Tadas Jakočiūnas
- Aging top 10%
- Molecular Biology top 10%
- CRISPR and Genetic Engineering 13
- Microbial Metabolic Engineering and Bioproduction 11
- RNA and protein synthesis mechanisms 8
- Fungal and yeast genetics research 5
- Viral Infectious Diseases and Gene Expression in Insects 3
- Gene Regulatory Network Analysis 2
- Receptor Mechanisms and Signaling 2
- Biotechnology top 5%
- Insect Science top 10%
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- Biofuel production and bioconversion 2
- Co-authors
- Michael K. JensenJay D. KeaslingLasse Ebdrup PedersenIrina BorodinaScott J. HarrisonMette KristensenMarkus J. HerrgårdIda Bonde
- Journals
- Proceedings of the National Academy of Sciences (1 paper)Nucleic Acids Research (2 papers)Nature Communications (1 paper)
- Partner nations
- DenmarkUnited StatesChina
In The Last Decade
Tadas Jakočiūnas
23 papers receiving 1.3k citations
Peers
Comparison fields: 5 of 77
- Aging 31
- Molecular Biology 1.2k
- Business and International Management 31
- Biotechnology 124
- Insect Science 89
Countries citing papers authored by Tadas Jakočiūnas
This map shows the geographic impact of Tadas Jakočiūnas'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 Tadas Jakočiūnas with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tadas Jakočiūnas more than expected).
Fields of papers citing papers by Tadas Jakočiūnas
This network shows the impact of papers produced by Tadas Jakočiūnas. 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 Tadas Jakočiūnas. The network helps show where Tadas Jakočiūnas may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Tadas Jakočiūnas, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 2 | |
| 2 | 2023 | 4 | |
| 3 | 2022 | 16 | |
| 4 | 2022 | 1 | |
| 5 | 2022 | 2 | |
| 6 | 2021 | 22 | |
| 7 | 2020 | 20 | |
| 8 | 2020 | 19 | |
| 9 | 2018 | 14 | |
| 10 | 2018 | 59 | |
| 11 | 2018 | 19 | |
| 12 | 2017 | 8 | |
| 13 | 2017 | 20 | |
| 14 | 2017 | 95 | |
| 15 | Easyclone-markerfree: A vector toolkit for marker-less integration of genes into saccharomyces cerevisiae via CRISPR-Cas9 | 2017 | 5 |
| 16 | 2016 | 195 | |
| 17 | 2015 | 164 | |
| 18 | 2015 | 121 | |
| 19 | 2015 | 313 | |
| 20 | 2013 | 21 |
About Tadas Jakočiūnas
Tadas Jakočiūnas is a scholar working on Molecular Biology, Biotechnology and Biochemistry, having authored 23 papers that have together received 1.3k indexed citations. Recurring topics across this work include CRISPR and Genetic Engineering (13 papers), Microbial Metabolic Engineering and Bioproduction (11 papers), RNA and protein synthesis mechanisms (8 papers), Fungal and yeast genetics research (5 papers), Viral Infectious Diseases and Gene Expression in Insects (3 papers), Biofuel production and bioconversion (2 papers), Gene Regulatory Network Analysis (2 papers) and Receptor Mechanisms and Signaling (2 papers). The work is most often cited by research in Aging (31 citations), Molecular Biology (1.2k citations) and Business and International Management (31 citations). Tadas Jakočiūnas has collaborated with scholars based in Denmark, United States and China. Frequent co-authors include Michael K. Jensen, Jay D. Keasling, Lasse Ebdrup Pedersen, Irina Borodina, Scott J. Harrison, Mette Kristensen, Markus J. Herrgård, Ida Bonde, Vratislav Šťovíček and Zongjie Dai. Their work appears in journals such as Proceedings of the National Academy of Sciences, Nucleic Acids Research and Nature Communications.
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.