Ivan Labat

1.6k citations
19 papers · 1.2k · h-index 14

Impact in

    • Advanced biosensing and bioanalysis techniques
    • Genomics and Phylogenetic Studies
    • Gene expression and cancer classification
    • RNA and protein synthesis mechanisms
    • Molecular Biology Techniques and Applications
    • DNA and Nucleic Acid Chemistry
    • CRISPR and Genetic Engineering
  • Aging top 10%

Papers in

    • RNA and protein synthesis mechanisms 6
    • Genomics and Phylogenetic Studies 4
    • Advanced biosensing and bioanalysis techniques 4
    • CRISPR and Genetic Engineering 4
    • Pluripotent Stem Cells Research 3
    • Gene expression and cancer classification 2
    • Telomeres, Telomerase, and Senescence 2

Ivan Labat

19 papers receiving 1.1k citations

Peers

Ivan Labat
Comparison fields: 5 of 122
  • Molecular Biology 902
  • Aging 23
  • Computational Theory and Mathematics 106
  • Cancer Research 58
  • Genetics 111
Replace Becky Drees with:
Becky Drees United States
Thuy D. Vo United States
Mehdi Sadeghi Iran
Michael D. Ward United States
Guohui Chuai China
Mineo Morohashi Japan
Luca Parca Italy
Ashwini Patil Japan
Magali Michaut Netherlands
Rohith Srivas United States
Ivan Labat relative to Becky Drees United States Becky Drees's profile →
Citations per field
00.5×3.6×
Becky Drees · 1×
Citations per year

Countries citing papers authored by Ivan Labat

Since Specialization
Citations

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

Fields of papers citing papers by Ivan Labat

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

19 of 19 papers shown
#Work
1 1989281
2 1993183
3 1998138
4 2016114
5 201699
6 199173
7 199666
8 199053
9 199235
10 201931
11 201728
12 201122
13 199120
14 202115
15 20199
16 19944
17 19942
18 19932
19 20031

About Ivan Labat

Ivan Labat is a scholar working on Molecular Biology, Physiology, Ecology, Plant Science and Surgery, having authored 19 papers that have together received 1.2k indexed citations. Recurring topics across this work include RNA and protein synthesis mechanisms (6 papers), Genomics and Phylogenetic Studies (4 papers), Advanced biosensing and bioanalysis techniques (4 papers), CRISPR and Genetic Engineering (4 papers), Pluripotent Stem Cells Research (3 papers), Gene expression and cancer classification (2 papers), Telomeres, Telomerase, and Senescence (2 papers) and Chromosomal and Genetic Variations (2 papers). The work is most often cited by research in Molecular Biology (902 citations), Aging (23 citations), Computational Theory and Mathematics (106 citations), Cancer Research (58 citations) and Genetics (111 citations). Ivan Labat has collaborated with scholars based in United States, Russia and Switzerland. Frequent co-authors include Radomir Crkvenjakov, Ivan Brukner, Snezana Drmanac, Radoje Drmanac, Žaklina Strezoska, Tatjana Paunesku, John D. Burczak, Carl J. Schmidt, Alexander Aliper and Artem V. Artemov. Their work appears in journals such as Genomics, Nature Communications, DNA and Cell Biology, Stem Cell Research & Therapy and Regenerative Medicine.

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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