PublisherDOIYearVolumeIssuePageTitleAuthor(s)Link
Genomics & Informatics10.5808/gi.2019.17.2.e202019172e20Improving methods for normalizing biomedical text entities with concepts from an ontology with (almost) no training data at BLAH5 the CONTESArnaud Ferré, Mouhamadou Ba, Robert Bossyhttp://genominfo.org/upload/pdf/gi-2019-17-2-e20.pdf, http://genominfo.org/journal/view.php?doi=10.5808/GI.2019.17.2.e20, http://genominfo.org/upload/pdf/gi-2019-17-2-e20.pdf
IEEE Access10.1109/access.2019.295215420197162818-162827Jointly Extract Entities and Their Relations From Biomedical TextJizhi Chen, Junzhong Guhttp://xplorestaging.ieee.org/ielx7/6287639/8600701/08893999.pdf?arnumber=8893999
Microprocessors and Microsystems10.1016/j.micpro.2016.03.003201646202-210Leveraging MapReduce to efficiently extract associations between biomedical concepts from large text dataYanqing Ji, Yun Tian, Fangyang Shen, John Tranhttps://api.elsevier.com/content/article/PII:S0141933116300072?httpAccept=text/xml, https://api.elsevier.com/content/article/PII:S0141933116300072?httpAccept=text/plain
Journal of Biomedical Informatics10.1016/j.yjbinx.2019.1000052019100100005Comparing breast cancer treatments using automatically detected surrogate and clinically relevant outcomes entities from textCatherine Blake, Rebecca Kehmhttps://api.elsevier.com/content/article/PII:S2590177X19300046?httpAccept=text/xml, https://api.elsevier.com/content/article/PII:S2590177X19300046?httpAccept=text/plain
BioMedical Engineering OnLine10.1186/s12938-018-0583-4201817S2A method of inferring the relationship between Biomedical entities through correlation analysis on textHye-Jeong Song, Byeong-Hun Yoon, Young-Shin Youn, Chan-Young Park, Jong-Dae Kim, Yu-Seop Kimhttp://link.springer.com/content/pdf/10.1186/s12938-018-0583-4.pdf, http://link.springer.com/article/10.1186/s12938-018-0583-4/fulltext.html, http://link.springer.com/content/pdf/10.1186/s12938-018-0583-4.pdf
10.1007/978-0-387-39252-32006Ontology Learning and Population from Texthttp://link.springer.com/content/pdf/10.1007/978-0-387-39252-3.pdf, http://link.springer.com/content/pdf/10.1007/978-0-387-39252-3
Inductive Databases and Constraint-Based Data Mining10.1007/978-1-4419-7738-0_2201027-58Representing Entities in the OntoDM Data Mining OntologyPanče Panov, Sašo Džeroski, Larisa N. Soldatovahttps://link.springer.com/content/pdf/10.1007/978-1-4419-7738-0_2
Journal of Biomedical Informatics10.1016/j.jbi.2019.103276201998103276Ontology-based clinical information extraction from physician’s free-text notesEngy Yehia, Hussein Boshnak, Sayed AbdelGaber, Amany Abdo, Doaa S. Elzanfalyhttps://api.elsevier.com/content/article/PII:S1532046419301959?httpAccept=text/xml, https://api.elsevier.com/content/article/PII:S1532046419301959?httpAccept=text/plain
Data Mining and Knowledge Discovery10.1007/s10618-014-0363-02014285-61222-1265Ontology of core data mining entitiesPanče Panov, Larisa Soldatova, Sašo Džeroskihttp://link.springer.com/content/pdf/10.1007/s10618-014-0363-0.pdf, http://link.springer.com/article/10.1007/s10618-014-0363-0/fulltext.html, http://link.springer.com/content/pdf/10.1007/s10618-014-0363-0
Journal of Open Source Software10.21105/joss.0170820205451708Adeft: Acromine-based Disambiguation of Entities from Text with applications to the biomedical literatureAlbert Steppi, Benjamin Gyori, John Bachmanhttp://www.theoj.org/joss-papers/joss.01708/10.21105.joss.01708.pdf