Pia Mach

1.4k total citations · 2 hit papers
7 papers, 775 citations indexed

About

Pia Mach is a scholar working on Molecular Biology, Infectious Diseases and Ecology, Evolution, Behavior and Systematics. According to data from OpenAlex, Pia Mach has authored 7 papers receiving a total of 775 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Molecular Biology, 1 paper in Infectious Diseases and 1 paper in Ecology, Evolution, Behavior and Systematics. Recurrent topics in Pia Mach's work include RNA Research and Splicing (4 papers), Genomics and Chromatin Dynamics (3 papers) and RNA and protein synthesis mechanisms (2 papers). Pia Mach is often cited by papers focused on RNA Research and Splicing (4 papers), Genomics and Chromatin Dynamics (3 papers) and RNA and protein synthesis mechanisms (2 papers). Pia Mach collaborates with scholars based in Switzerland, Italy and Germany. Pia Mach's co-authors include Luca Giorgetti, Andrew J. Bannister, Rossella Brandi, Samuel C. Robson, Pierre Murat, Tony Kouzarides, Shankar Balasubramanian, Valentina Migliori, Mara D’Onofrio and Yinxiu Zhan and has published in prestigious journals such as Nature, Nature Genetics and Molecular Cell.

In The Last Decade

Pia Mach

7 papers receiving 773 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

METTL1 Promotes let-7 MicroRNA Processing via m7G Methylation

2019 349 citations Luca Pandolfini, Isaia Barbieri et al. Molecular Cell profile →
Nonlinear control of transcription through enhancer–promoter interactions

2022 231 citations Jessica Zuin, Grégory Roth et al. Nature profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Pia Mach Switzerland	5	735	231	82	44	34	7	775
Bryony Graham United Kingdom	7	443 0.6×	225 1.0×	46 0.6×	59 1.3×	13 0.4×	7	481
Fabio Pessina Italy	7	689 0.9×	148 0.6×	53 0.6×	28 0.6×	83 2.4×	11	745
Guang Xu China	6	589 0.8×	270 1.2×	29 0.4×	11 0.3×	20 0.6×	10	643
Angela Chan United States	13	626 0.9×	68 0.3×	98 1.2×	54 1.2×	83 2.4×	16	709
Dror Hollander Israel	9	653 0.9×	168 0.7×	44 0.5×	40 0.9×	16 0.5×	9	719
Cyrille Girard Germany	12	687 0.9×	69 0.3×	30 0.4×	26 0.6×	24 0.7×	14	738
Joseph S. Bowness United Kingdom	7	379 0.5×	159 0.7×	58 0.7×	105 2.4×	8 0.2×	8	424
Tamer Ali Germany	7	376 0.5×	238 1.0×	55 0.7×	37 0.8×	13 0.4×	9	453
Shashank Sathe United States	10	612 0.8×	275 1.2×	30 0.4×	10 0.2×	24 0.7×	12	662
Rahul Maurya United States	4	264 0.4×	122 0.5×	55 0.7×	26 0.6×	19 0.6×	5	317

Countries citing papers authored by Pia Mach

Since Specialization

Citations

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

Fields of papers citing papers by Pia Mach

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Pia Mach

This figure shows the co-authorship network connecting the top 25 collaborators of Pia Mach. A scholar is included among the top collaborators of Pia Mach 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 Pia Mach. Pia Mach is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

7 of 7 papers shown

1.

Mach, Pia & Luca Giorgetti. (2023). Integrative approaches to study enhancer–promoter communication. Current Opinion in Genetics & Development. 80. 102052–102052. 4 indexed citations

2.

Zuin, Jessica, Grégory Roth, Yinxiu Zhan, et al.. (2022). Nonlinear control of transcription through enhancer–promoter interactions. Nature. 604(7906). 571–577. 231 indexed citations breakdown →

3.

Mach, Pia, Pavel Kos, Yinxiu Zhan, et al.. (2022). Cohesin and CTCF control the dynamics of chromosome folding. Nature Genetics. 54(12). 1907–1918. 137 indexed citations

4.

Ng, Tai L., Tae Yeon Yoo, Peter D. Koch, et al.. (2022). High-Content Screening and Computational Prediction Reveal Viral Genes That Suppress the Innate Immune Response. mSystems. 7(2). e0146621–e0146621. 4 indexed citations

5.

Pandolfini, Luca, Isaia Barbieri, Andrew J. Bannister, et al.. (2019). METTL1 Promotes let-7 MicroRNA Processing via m7G Methylation. Molecular Cell. 74(6). 1278–1290.e9. 349 indexed citations breakdown →

6.

Barthels, Fabian, et al.. (2017). Identification of sortase substrates by specificity profiling. Bioorganic & Medicinal Chemistry. 25(18). 5002–5007. 25 indexed citations

7.

Mach, Pia, et al.. (2017). Applying Human ADAR1p110 and ADAR1p150 for Site-Directed RNA Editing—G/C Substitution Stabilizes GuideRNAs against Editing. Genes. 8(1). 34–34. 25 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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