Learn−Han Lee

16.4k citations
304 papers · 12.3k indexed · 6 hit papers · h-index 60

Learn−Han Lee

299 papers receiving 12.0k citations

Hit Papers

Health Benefits and Pharmac...2182014202620182022250500750

Peers

Learn−Han Lee
Comparison fields: 5 of 180
  • Endocrinology 1.7k
  • Biotechnology 1.6k
  • Applied Microbiology and Biotechnology 281
  • Molecular Medicine 679
  • Pharmacology 2.3k
Replace Kok‐Gan Chan with:
Kok‐Gan Chan Malaysia
Bey Hing Goh Malaysia
Qingping Wu China
Shunmugiah Karutha Pandian India
Michael A. Fischbach United States
Kim Lewis United States
Friedrich Götz Germany
Octávio Luiz Franco Brazil
William Kirby United States
Paul D. Cotter Ireland
Learn−Han Lee relative to Kok‐Gan Chan Malaysia Kok‐Gan Chan's profile →
Citations per field
00.5×1.5×
Kok‐Gan Chan · 1×
Citations per year

Countries citing papers authored by Learn−Han Lee

Since Specialization
Citations

This map shows the geographic impact of Learn−Han Lee'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 Learn−Han Lee with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Learn−Han Lee more than expected).

Fields of papers citing papers by Learn−Han Lee

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Learn−Han Lee. 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 Learn−Han Lee. The network helps show where Learn−Han Lee may publish in the future.

Co-authorship network

The 25 scholars most cited alongside Learn−Han Lee, 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 Learn−Han Lee Line = papers co-authored together Learn−Han Lee links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
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7 20249
8 202421
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12 20241
13 20231
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17 202315
18 202339
19 202213
20 20211

About Learn−Han Lee

Learn−Han Lee is a scholar working on Endocrinology, Biotechnology, Applied Microbiology and Biotechnology, Pharmacology and Biochemistry, having authored 304 papers that have together received 12.3k indexed citations. Recurring topics across this work include Microbial Natural Products and Biosynthesis (53 papers), Genomics and Phylogenetic Studies (46 papers), Gut microbiota and health (27 papers), Vibrio bacteria research studies (24 papers), Aquaculture disease management and microbiota (23 papers), Enzyme Production and Characterization (22 papers), Identification and Quantification in Food (20 papers) and Phytochemicals and Antioxidant Activities (17 papers). The work is most often cited by research in Endocrinology (1.7k citations), Biotechnology (1.6k citations), Applied Microbiology and Biotechnology (281 citations), Molecular Medicine (679 citations) and Pharmacology (2.3k citations). Learn−Han Lee has collaborated with scholars based in Malaysia, China and Thailand. Frequent co-authors include Kok‐Gan Chan, Bey Hing Goh, Nurul‐Syakima Ab Mutalib, Vengadesh Letchumanan, Loh Teng‐Hern Tan, Jodi Woan‐Fei Law, Priyia Pusparajah, Hooi‐Leng Ser, Tahir Mehmood Khan and Lay‐Hong Chuah. Their work appears in journals such as Frontiers in Microbiology, Frontiers in Pharmacology, INTERNATIONAL JOURNAL OF SYSTEMATIC AND EVOLUTIONARY MICROBIOLOGY, Nutrients and Scientific Reports.

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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