Brian Kang

469 citations
6 papers · 164 · 1 hit paper · h-index 4

Impact in

Papers in

    • Bacterial biofilms and quorum sensing 3
    • CRISPR and Genetic Engineering 2
    • Bacillus and Francisella bacterial research 1
    • RNA and protein synthesis mechanisms 1
    • Antibiotic Resistance in Bacteria 3

Brian Kang

6 papers receiving 160 citations

Brian Kang's Hit Papers

Sequence modeling and design from molecular to genome scale with Evo 2024 · 127 citations
1270+1Years since publication4080120

Peers

Brian Kang
Comparison fields: 5 of 57
  • Health Informatics 6
  • Molecular Medicine 16
  • Molecular Biology 106
  • Business and International Management 3
  • Microbiology 9
Replace Wiebke Ewert with:
Wiebke Ewert Germany
Neven Šumonja Serbia
Tiago Lubiana Brazil
Yujie Gou China
Srajan Kapoor India
Andrew Muenks United States
Hsuan-Lin Her United States
Elisabetta Cacace Germany
Akira Iinishi United States
Mike Kuranda United States
Brian Kang relative to Wiebke Ewert Germany Wiebke Ewert's profile →
Citations per field
00.5×6.8×
Wiebke Ewert · 1×
Citations per year

Countries citing papers authored by Brian Kang

Since Specialization
Citations

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

Fields of papers citing papers by Brian Kang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Brian Kang. 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 Brian Kang. The network helps show where Brian Kang may publish in the future.

Co-authors

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

All Works

6 of 6 papers shown
#Work
1
Sequence modeling and design from molecular to genome scale with Evo
Hit paper breakdown →
2024127
2 201823
3 20236
4 20106
5 20171
6 20181

About Brian Kang

Brian Kang is a scholar working on Molecular Biology, Molecular Medicine, Microbiology, Organic Chemistry and Pharmacology, having authored 6 papers that have together received 164 indexed citations. Recurring topics across this work include Bacterial biofilms and quorum sensing (3 papers), Antibiotic Resistance in Bacteria (3 papers), Antimicrobial Peptides and Activities (2 papers), CRISPR and Genetic Engineering (2 papers), Cholinesterase and Neurodegenerative Diseases (1 paper), Cytomegalovirus and herpesvirus research (1 paper), Bacillus and Francisella bacterial research (1 paper) and RNA and protein synthesis mechanisms (1 paper). The work is most often cited by research in Health Informatics (6 citations), Molecular Medicine (16 citations), Molecular Biology (106 citations), Business and International Management (3 citations) and Microbiology (9 citations). Brian Kang has collaborated with scholars based in United States. Frequent co-authors include Matthew G. Durrant, Tina Hernandez‐Boussard, Dhruva Katrekar, Christopher Ré, Patrick D. Hsu, Brian Hie, Armin W. Thomas, Ashley Lewis, Madelena Y. Ng and Michael Poli. Their work appears in journals such as Journal of Industrial Microbiology & Biotechnology, The FASEB Journal, Frontiers in Molecular Biosciences, Science and Current Opinion in Biomedical Engineering.

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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