Sang-Nae Cho

4.5k total citations
76 papers, 2.6k citations indexed

About

Sang-Nae Cho is a scholar working on Infectious Diseases, Epidemiology and Surgery. According to data from OpenAlex, Sang-Nae Cho has authored 76 papers receiving a total of 2.6k indexed citations (citations by other indexed papers that have themselves been cited), including 66 papers in Infectious Diseases, 62 papers in Epidemiology and 25 papers in Surgery. Recurrent topics in Sang-Nae Cho's work include Tuberculosis Research and Epidemiology (63 papers), Mycobacterium research and diagnosis (57 papers) and Infectious Diseases and Tuberculosis (22 papers). Sang-Nae Cho is often cited by papers focused on Tuberculosis Research and Epidemiology (63 papers), Mycobacterium research and diagnosis (57 papers) and Infectious Diseases and Tuberculosis (22 papers). Sang-Nae Cho collaborates with scholars based in South Korea, United States and France. Sang-Nae Cho's co-authors include Laura E. Via, Clifton E. Barry, Seok-Yong Eum, Soo-Hee Hwang, Ye Jin Lee, Jin Hee Kim, Hyeyoung Lee, Patrick J. Brennan, Sung Jae Shin and Bo‐Young Jeon and has published in prestigious journals such as The Lancet, Nature Communications and The Journal of Immunology.

In The Last Decade

Sang-Nae Cho

76 papers receiving 2.6k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Sang-Nae Cho South Korea 30 2.0k 1.8k 762 503 428 76 2.6k
Claudia Manca United States 24 2.4k 1.2× 2.0k 1.1× 788 1.0× 597 1.2× 805 1.9× 44 3.1k
Paul J. Converse United States 30 1.8k 0.9× 1.6k 0.9× 482 0.6× 746 1.5× 359 0.8× 77 2.6k
Lanfranco Fattorini Italy 32 2.0k 1.0× 1.7k 1.0× 474 0.6× 695 1.4× 668 1.6× 102 3.0k
Xiuqin Zhao China 24 1.2k 0.6× 1.1k 0.6× 551 0.7× 396 0.8× 227 0.5× 114 1.8k
Pilar Domenech Canada 21 1.7k 0.8× 1.6k 0.9× 518 0.7× 623 1.2× 221 0.5× 31 2.2k
Alexander Apt Russia 29 1.6k 0.8× 1.3k 0.7× 315 0.4× 584 1.2× 993 2.3× 112 2.5k
Liana Tsenova United States 31 2.9k 1.4× 2.2k 1.2× 1.0k 1.4× 1.0k 2.0× 944 2.2× 40 3.9k
Varalakshmi Vissa United States 26 1.5k 0.8× 1.4k 0.8× 433 0.6× 861 1.7× 210 0.5× 51 2.3k
Roberto Colangeli United States 25 1.7k 0.8× 1.4k 0.8× 364 0.5× 738 1.5× 268 0.6× 30 2.2k
Wladimir Malaga France 22 1.6k 0.8× 1.3k 0.7× 337 0.4× 708 1.4× 369 0.9× 37 2.0k

Countries citing papers authored by Sang-Nae Cho

Since Specialization
Citations

This map shows the geographic impact of Sang-Nae Cho's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Sang-Nae Cho with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sang-Nae Cho more than expected).

Fields of papers citing papers by Sang-Nae Cho

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sang-Nae Cho. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Sang-Nae Cho. The network helps show where Sang-Nae Cho may publish in the future.

Co-authorship network of co-authors of Sang-Nae Cho

This figure shows the co-authorship network connecting the top 25 collaborators of Sang-Nae Cho. A scholar is included among the top collaborators of Sang-Nae Cho based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Sang-Nae Cho. Sang-Nae Cho is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Moss, Caitlin E., Megan K. Proulx, Clare M. Smith, et al.. (2019). Common Variants in the Glycerol Kinase Gene Reduce Tuberculosis Drug Efficacy. mBio. 10(4). 76 indexed citations
2.
Hur, Yun‐Gyoung, et al.. (2019). A Feasibility Study for Diagnosis of Latent Tuberculosis Infection Using an IGRA Point-of-Care Platform in South Korea. Yonsei Medical Journal. 60(4). 375–375. 8 indexed citations
3.
Lee, Jae Jin, Sun‐Kyung Lee, Benjamin M. Swarts, et al.. (2019). Transient drug-tolerance and permanent drug-resistance rely on the trehalose-catalytic shift in Mycobacterium tuberculosis. Nature Communications. 10(1). 2928–2928. 79 indexed citations
4.
Lee, Hyejon, Jungho Kim, Young Ae Kang, et al.. (2019). In vitro Mycobacterial Growth Inhibition in South Korean Adults With Latent TB Infection. Frontiers in Immunology. 10. 896–896. 13 indexed citations
5.
6.
7.
Kim, Joo Hyoung, Young Wook Chang, Hyun Jeong Kim, et al.. (2013). Detection of IFN-γ for latent tuberculosis diagnosis using an anodized aluminum oxide-based capacitive sensor. Biosensors and Bioelectronics. 51. 366–370. 20 indexed citations
8.
Hong, Ji Young, Song Yee Kim, Kyung Soo Chung, et al.. (2013). Efficacy of IP-10 as a biomarker for monitoring tuberculosis treatment. Journal of Infection. 68(3). 252–258. 39 indexed citations
9.
Kim, Sunghyun, Young Keun Kim, Hyejon Lee, et al.. (2012). Interferon gamma mRNA quantitative real-time polymerase chain reaction for the diagnosis of latent tuberculosis: a novel interferon gamma release assay. Diagnostic Microbiology and Infectious Disease. 75(1). 68–72. 19 indexed citations
10.
Dalton, Tracy, Peter Cegielski, Somsak Akksilp, et al.. (2012). Prevalence of and risk factors for resistance to second-line drugs in people with multidrug-resistant tuberculosis in eight countries: a prospective cohort study. The Lancet. 380(9851). 1406–1417. 163 indexed citations
11.
12.
Jung, Ji Ye, Hye‐Jeong Lee, Young Mi Kim, et al.. (2011). Questionable role of interferon-γ assays for smear-negative pulmonary TB in immunocompromised patients. Journal of Infection. 64(2). 188–196. 24 indexed citations
13.
Eum, Seok-Yong, Bo‐Young Jeon, Sang-Nae Cho, et al.. (2010). Metaplastic ossification in the cartilage of the bronchus of a patient with chronic multi-drug resistant tuberculosis: a case report. Journal of Medical Case Reports. 4(1). 156–156. 6 indexed citations
14.
Lee, In-Soo, et al.. (2009). Microplate hybridization assay for detection of isoniazid resistance in Mycobacterium tuberculosis. BMB Reports. 42(2). 81–85. 1 indexed citations
15.
Eum, Seok-Yong, Ye Jin Lee, Jin Hee Kim, et al.. (2009). Neutrophils Are the Predominant Infected Phagocytic Cells in the Airways of Patients With Active Pulmonary TB. CHEST Journal. 137(1). 122–128. 380 indexed citations
16.
Lee, Jong Seok, Roland Krause, J. Schreiber, et al.. (2008). Mutation in the Transcriptional Regulator PhoP Contributes to Avirulence of Mycobacterium tuberculosis H37Ra Strain. Cell Host & Microbe. 3(2). 97–103. 138 indexed citations
17.
Park, Seung-Kyu, Sungae Cho, In‐Hee Lee, et al.. (2007). Subcutaneously administered interferon-gamma for the treatment of multidrug-resistant pulmonary tuberculosis. International Journal of Infectious Diseases. 11(5). 434–440. 26 indexed citations
18.
Spencer, John S., Hazel M. Dockrell, Hee Jin Kim, et al.. (2005). Identification of Specific Proteins and Peptides in Mycobacterium leprae Suitable for the Selective Diagnosis of Leprosy. The Journal of Immunology. 175(12). 7930–7938. 64 indexed citations
19.
Youn, Ju Ho, Delphi Chatterjee, Patrick J. Brennan, et al.. (2004). Production and characterization of peptide mimotopes of phenolic glycolipid-I ofMycobacterium leprae. FEMS Immunology & Medical Microbiology. 41(1). 51–57. 11 indexed citations
20.
Victor, Thomas C., Hyeyoung Lee, Sang-Nae Cho, et al.. (2002). Molecular Detection of Early Appearance of Drug Resistance during Mycobacterium tuberculosis Infection. Clinical Chemistry and Laboratory Medicine (CCLM). 40(9). 876–81. 12 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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