Marika Miot

595 total citations
12 papers, 463 citations indexed

About

Marika Miot is a scholar working on Molecular Biology, Genetics and Cell Biology. According to data from OpenAlex, Marika Miot has authored 12 papers receiving a total of 463 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Molecular Biology, 6 papers in Genetics and 3 papers in Cell Biology. Recurrent topics in Marika Miot's work include Bacterial Genetics and Biotechnology (6 papers), Heat shock proteins research (5 papers) and RNA and protein synthesis mechanisms (4 papers). Marika Miot is often cited by papers focused on Bacterial Genetics and Biotechnology (6 papers), Heat shock proteins research (5 papers) and RNA and protein synthesis mechanisms (4 papers). Marika Miot collaborates with scholars based in France and United States. Marika Miot's co-authors include Jean‐Michel Betton, Michael Reidy, Daniel C. Masison, Shannon M. Doyle, Joel R. Hoskins, Sue Wickner, Nathalie Sassoon, Jean‐Philippe Arié, Maria del Carmen Vitery and Olivier Genest and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nature Communications and Journal of Molecular Biology.

In The Last Decade

Marika Miot

12 papers receiving 460 citations

Peers

Marika Miot

MB

GENET

MC

CB

ECOLO

Željka Maglica Switzerland

Nathalie Sassoon France

Chi T. Wong United Kingdom

Victoria A. Doronina United Kingdom

R. Scott Houliston Canada

Anita Lehner Austria

Yohei Hizukuri Japan

Hansjörg Götzke Sweden

Yo Kikuchi Japan

Sonja Hasenbein Germany

Željka Maglica Switzerland

Marika Miot

537 ×1.3

MB

127 ×1.3

GENET

147 ×1.6

MC

140 ×2.0

CB

36 ×1.0

ECOLO

Citations per year, relative to Marika Miot Marika Miot (= 1×) peers Željka Maglica

Countries citing papers authored by Marika Miot

Since Specialization

Citations

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

Fields of papers citing papers by Marika Miot

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Marika Miot

This figure shows the co-authorship network connecting the top 25 collaborators of Marika Miot. A scholar is included among the top collaborators of Marika Miot based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Marika Miot. Marika Miot is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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12 of 12 papers shown

1.

Castella, Sandrine, et al.. (2024). Unraveling the interplay between PKA inhibition and Cdk1 activation during oocyte meiotic maturation. Cell Reports. 43(2). 113782–113782. 6 indexed citations

2.

Daldello, Enrico Maria, et al.. (2021). The M-phase regulatory phosphatase PP2A-B55δ opposes protein kinase A on Arpp19 to initiate meiotic division. Nature Communications. 12(1). 5 indexed citations

3.

Miot, Marika, et al.. (2017). Substrate Discrimination by ClpB and Hsp104. Frontiers in Molecular Biosciences. 4. 36–36. 9 indexed citations

4.

Doyle, Shannon M., Shankar Shastry, Andrea N. Kravats, et al.. (2014). Interplay between E. coli DnaK, ClpB and GrpE during Protein Disaggregation. Journal of Molecular Biology. 427(2). 312–327. 48 indexed citations

5.

Reidy, Michael, Marika Miot, & Daniel C. Masison. (2012). Prokaryotic Chaperones Support Yeast Prions and Thermotolerance and Define Disaggregation Machinery Interactions. Genetics. 192(1). 185–193. 58 indexed citations

6.

Benaroudj, Nadia, Bertrand Raynal, Marika Miot, & M. Ortiz-Lombardı́a. (2011). Assembly and proteolytic processing of mycobacterial ClpP1 and ClpP2. BMC Biochemistry. 12(1). 61–61. 22 indexed citations

7.

Miot, Marika, Michael Reidy, Shannon M. Doyle, et al.. (2011). Species-specific collaboration of heat shock proteins (Hsp) 70 and 100 in thermotolerance and protein disaggregation. Proceedings of the National Academy of Sciences. 108(17). 6915–6920. 119 indexed citations

8.

Miot, Marika & Jean‐Michel Betton. (2010). Reconstitution of the Cpx signaling system from cell-free synthesized proteins. New Biotechnology. 28(3). 277–281. 14 indexed citations

9.

Ratelade, Julien, Marika Miot, Emmett J. Johnson, et al.. (2009). Production of Recombinant Proteins in thelon-Deficient BL21(DE3) Strain ofEscherichia coliin the Absence of the DnaK Chaperone. Applied and Environmental Microbiology. 75(11). 3803–3807. 26 indexed citations

10.

Miot, Marika & Jean‐Michel Betton. (2007). Optimization of the inefficient translation initiation region of the cpxP gene from Escherichia coli. Protein Science. 16(11). 2445–2453. 8 indexed citations

11.

Arié, Jean‐Philippe, Marika Miot, Nathalie Sassoon, & Jean‐Michel Betton. (2006). Formation of active inclusion bodies in the periplasm of Escherichia coli. Molecular Microbiology. 62(2). 427–437. 70 indexed citations

12.

Miot, Marika & Jean‐Michel Betton. (2004). Protein quality control in the bacterial periplasm. Microbial Cell Factories. 3(1). 4–4. 78 indexed citations

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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