This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Arun, R., Suresh, V., Madhavan, C.E.V., & Murty, M.N. (2010). On Finding the Natural Number of Topics with Latent Dirichlet Allocation: Some Observations. In M.J. Zaki, J.X. Yu, B. Ravindran, & V. Pudi (Eds.), Advances in Knowledge Discovery and Data Mining, Pt I, Proceedings (Vol. 6118, pp. 391–402). Berlin: Springer-Verlag Berlin.ArunR.SureshV.MadhavanC.E.V.MurtyM.N.2010On Finding the Natural Number of Topics with Latent Dirichlet Allocation: Some ObservationsInZakiM.J.YuJ.X.RavindranB.PudiV.(Eds.),Advances in Knowledge Discovery and Data Mining, Pt I, Proceedings6118391402BerlinSpringer-Verlag Berlin10.1007/978-3-642-13657-3_43Search in Google Scholar
Baltimore, D., Berg, P., Botchan, M., Carroll, D., Charo, R.A., Church, G., … Yamamoto, K.R. (2015). A prudent path forward for genomic engineering and germline gene modification. Science, 348(6230), 36–38. doi:10.1126/science.aab1028BaltimoreD.BergP.BotchanM.CarrollD.CharoR.A.ChurchG.YamamotoK.R.2015A prudent path forward for genomic engineering and germline gene modificationScience3486230363810.1126/science.aab1028439418325791083Open DOISearch in Google Scholar
Blei, D.M., Ng, A.Y., & Jordan, M.I. (2003). Latent dirichlet allocation. Journal of Machine Learning Research, 3(4/5), 993–1022BleiD.M.NgA.Y.JordanM.I.2003Latent dirichlet allocationJournal of Machine Learning Research34/59931022Search in Google Scholar
Blei, D.M. (2012). Probabilistic topic models. Communications of the Acm, 55(4), 77–84. doi:10.1145/2133806.2133826BleiD.M.2012Probabilistic topic modelsCommunications of the Acm554778410.1145/2133806.2133826Open DOISearch in Google Scholar
Blei, D.M., & Lafferty, J.D. (2006). Dynamic topic models. In Proceedings of the 23rd international conference on Machine learning (pp. 113–120).BleiD.M.LaffertyJ.D.2006Dynamic topic modelsInProceedings of the 23rd international conference on Machine learning11312010.1145/1143844.1143859Search in Google Scholar
Blei, D.M., & Lafferty, J.D. (2007). A correlated topic model of science. Annals of Applied Statistics, 1(1), 17–35. doi:10.1214/07-aoas114BleiD.M.LaffertyJ.D.2007A correlated topic model of scienceAnnals of Applied Statistics11173510.1214/07-aoas114Open DOISearch in Google Scholar
Campbell, A., Cavalade, C., Haunold, C., Karanikic, P., & Piccaluga, A. (2020). Knowledge Transfer Metrics. Towards a European-wide set of harmonised indicators. EUR, 30218CampbellA.CavaladeC.HaunoldC.KaranikicP.PiccalugaA.2020Knowledge Transfer Metrics. Towards a European-wide set of harmonised indicators. EUR, 30218Search in Google Scholar
Doudna, J.A. (2020). The promise and challenge of therapeutic genome editing. Nature, 578(7794), 229–236. doi:10.1038/s41586-020-1978-5DoudnaJ.A.2020The promise and challenge of therapeutic genome editingNature578779422923610.1038/s41586-020-1978-5899261332051598Open DOISearch in Google Scholar
Doudna, J.A., & Charpentier, E. (2014). The new frontier of genome engineering with CRISPR-Cas9. Science, 346(6213), 1077–+. doi:10.1126/science.1258096DoudnaJ.A.CharpentierE.2014The new frontier of genome engineering with CRISPR-Cas9Science3466213107710.1126/science.125809625430774Open DOISearch in Google Scholar
Doudna, J.A., & Gersbach, C.A. (2015). Genome editing: The end of the beginning. Genome Biology, 16. doi:10.1186/s13059-015-0860-5DoudnaJ.A.GersbachC.A.2015Genome editing: The end of the beginningGenome Biology1610.1186/s13059-015-0860-5469935626700220Open DOISearch in Google Scholar
Figuerola, C.G., Marco, F.J.G., & Pinto, M. (2017). Mapping the evolution of library and information science (1978–2014) using topic modeling on LISA. Scientometrics, 112(3), 1507–1535. doi:10.1007/s11192-017-2432-9FiguerolaC.G.MarcoF.J.G.PintoM.2017Mapping the evolution of library and information science (1978–2014) using topic modeling on LISAScientometrics11231507153510.1007/s11192-017-2432-9Open DOISearch in Google Scholar
Fukuzawa, N., & Ida, T. (2016). Science linkages between scientific articles and patents for leading scientists in the life and medical sciences field: The case of Japan. Scientometrics, 106(2), 629–644. doi:10.1007/s11192-015-1795-zFukuzawaN.IdaT.2016Science linkages between scientific articles and patents for leading scientists in the life and medical sciences field: The case of JapanScientometrics106262964410.1007/s11192-015-1795-zOpen DOISearch in Google Scholar
Gittelman, M., & Kogut, B. (2003). Does good science lead to valuable knowledge? Biotechnology firms and the evolutionary logic of citation patterns. Management Science, 49(4), 366–382. doi:10.1287/mnsc.49.4.366.14420GittelmanM.KogutB.2003Does good science lead to valuable knowledge? Biotechnology firms and the evolutionary logic of citation patternsManagement Science49436638210.1287/mnsc.49.4.366.14420Open DOISearch in Google Scholar
Griffiths, T.L., & Steyvers, M. (2004). Finding scientific topics. In Proceedings of the National Academy of Sciences of the United States of America, 101, 5228–5235. doi:10.1073/pnas.0307752101GriffithsT.L.SteyversM.2004Finding scientific topicsInProceedings of the National Academy of Sciences of the United States of America1015228523510.1073/pnas.030775210138730014872004Open DOISearch in Google Scholar
Gupta, P., & Gulati, P. (2021). Implementation and comparison of topic modeling techniques based on user reviews in e-commerce recommendations. Journal of Ambient Intelligence and Humanized Computing, 12(5), 5055–5070. doi:10.1007/s12652-020-01956-6GuptaP.GulatiP.2021Implementation and comparison of topic modeling techniques based on user reviews in e-commerce recommendationsJournal of Ambient Intelligence and Humanized Computing1255055507010.1007/s12652-020-01956-6Open DOISearch in Google Scholar
Gurwitz, D. (2014). Gene drives raise dual-use concerns. Science, 345(6200), 1010–1010. doi:10.1126/science.345.6200.1010-bGurwitzD.2014Gene drives raise dual-use concernsScience34562001010101010.1126/science.345.6200.1010-b25170142Open DOISearch in Google Scholar
Han, X.Y. (2020). Evolution of research topics in LIS between 1996 and 2019: An analysis based on latent Dirichlet Allocation topic model. Scientometrics, 125(3), 2561–2595. doi:10.1007/s11192-020-03721-0HanX.Y.2020Evolution of research topics in LIS between 1996 and 2019: An analysis based on latent Dirichlet Allocation topic modelScientometrics12532561259510.1007/s11192-020-03721-0Open DOISearch in Google Scholar
Hsu, P.D., Lander, E.S., & Zhang, F. (2014). Development and applications of CRISPR-Cas9 for genome engineering. Cell, 157(6), 1262–1278. doi:10.1016/j.cell.2014.05.010HsuP.D.LanderE.S.ZhangF.2014Development and applications of CRISPR-Cas9 for genome engineeringCell15761262127810.1016/j.cell.2014.05.010434319824906146Open DOISearch in Google Scholar
Hoffman, M., Bach, F., & Blei, D. (2010). Online learning for latent dirichlet allocation. Advances in Neural Information Processing Systems, 23, 856–864.HoffmanM.BachF.BleiD.2010Online learning for latent dirichlet allocationAdvances in Neural Information Processing Systems23856864Search in Google Scholar
Hu, X., & Rousseau, R. (2018). A new approach to explore the knowledge transition path in the evolution of science & technology: From the biology of restriction enzymes to their application in biotechnology. Journal of Informetrics, 12(3), 842–857. doi:10.1016/j.joi.2018.07.004HuX.RousseauR.2018A new approach to explore the knowledge transition path in the evolution of science & technology: From the biology of restriction enzymes to their application in biotechnologyJournal of Informetrics12384285710.1016/j.joi.2018.07.004Open DOISearch in Google Scholar
Jiang, T., Liu, X.P., Zhang, C., Yin, C.A.H., & Liu, H.Z. (2021). Overview of trends in global single cell research based on bibliometric analysis and LDA model (2009–2019). Journal of Data and Information Science, 6(2), 163–178. doi:10.2478/jdis-2021-0008JiangT.LiuX.P.ZhangC.YinC.A.H.LiuH.Z.2021Overview of trends in global single cell research based on bibliometric analysis and LDA model (2009–2019)Journal of Data and Information Science6216317810.2478/jdis-2021-0008Open DOISearch in Google Scholar
Kim, H., & Kim, J.S. (2014). A guide to genome engineering with programmable nucleases. Nature Reviews Genetics, 15(5), 321–334. doi:10.1038/nrg3686KimH.KimJ.S.2014A guide to genome engineering with programmable nucleasesNature Reviews Genetics15532133410.1038/nrg368624690881Open DOISearch in Google Scholar
Knott, G.J., & Doudna, J.A. (2018). CRISPR-Cas guides the future of genetic engineering. Science, 361(6405), 866–869. doi:10.1126/science.aat5011KnottG.J.DoudnaJ.A.2018CRISPR-Cas guides the future of genetic engineeringScience361640586686910.1126/science.aat5011645591330166482Open DOISearch in Google Scholar
Kushkowski, J.D., Shrader, C.B., Anderson, M.H., & White, R.E. (2020). Information flows and topic modeling in corporate governance. Journal of Documentation, 76(6), 1313–1339. doi:10.1108/jd-10-2019-0207KushkowskiJ.D.ShraderC.B.AndersonM.H.WhiteR.E.2020Information flows and topic modeling in corporate governanceJournal of Documentation7661313133910.1108/jd-10-2019-0207Open DOISearch in Google Scholar
Lamba, M., & Madhusudhan, M. (2019). Mapping of topics in DESIDOC Journal of Library and Information Technology, India: A study. Scientometrics, 120(2), 477–505. doi:10.1007/s11192-019-03137-5LambaM.MadhusudhanM.2019Mapping of topics in DESIDOC Journal of Library and Information Technology, India: A studyScientometrics120247750510.1007/s11192-019-03137-5Open DOISearch in Google Scholar
Ledford, H. (2015). CRISPR, the disruptor. Nature, 522(7554), 20–24. doi:10.1038/522020aLedfordH.2015CRISPR, the disruptorNature5227554202410.1038/522020aOpen DOISearch in Google Scholar
Li, D., Azoulay, P., & Sampat, B.N. (2017). The applied value of public investments in biomedical research. Science, 356(6333), 78–81. doi:10.1126/science.aal0010LiD.AzoulayP.SampatB.N.2017The applied value of public investments in biomedical researchScience3566333788110.1126/science.aal0010Open DOISearch in Google Scholar
Lo, S.C.S. (2010). Scientific linkage of science research and technology development: A case of genetic engineering research. Scientometrics, 82(1), 109–120. doi:10.1007/s11192-009-0036-8LoS.C.S.2010Scientific linkage of science research and technology development: A case of genetic engineering researchScientometrics82110912010.1007/s11192-009-0036-8Open DOISearch in Google Scholar
McMillan, G.S., Narin, F., & Deeds, D.L. (2000). An analysis of the critical role of public science in innovation: The case of biotechnology. Research Policy, 29(1), 1–8. doi:10.1016/s0048-7333(99)00030-xMcMillanG.S.NarinF.DeedsD.L.2000An analysis of the critical role of public science in innovation: The case of biotechnologyResearch Policy2911810.1016/s0048-7333(99)00030-xOpen DOISearch in Google Scholar
Mendes, F.M.L., Castor, K., Monteiro, R., Mota, F.B., & Rocha, L.F.M. (2019). Mapping the lab-on-a-chip patent landscape through bibliometric techniques. World Patent Information, 58. doi:10.1016/j.wpi.2019.101904MendesF.M.L.CastorK.MonteiroR.MotaF.B.RochaL.F.M.2019Mapping the lab-on-a-chip patent landscape through bibliometric techniquesWorld Patent Information5810.1016/j.wpi.2019.101904Open DOISearch in Google Scholar
Miyata, Y., Ishita, E., Yang, F., Yamamoto, M., Iwase, A., & Kurata, K. (2020). Knowledge structure transition in library and information science: Topic modeling and visualization. Scientometrics, 125(1), 665–687. doi:10.1007/s11192-020-03657-5MiyataY.IshitaE.YangF.YamamotoM.IwaseA.KurataK.2020Knowledge structure transition in library and information science: Topic modeling and visualizationScientometrics125166568710.1007/s11192-020-03657-5Open DOISearch in Google Scholar
Newman, D.J., & Block, S. (2006). Probabilistic topic decomposition of an eighteenth-century American newspaper. Journal of the American Society for Information Science and Technology, 57(6), 753–767. doi:10.1002/asi.20342NewmanD.J.BlockS.2006Probabilistic topic decomposition of an eighteenth-century American newspaperJournal of the American Society for Information Science and Technology57675376710.1002/asi.20342Open DOISearch in Google Scholar
Pickar-Oliver, A., & Gersbach, C.A. (2019). The next generation of CRISPR-Cas technologies and applications. Nature Reviews Molecular Cell Biology, 20(8), 490–507. doi:10.1038/s41580-019-0131-5Pickar-OliverA.GersbachC.A.2019The next generation of CRISPR-Cas technologies and applicationsNature Reviews Molecular Cell Biology20849050710.1038/s41580-019-0131-5707920731147612Open DOISearch in Google Scholar
Qin, J.H., Wang, J.J., & Ye, F.Y. (2019). A metric approach to hot topics in biomedicine via keyword co-occurrence. Journal of Data and Information Science, 4(4), 13–25. doi:10.2478/jdis-2019-0018QinJ.H.WangJ.J.YeF.Y.2019A metric approach to hot topics in biomedicine via keyword co-occurrenceJournal of Data and Information Science44132510.2478/jdis-2019-0018Open DOISearch in Google Scholar
Roder, M., Both, A., Hinneburg, A., & Assoc Comp, M. (2015). Exploring the space of topic coherence measures. New York: Assoc Computing Machinery.RoderM.BothA.HinneburgA.Assoc CompM.2015Exploring the space of topic coherence measuresNew YorkAssoc Computing Machinery10.1145/2684822.2685324Search in Google Scholar
Shan B. & Li F. (2010). A survey of topic evolution based on LDA (in Chinese). Journal of Chinese Information Processing, 24(06), 43–49+68ShanB.LiF.2010A survey of topic evolution based on LDA (in Chinese)Journal of Chinese Information Processing24064349+68Search in Google Scholar
Sievert, C., & Shirley, K. (2014). LDAvis: A method for visualizing and interpreting topics. In Proceedings of the workshop on interactive language learning, visualization, and interfaces (pp. 63–70).SievertC.ShirleyK.2014LDAvis: A method for visualizing and interpreting topicsInProceedings of the workshop on interactive language learning, visualization, and interfaces637010.3115/v1/W14-3110Search in Google Scholar
Sugimoto, C.R., Li, D.F., Russell, T.G., Finlay, S.C., & Ding, Y. (2011). The shifting sands of disciplinary development: Analyzing north american library and information science dissertations using Latent Dirichlet Allocation. Journal of the American Society for Information Science and Technology, 62(1), 185–204. doi:10.1002/asi.21435SugimotoC.R.LiD.F.RussellT.G.FinlayS.C.DingY.2011The shifting sands of disciplinary development: Analyzing north american library and information science dissertations using Latent Dirichlet AllocationJournal of the American Society for Information Science and Technology62118520410.1002/asi.21435Open DOISearch in Google Scholar
Suominen, A., & Toivanen, H. (2016). Map of science with topic modeling: Comparison of unsupervised learning and human-assigned subject classification. Journal of the Association for Information Science and Technology, 67(10), 2464–2476. doi:10.1002/asi.23596SuominenA.ToivanenH.2016Map of science with topic modeling: Comparison of unsupervised learning and human-assigned subject classificationJournal of the Association for Information Science and Technology67102464247610.1002/asi.23596Open DOISearch in Google Scholar
Tijssen, R.J.W. (2010). Discarding the ‘basic science/applied science’ dichotomy: A knowledge utilization triangle classification system of research journals. Journal of the American Society for Information Science and Technology, 61(9), 1842–1852. doi:10.1002/asi.21366TijssenR.J.W.2010Discarding the ‘basic science/applied science’ dichotomy: A knowledge utilization triangle classification system of research journalsJournal of the American Society for Information Science and Technology6191842185210.1002/asi.21366Open DOISearch in Google Scholar
Tijssen, R.J.W., Buter, R.K., & van Leeuwen, T.N. (2000). Technological relevance of science: An assessment of citation linkages between patents and research papers. Scientometrics, 47(2), 389–412. Retrieved from <Go to ISI>://WOS:000089449100014TijssenR.J.W.ButerR.K.van LeeuwenT.N.2000Technological relevance of science: An assessment of citation linkages between patents and research papersScientometrics472389412Retrieved from <Go to ISI>://WOS:00008944910001410.1023/A:1005603513439Search in Google Scholar
Wang, J.J., & Ye, F.Y. (2021). Probing into the interactions between papers and patents of new CRISPR/CAS9 technology: A citation comparison. Journal of Informetrics, 15(4), 12. doi:10.1016/j.joi.2021.101189WangJ.J.YeF.Y.2021Probing into the interactions between papers and patents of new CRISPR/CAS9 technology: A citation comparisonJournal of Informetrics1541210.1016/j.joi.2021.101189Open DOISearch in Google Scholar
Wang, X., & McCallum, A. (2006). Topics over time: A non-markov continuous-time model of topical trends. In Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 424–433).WangX.McCallumA.2006Topics over time: A non-markov continuous-time model of topical trendsInProceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining42443310.1145/1150402.1150450Search in Google Scholar
WIPO. (2019). International patent classification (version 2019). Available at http://www.wipo.int/classifications/ipcWIPO2019International patent classification (version 2019)Available at http://www.wipo.int/classifications/ipcSearch in Google Scholar
van Raan, A.F.J. (2017a). Patent citations analysis and its value in research evaluation: A review and a new approach to map technology-relevant research. Journal of Data and Information Science, 2(1), 13–50. doi:10.1515/jdis-2017-0002van RaanA.F.J.2017aPatent citations analysis and its value in research evaluation: A review and a new approach to map technology-relevant researchJournal of Data and Information Science21135010.1515/jdis-2017-0002Open DOISearch in Google Scholar
van Raan, A.F.J. (2017b). Sleeping beauties cited in patents: Is there also a dormitory of inventions? Scientometrics, 110(3), 1123–1156. doi:10.1007/s11192-016-2215-8van RaanA.F.J.2017bSleeping beauties cited in patents: Is there also a dormitory of inventions?Scientometrics11031123115610.1007/s11192-016-2215-8531108728255185Open DOISearch in Google Scholar
Yau, C.K., Porter, A., Newman, N., & Suominen, A. (2014). Clustering scientific documents with topic modeling. Scientometrics, 100(3), 767–786. doi:10.1007/s11192-014-1321-8YauC.K.PorterA.NewmanN.SuominenA.2014Clustering scientific documents with topic modelingScientometrics100376778610.1007/s11192-014-1321-8Open DOISearch in Google Scholar
Zhou, H.C., Zheng, D.J., Li, Y.M., & Shen, J.W. (2019). User-opinion mining for mobile library apps in China: Exploring user improvement needs. Library Hi Tech, 37(3), 325–337. doi:10.1108/lht-05-2018-0066ZhouH.C.ZhengD.J.LiY.M.ShenJ.W.2019User-opinion mining for mobile library apps in China: Exploring user improvement needsLibrary Hi Tech37332533710.1108/lht-05-2018-0066Open DOISearch in Google Scholar
Zhou, W.Y., Yuan, Y.J., Zhang, Y.Q., & Chen, D. (2021). A decade of CRISPR gene editing in China and beyond: A scientometric landscape. Crispr Journal, 4(3), 313–320. doi:10.1089/crispr.2020.0148ZhouW.Y.YuanY.J.ZhangY.Q.ChenD.2021A decade of CRISPR gene editing in China and beyond: A scientometric landscapeCrispr Journal4331332010.1089/crispr.2020.014834152220Open DOISearch in Google Scholar
Zhu, H.C., Li, C., & Gao, C.X. (2020). Applications of CRISPR-Cas in agriculture and plant biotechnology. Nature Reviews Molecular Cell Biology, 21(11), 661–677. doi:10.1038/s41580-020-00288-9ZhuH.C.LiC.GaoC.X.2020Applications of CRISPR-Cas in agriculture and plant biotechnologyNature Reviews Molecular Cell Biology211166167710.1038/s41580-020-00288-932973356Open DOISearch in Google Scholar