1,726,137 research outputs found
Correction: Corrigendum: An analysis of disease-gene relationship from Medline abstracts by DigSee
No full textScientific Reports 7: Article number: 40154; published online: 05 January 2017; updated: 11 April 2017 In this Article, Jung-jae Kim is incorrectly listed as being affiliated with ‘1 Fusionopolis Way, #21–01 Connexis (South Tower), 138632, Singapore’. The correct affiliation is listed below: Institute for Infocomm Research, Data Analytics Department, 138632, Singapore.</jats:p
sj-docx-1-eae-10.1177_0958305X221092401 - Supplemental material for Creating portfolios of firm-specific energy R&D investment under market uncertainty
No full textSupplemental material, sj-docx-1-eae-10.1177_0958305X221092401 for Creating portfolios of firm-specific energy R&D investment under market uncertainty by Young Gwan Lee, Kihyun Park, Hyun Jae Kim and Seong-Hoon Cho in Energy & Environment</p
INTERACTIVE DESIRABILITY FUNCTION APPROACH TO MULTI-RESPONSE SURFACE OPTIMIZATION
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Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
DigChem: Identification of disease–gene–chemical relationships from Medline abstracts
No full textPaper:Jeongkyun Kim, Jung-jae Kim, Hyunju Lee* (2019) DigChem: Identification of disease-gene-chemical relationships from Medline abstracts. PLoS Computational Biology, In press.Introduction:In this study, we propose a deep learning
model based on bidirectional long short-term memory to identify the evidence sentences
of relationships among genes, chemicals, and diseases from Medline abstracts. Then, we
develop the search engine DigChem to enable disease–gene–chemical relationship
searches for 35,124 genes, 56,382 chemicals, and 5,675 diseases. We show that the
identified relationships are reliable by comparing them with manual curation and
existing databases.Description:DigChem is available at http://gcancer.org/digchem. The unique triplets identified from DigChem can be downloaded from here.</div
sj-pdf-1-jcb-10.1177_0271678X231218589 - Supplemental material for Investigation of paraclinoid aneurysm formation by comparing the combined influence of hemodynamic parameters between aneurysmal and non-aneurysmal arteries
No full textSupplemental material, sj-pdf-1-jcb-10.1177_0271678X231218589 for Investigation of paraclinoid aneurysm formation by comparing the combined influence of hemodynamic parameters between aneurysmal and non-aneurysmal arteries by Hyeondong Yang, Jung-Jae Kim, Yong Bae Kim, Kwang-Chun Cho and Je Hoon Oh in Journal of Cerebral Blood Flow & Metabolism</p
Enhanced production of tyrosine from acetate in Escherichia coli by engineering of glyoxylate cycle
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