Yok‐Ai Que

8.8k citations
104 papers · 5.5k indexed · 3 hit papers · h-index 37

Yok‐Ai Que

104 papers receiving 5.4k citations

Hit Papers

Pulmonary function...2652004202620112018200400600

Peers

Yok‐Ai Que
Comparison fields: 5 of 148
  • Microbiology 784
  • Molecular Medicine 561
  • Infectious Diseases 1.9k
  • Applied Microbiology and Biotechnology 137
  • Epidemiology 1.8k
Replace Anthony Coates with:
Anthony Coates United Kingdom
Marco R. Oggioni Italy
Rosa del Campo Spain
Kazuhiro Tateda Japan
Holger Rohde Germany
Andrej Weintraub Sweden
Katsunori Yanagihara Japan
Lee‐Jene Teng Taiwan
Franklin D. Lowy United States
Eileen M. Burd United States
Yok‐Ai Que relative to Anthony Coates United Kingdom Anthony Coates's profile →
Citations per field
00.5×4.5×
Anthony Coates · 1×
Citations per year

Countries citing papers authored by Yok‐Ai Que

Since Specialization
Citations

This map shows the geographic impact of Yok‐Ai Que'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 Yok‐Ai Que with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yok‐Ai Que more than expected).

Fields of papers citing papers by Yok‐Ai Que

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Yok‐Ai Que. 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 Yok‐Ai Que. The network helps show where Yok‐Ai Que may publish in the future.

Co-authorship network

The 25 scholars most cited alongside Yok‐Ai Que, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Yok‐Ai Que Line = papers co-authored together Yok‐Ai Que links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20251
2 20241
3 20229
4 20222
5 202216
6 202119
7 202138
8 202129
9 20211
10 20215
11 202110
12 20208
13 20197
14 201960
15 201832
16 201631
17 201527
18 201513
19 201526
20 199823

About Yok‐Ai Que

Yok‐Ai Que is a scholar working on Molecular Medicine, Critical Care and Intensive Care Medicine and Applied Microbiology and Biotechnology, having authored 104 papers that have together received 5.5k indexed citations. Recurring topics across this work include Antimicrobial Resistance in Staphylococcus (21 papers), Bacterial biofilms and quorum sensing (19 papers), Antibiotic Resistance in Bacteria (18 papers), Bacteriophages and microbial interactions (16 papers), Streptococcal Infections and Treatments (14 papers), Infective Endocarditis Diagnosis and Management (13 papers), Burn Injury Management and Outcomes (11 papers) and Sepsis Diagnosis and Treatment (10 papers). The work is most often cited by research in Microbiology (784 citations), Molecular Medicine (561 citations) and Infectious Diseases (1.9k citations). Yok‐Ai Que has collaborated with scholars based in Switzerland, United States and France. Frequent co-authors include Philippe Moreillon, Philippe Moreillon, José M. Entenza, Laurence G. Rahme, Grégory Resch, Ronen Hazan, Jacques‐Antoine Haefliger, Philippe Eggimann, Jérôme Gabard and Patrice François. Their work appears in journals such as Antimicrobial Agents and Chemotherapy, Infection and Immunity, The Journal of Infectious Diseases, Burns and Critical Care.

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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