation counts. Journal of the American Society for Information Science and Technology 63(3), 431449.
Chen C., Chen Y., Horowitz M., Hou H., Liu Z. & Pellegrino D. 2009. Towards an explanatory and computational theory of scientific discovery. Journal of Informetrics 3(3), 191209.
Chen H.-H., Gou L., Zhang X. L. & Giles C. L. 2013. Towards the discovery of diseases related by genes using vertex similarity measures. In 2013 IEEE International Conference on Healthcare Informatics (ICHI), 505–510. IEEE.
Cohen A. M. & Hersh W. R. 2005. A survey of current work in biomedical text mining. Briefings in Bioinformatics 6(1), 5771.
Cohen P. R. 2015. Darpa’s big mechanism program. Physical Biology 12(4), 045008.
Cohen T., Schvaneveldt R. & Widdows D. 2010. Reflective random indexing and indirect inference: a scalable method for discovery of implicit connections. Journal of Biomedical Informatics 43(2), 240256.
Cohen T., Widdows D. & Rindflesch T. 2015. Expansion-by-analogy: a vector symbolic approach to semantic search. In Quantum Interaction: 8th International Conference, QI 2014, Filzbach, Switzerland, June 30–July 3, Atmanspacher, H., Bergomi, C., Filk, T. & Kitto, K. (eds). Springer International Publishing, 54–66.
Cohen T., Widdows D., Schvaneveldt R. W., Davies P. & Rindflesch T. C. 2012. Discovering discovery patterns with predication-based semantic indexing. Journal of Biomedical Informatics 45(6), 10491065.
Cohen T., Widdows D., Stephan C., Zinner R., Kim J., Rindflesch T. & Davies P. 2014. Predicting high-throughput screening results with scalable literature-based discovery methods. CPT: Pharmacometrics & Systems Pharmacology 3(10), 19.
Cory K. A. 1997. Discovering hidden analogies in an online humanities database. Computers and the Humanities 31(1), 112.
Davies R. 1989. The creation of new knowledge by information retrieval and classification. Journal of Documentation 45(4), 273301.
Deerwester S., Dumais S. T., Furnas G. W., Landauer T. K. & Harshman R. 1990. Indexing by latent semantic analysis. Journal of the American Society for Information Science 41(6), 391.
DiGiacomo R. A., Kremer J. M. & Shah D. M. 1989. Fish-oil dietary supplementation in patients with Raynaud’s phenomenon: a double-blind, controlled, prospective study. The American Journal of Medicine 86(2), 158164.
Ding Y., Song M., Han J., Yu Q., Yan E., Lin L. & Chambers T. 2013. Entitymetrics: measuring the impact of entities. PloS One 8(8), e71416.
Eronen L. & Toivonen H. 2012. Biomine: predicting links between biological entities using network models of heterogeneous databases. BMC Bioinformatics 13(1), 1.
Feller I. & Stern P. C. 2007. A Strategy for Assessing Science: Behavioral and Social Research on Aging. National Academies Press.
Freeman L. C. 1978. Centrality in social networks conceptual clarification. Social Networks 1(3), 215239.
Frijters R., Van Vugt M., Smeets R., Van Schaik R., De Vlieg J. & Alkema W. 2010. Literature mining for the discovery of hidden connections between drugs, genes and diseases. PLoS Computational Biology 6(9), e1000943.
Fujita K. 2012. Finding linkage between sustainability science and technologies based on citation network analysis. In 2012 Fifth IEEE International Conference on Service-Oriented Computing and Applications (SOCA), 1–6. IEEE.
Ganiz M., Pottenger W. M. & Janneck C. D. 2005. Recent Advances in Literature Based Discovery. Technical report, Lehigh University.
Getoor L. & Diehl C. P. 2005. Link mining: a survey. ACM SIGKDD Explorations Newsletter 7(2), 312.
Goodwin J. C., Cohen T. & Rindflesch T. 2012. Discovery by scent: discovery browsing system based on the information foraging theory. In Proceedings of the 2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW), 232–239. IEEE.
Gordon M. D. & Dumais S. 1998. Using latent semantic indexing for literature based discovery. Journal of the American Society for Information Science 49(8), 674685.
Gordon M. D. & Lindsay R. K. 1996. Toward discovery support systems: a replication, re-examination, and extension of Swanson’s work on literature-based discovery of a connection between Raynaud’s and fish oil. Journal of the American Society for Information Science 47(2), 116128.
Gordon M., Lindsay R. K. & Fan W. 2002. Literature-based discovery on the world wide web. ACM Transactions on Internet Technology 2(4), 261275.
Hahn U., Cohen K. B., Garten Y. & Shah N. H. 2012. Mining the pharmacogenomics literature: a survey of the state of the art. Briefings in Bioinformatics 13(4), 460494.
Hristovski D., Džeroski S., Peterlin B. & Rožić A. 2000. Supporting discovery in medicine by association rule mining of bibliographic databases. In Principles of Data Mining and Knowledge Discovery: 4th European Conference, PKDD 2000 Lyon, France, September 13–16, 2000 Proceedings, Zighed, D. A., Komorowski, J, Żytkow, J. (eds). Springer Berlin Heidelberg, 149159.
Hristovski D., Friedman C., Rindflesch T. C. & Peterlin B. 2006. Exploiting semantic relations for literature-based discovery. In Proceedings of the 2006 AMIA Symposium, 349–353.
Hu X., Yoo I., Song M., Zhang Y. & Song I.-Y. 2005. Mining undiscovered public knowledge from complementary and non-interactive biomedical literature through semantic pruning. In Proceedings of the 14th ACM International Conference on Information and Knowledge Management, CIKM ’05, 249–250. ACM.
Ittipanuvat V., Fujita K., Sakata I. & Kajikawa Y. 2014. Finding linkage between technology and social issue: a literature based discovery approach. Journal of Engineering and Technology Management 32, 160184.
Janssens F., Glänzel W. & De Moor B. 2008. A hybrid mapping of information science. Scientometrics 75(3), 607631.
Jensen L. J., Saric J. & Bork P. 2006. Literature mining for the biologist: from information retrieval to biological discovery. Nature Reviews Genetics 7(2), 119129.
Juršič M., Sluban B., Cestnik B., Grčar M. & Lavrač N. 2012. Bridging concept identification for constructing information networks from text documents. In Bisociative Knowledge Discovery: An Introduction to Concept, Algorithms, Tools, and Applications, M. R. Berthold (ed.). Springer Berlin Heidelberg, 6690.
Kastrin A., Rindflesch T. C. & Hristovski D. 2013. Link prediction in a mesh co-occurrence network: preliminary results. Studies in Health Technology and Informatics 205, 579583.
Kessler M. M. 1963. Bibliographic coupling between scientific papers. American Documentation 14(1), 1025.
Kleinberg J. M. 1999. Authoritative sources in a hyperlinked environment. Journal of the ACM 46(5), 604632.
Kostoff R. N. 2007. Validating discovery in literature-based discovery. Journal of Biomedical Informatics 40(4), 448450.
Kostoff R. N. 2008. Literature-related discovery (LRD): potential treatments for cataracts. Technological Forecasting and Social Change 75(2), 215225.
Kostoff R. N. 2012. Literature-related discovery and innovation update. Technological Forecasting and Social Change 79(4), 789800.
Kostoff R. N. 2014. Literature-related discovery: common factors for Parkinson’s disease and Crohn’s disease. Scientometrics 100(3), 623657.
Kostoff R. N., Block J. A., Solka J. L., Briggs M. B., Rushenberg R. L., Stump J. A., Johnson D., Lyons T. J. & Wyatt J. R. 2009. Literature-related discovery. Annual Review of Information Science and Technology 43(1), 171.
Kostoff R. N. & Briggs M. B. 2008. Literature-related discovery (LRD): potential treatments for Parkinson’s disease. Technological Forecasting and Social Change 75(2), 226238.
Kostoff R. N., Briggs M. B. & Lyons T. J. 2008. Literature-related discovery (LRD): potential treatments for multiple sclerosis. Technological Forecasting and Social Change 75(2), 239255.
Kostoff R. N., Solka J. L., Rushenberg R. L. & Wyatt J. A. 2008. Literature-related discovery (LRD): water purification. Technological Forecasting and Social Change 75(2), 256275.
Kraines S. B., Guo W., Hoshiyama D., Makino T., Mizutani H., Okuda Y., Shidahara Y. & Takagi T. 2010. Literature-based knowledge discovery from relationship associations based on a DL ontology created from mesh. In Proceedings of the International Joint Conference on Knowledge Discovery, Knowledge Engineering, and Knowledge Management, 87–106. Springer.
Larsen P. O. & Von Ins M. 2010. The rate of growth in scientific publication and the decline in coverage provided by science citation index. Scientometrics 84(3), 575603.
Leskovec J., Kleinberg J. & Faloutsos C. 2005. Graphs over time: densification laws, shrinking diameters and possible explanations. In Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, 177–187. ACM.
Leskovec J., Lang K. J. & Mahoney M. 2010. Empirical comparison of algorithms for network community detection. In Proceedings of the 19th International Conference on World Wide Web, 631–640. ACM.
Li C., Liakata M. & Rebholz-Schuhmann D. 2014. Biological network extraction from scientific literature: state of the art and challenges. Briefings in Bioinformatics 15(5), 856877.
Li J., Zhu X. & Chen J. Y. 2010. Discovering breast cancer drug candidates from biomedical literature. International Journal of Data Mining and Bioinformatics 4(3), 241255.
Lindsay R. K. & Gordon M. D. 1999. Literature-based discovery by lexical statistics. Journal of the Association for Information Science and Technology 50(7), 574.
Lytras M., Sicilia M.-A., Davies J., Kashyap V. & Hu X. 2005. Mining novel connections from large online digital library using biomedical ontologies. Library Management 26(4/5), 261270.
Manning C. D., Raghavan P. & Schütze H. 2008. Introduction to Information Retrieval. Cambridge University Press.
Marsi E. & Öztürk P. 2015. Extraction and generalisation of variables from scientific publications. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP 2015).
Marsi E., Oztürk P., Aamot E., Sizov G. & Ardelan M. V. 2014. Towards text mining in climate science: extraction of quantitative variables and their relations. In Proceedings of the Fourth Workshop on Building and Evaluating Resources for Health and Biomedical Text Processing, Reykjavik, Iceland.
Meyer H. S. & Lundberg G. D. 1985. Fifty-One Landmark Articles in Medicine: The JAMA Centennial Series. Chicago Review Press.
Miller C. M., Rindflesch T. C., Fiszman M., Hristovski D., Shin D., Rosemblat G., Zhang H. & Strohl K. P. 2012. A closed literature-based discovery technique finds a mechanistic link between hypogonadism and diminished sleep quality in aging men. Sleep 35(2), 279285.
Mostafa J., Seki K. & Ke W. 2009. Beyond information retrieval: literature mining for biomedical knowledge discovery. In J. Y. Chen & S. Lonardi (eds). Biological Data Mining. CRC Press, 449485.
Nakamura H., Ii S., Chida H., Friedl K., Suzuki S., Mori J. & Kajikawa Y. 2014. Shedding light on a neglected area: a new approach to knowledge creation. Sustainability Science 9(2), 193204.
Narayanasamy V., Mukhopadhyay S., Palakal M. & Potter D. A. 2004. Transminer: Mining transitive associations among biological objects from text. Journal of Biomedical Science 11(6), 864873.
Newman M. E. 2001. The structure of scientific collaboration networks. Proceedings of the National Academy of Sciences 98(2), 404409.
Newman M. E. 2003. The structure and function of complex networks. SIAM Review 45(2), 167256.
Newman M. E. 2004. Fast algorithm for detecting community structure in networks. Physical Review E 69(6), 066133.
Novacek V. 2015. Formalising hypothesis virtues in knowledge graphs: a general theoretical framework and its validation in literature-based discovery experiments. arXiv preprint arXiv:1503.09137.
Perez-Iratxeta C., Bork P. & Andrade M. A. 2002. Association of genes to genetically inherited diseases using data mining. Nature Genetics 31(3), 316319.
Perez-Iratxeta C., Wjst M., Bork P. & Andrade M. A. 2005. G2d: a tool for mining genes associated with disease. BMC Genetics 6(1), 1.
Petrič I., Cestnik B., Lavrač N. & Urbančič T. 2010. Outlier detection in cross-context link discovery for creative literature mining 55(1). The Computer Journal, 4761.
Piatetsky-Shapiro G., Djeraba C., Getoor L., Grossman R., Feldman R. & Zaki M. 2006. What are the grand challenges for data mining?: Kdd-2006 panel report. ACM SIGKDD Explorations Newsletter 8(2), 7077.
Pratt W. & Yetisgen-Yildiz M. 2003. Litlinker: capturing connections across the biomedical literature. In Proceedings of the 2nd International Conference on Knowledge Capture, K-CAP ’03, 105–112. ACM.
Preiss J. & Stevenson R. 2016. The effect of word sense disambiguation accuracy on literature based discovery. BMC Medical Informatics and Decision Making 16(Suppl 1), 57.
Preiss J., Stevenson M. & Gaizauskas R. 2015. Exploring relation types for literature-based discovery, Journal of the American Medical Informatics Association 22(5), 987992.
Salton G. & McGill M. J. 1986. Introduction to Modern Information Retrieval. McGraw-Hill.
Sebastian Y. 2014. Cluster links prediction for literature based discovery using latent structure and semantic features. In Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval, 1275–1275. ACM.
Sebastian Y., Siew E.-G. & Orimaye S. O. 2015. Predicting future links between disjoint research areas using heterogeneous bibliographic information network. In Advances in Knowledge Discovery and Data Mining: 19th Pacific-Asia Conference, PAKDD 2015, Ho Chi Minh City, Vietnam, May 19-22, T. Cao, E.-P. Lim, Z.-H. Zhou, T.-B. Ho, D. Cheung H. Motoda (eds). Springer International Publishing, 610621.
Sebastian Y., Siew E.-G. & Orimaye S. O. 2017. Learning the heterogeneous bibliographic information network for literature-based discovery. Knowledge-Based Systems 115, 6679.
Seki K. 2015. Hypothesis discovery exploiting closed chains of relation. In A. Hameurlain, J. Küng & R. Wagner (eds). Transactions on Large-Scale Data- and Knowledge-Centered Systems XXII. Springer Berlin Heidelberg, 145164.
Shang N., Xu H., Rindflesch T. C. & Cohen T. 2014. Identifying plausible adverse drug reactions using knowledge extracted from the literature. Journal of Biomedical Informatics 52, 293310.
Smalheiser N. R. 2012. Literature-based discovery: beyond the ABCs. Journal of the American Society for Information Science and Technology 63(2), 218224.
Smalheiser N. R. & Swanson D. R. 1996a. Indomethacin and Alzheimer’s disease. Neurology 46(2), 583583.
Smalheiser N. R. & Swanson D. R. 1996b. Linking estrogen to Alzheimer’s disease an informatics approach. Neurology 47(3), 809810.
Smalheiser N. R. & Torvik V. I. 2008. The place of literature-based discovery in contemporary scientific practice. In P. Bruza & M. Weeber (eds). Literature-Based Discovery. Springer Berlin Heidelberg, 1322.
Small H. 2010. Maps of science as interdisciplinary discourse: co-citation contexts and the role of analogy. Scientometrics 83(3), 835849.
Sneed W. A. 2003. Knowledge Synthesis in the Biomedical Literature: Nordihydroguaiaretic Acid and Breast Cancer. PhD thesis, University of North Texas.
Song M., Han N.-G., Kim Y.-H., Ding Y. & Chambers T. 2013. Discovering implicit entity relation with the gene-citation-gene network. PloS One 8(12), e84639.
Song M., Heo G. E. & Ding Y. 2015. SemPathFinder: semantic path analysis for discovering publicly unknown knowledge. Journal of Informetrics 9(4), 686703.
Srinivasan P. 2004. Text mining: generating hypotheses from medline. Journal of the American Society for Information Science and Technology 55(5), 396413.
Srinivasan P. & Libbus B. 2004. Mining medline for implicit links between dietary substances and diseases. Bioinformatics 20(Suppl 1), i290i296.
Srinivasan P., Libbus B. & Sehgal A. K. 2004. Mining medline: postulating a beneficial role for curcumin longa in retinal diseases. In Workshop BioLINK, Linking Biological Literature, Ontologies and Databases at HLT NAACL, 33–40.
Stegmann J. & Grohmann G. 2003. Hypothesis generation guided by co-word clustering. Scientometrics 56(1), 111135.
Sun Y. & Han J. 2012. Mining heterogeneous information networks: principles and methodologies. Synthesis Lectures on Data Mining and Knowledge Discovery 3(2), 1159.
Swanson D. 2008. Literature-based discovery? The very idea. In Literature-Based Discovery, Peter Bruza & Marc Weeber (eds.). Springer, 311.
Swanson D. R. 1979. Libraries and the growth of knowledge. The Library Quarterly 49(1), 325.
Swanson D. R. 1986a. Fish oil, Raynaud’s syndrome, and undiscovered public knowledge. Perspectives in Biology and Medicine 30(1), 718.
Swanson D. R. 1986b. Undiscovered public knowledge. The Library Quarterly 56(2), 103118.
Swanson D. R. 1987. Two medical literatures that are logically but not bibliographically connected. Journal of the American Society for Information Science 38(4), 228.
Swanson D. R. 1988. Migraine and magnesium: eleven neglected connections. Perspectives in Biology and Medicine 31(4), 526557.
Swanson D. R. 1990. The absence of co-citation as a clue to undiscovered causal connections. Scholarly Communication and Bibliometrics, 129137.
Swanson D. R. 1993. Intervening in the life cycles of scientific knowledge. Library Trends 41(4), 606631.
Swanson D. R. & Smalheiser N. R. 1997. An interactive system for finding complementary literatures: a stimulus to scientific discovery. Artificial Intelligence 91(2), 183203.
Symonds M., Bruza P. & Sitbon L. 2014. The efficiency of corpus-based distributional models for literature-based discovery on large data sets. In Proceedings of the Second Australasian Web Conference – Volume 155, AWC ’14, 49–57.
Tarjan R. 1972. Depth-first search and linear graph algorithms. SIAM Journal on Computing 1(2), 146160.
Torvik V. I. & Smalheiser N. R. 2007. A quantitative model for linking two disparate sets of articles in medline. Bioinformatics 23(13), 16581665.
Uzzi B., Mukherjee S., Stringer M. & Jones B. 2013. Atypical combinations and scientific impact. Science 342(6157), 468472.
Valdés-Pérez R. E. 1999. Principles of human-computer collaboration for knowledge discovery in science. Artificial Intelligence 107(2), 335346.
van Haagen H.H., AC’t Hoen P., Bovo A.B., de Morrée A., van Mulligen E.M., Chichester C., Kors J.A., den Dunnen J.T., van Ommen G.J.B., van der Maarel S.M. & Kern V.M. 2009. Novel protein-protein interactions inferred from literature context. PLoS One 4(11), e7894.
van Haagen H. H., ’t Hoen P. A., de Morree A., van Roon-Mom W., Peters D. J., Roos M., Mons B., van Ommen G.-J. & Schuemie M. J. 2011. In silico discovery and experimental validation of new protein–protein interactions. Proteomics 11(5), 843853.
van Mulligen E. M., van Der Eijk C., Kors J. A., Schijvenaars B. J. & Mons B. 2002. Research for research: tools for knowledge discovery and visualization. In Proceedings of the 2002 AMIA Symposium, 835. American Medical Informatics Association.
Waltman L. & Eck N. J. 2012. A new methodology for constructing a publication-level classification system of science. Journal of the American Society for Information Science and Technology 63(12), 23782392.
Weeber M., Klein H., de Jong-van den Berg L. & Vos R. 2001. Using concepts in literature-based discovery: simulating Swanson’s Raynaud–fish oil and migraine–magnesium discoveries. Journal of the American Society for Information Science and Technology 52(7), 548557.
Weeber M., Kors J. A. & Mons B. 2005. Online tools to support literature-based discovery in the life sciences. Briefings in Bioinformatics 6(3), 277286.
Weeber M., Vos R., Klein H., Aronson A. R. & Molema G. 2003. Generating hypotheses by discovering implicit associations in the literature: a case report of a search for new potential therapeutic uses for thalidomide. Journal of the American Medical Informatics Association 10(3), 252259.
Wei C.-P., Chen K.-A. & Chen L.-C. 2014. Mining biomedical literature and ontologies for drug repositioning discovery. In Advances in Knowledge Discovery and Data Mining: 18th Pacific-Asia Conference, PAKDD 2014, Tainan, Taiwan, May 13-16, V. S. Tseng, T. B. Ho, Z.-H. Zhou, A. L. P. Chen & H.-Y. Kao (eds). Springer International Publishing, 373384.
White H. D. & Griffith B. C. 1981. Author cocitation: a literature measure of intellectual structure. Journal of the American Society for Information Science 32(3), 163171.
Wilkowski B., Fiszman M., Miller C. M., Hristovski D., Arabandi S., Rosemblat G. & Rindflesch T. C. 2011. Graph-based methods for discovery browsing with semantic predications. In Proceedings of the 2011 AMIA Symposium, 2011, 1514. American Medical Informatics Association.
Witten I. H. & Frank E. 2005. Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann.
Wren J. D. 2004. Extending the mutual information measure to rank inferred literature relationships. BMC Bioinformatics 5(1), 1.
Wren J. D. 2008. The ‘open discovery’ challenge. In Literature-Based Discovery, P. Bruza & M. Weeber (eds). Springer Berlin Heidelberg, 3955.
Wren J. D., Bekeredjian R., Stewart J. A., Shohet R. V. & Garner H. R. 2004. Knowledge discovery by automated identification and ranking of implicit relationships. Bioinformatics 20(3), 389398.
Yamamoto Y. & Takagi T. 2007. Biomedical knowledge navigation by literature clustering. Journal of Biomedical Informatics 40(2), 114130.
Yetisgen-Yildiz M. 2006. Litlinker: a system for searching potential discoveries in biomedical literature. In Proceedings of 29th Annual International ACM SIGIR Conference on Research & Development on Information Retrieval (SIGIR’06) Doctoral Consortium, Seattle, WA.
Yetisgen-Yildiz M. & Pratt W. 2006. Using statistical and knowledge-based approaches for literature-based discovery. Journal of Biomedical Informatics 39(6), 600611.
Yetisgen-Yildiz M. & Pratt W. 2008. Evaluation of literature-based discovery systems. In Literature-Based Discovery, P. Bruza & M. Weeber (eds). Springer Berlin Heidelberg, 101113.
Yetisgen-Yildiz M. & Pratt W. 2009. A new evaluation methodology for literature-based discovery systems. Journal of Biomedical Informatics 42(4), 633643.
Youn H., Strumsky D., Bettencourt L. M. & Lobo J. 2015. Invention as a combinatorial process: evidence from US patents. Journal of The Royal Society Interface 12(106), 20150272.
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