M. Macchi

567 total citations
9 papers, 496 citations indexed

About

M. Macchi is a scholar working on Molecular Biology, Public Health, Environmental and Occupational Health and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, M. Macchi has authored 9 papers receiving a total of 496 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Molecular Biology, 2 papers in Public Health, Environmental and Occupational Health and 2 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in M. Macchi's work include Genomics and Chromatin Dynamics (4 papers), RNA Research and Splicing (2 papers) and Glycosylation and Glycoproteins Research (1 paper). M. Macchi is often cited by papers focused on Genomics and Chromatin Dynamics (4 papers), RNA Research and Splicing (2 papers) and Glycosylation and Glycoproteins Research (1 paper). M. Macchi collaborates with scholars based in France, Luxembourg and Australia. M. Macchi's co-authors include Ricardo Rosales, J.H. Xiao, Irwin Davidson, Marc Vigneron, Pierre Chambon, Pierre Chambon, Adrien Staub, Frank Ruffenach, Dominique Ferrandon and Bohdan Wasylyk and has published in prestigious journals such as Nucleic Acids Research, Genes & Development and The EMBO Journal.

In The Last Decade

M. Macchi

8 papers receiving 470 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
M. Macchi France	6	338	122	89	76	59	9	496
Diane Giannola United States	11	345 1.0×	180 1.5×	121 1.4×	82 1.1×	76 1.3×	18	665
L D Vales United States	11	505 1.5×	85 0.7×	153 1.7×	140 1.8×	72 1.2×	13	661
Leyi Shen United States	8	419 1.2×	211 1.7×	89 1.0×	98 1.3×	84 1.4×	15	620
Jun Hasegawa Japan	7	1.0k 3.0×	121 1.0×	86 1.0×	77 1.0×	60 1.0×	8	1.2k
D. Rahman United Kingdom	5	316 0.9×	181 1.5×	93 1.0×	76 1.0×	28 0.5×	5	546
Julita Ramírez United States	14	422 1.2×	244 2.0×	92 1.0×	59 0.8×	45 0.8×	15	672
Lola Margulies United States	12	228 0.7×	108 0.9×	73 0.8×	140 1.8×	58 1.0×	24	431
Ursula Kurzik‐Dumke Germany	13	386 1.1×	132 1.1×	59 0.7×	79 1.0×	94 1.6×	21	603
U Bregula Sweden	7	219 0.6×	49 0.4×	76 0.9×	102 1.3×	67 1.1×	14	365
Senthilkumar Ramamoorthy Germany	14	471 1.4×	215 1.8×	66 0.7×	89 1.2×	52 0.9×	26	705

Countries citing papers authored by M. Macchi

Since Specialization

Citations

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

Fields of papers citing papers by M. Macchi

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of M. Macchi

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

All Works

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9 of 9 papers shown

Macchi, M., Anaı̈s Baudot, Frada Burstein, et al.. (2025). Recommendations for Successful Development and Implementation of Digital Health Technology Tools. Journal of Medical Internet Research. 27. e56747–e56747. 1 indexed citations

Soudy, Mohamed, et al.. (2024). Bioinformatics approaches for studying molecular sex differences in complex diseases. Briefings in Bioinformatics. 25(6).

Macchi, M., et al.. (2000). Conocimientos, actitudes y prácticas acerca del Dengue en un barrio de Asunción. 27(2). 16–23. 2 indexed citations

Ferretti, J.L., Vivían Scheinsohn, M. Macchi, & J. R. Zanchetta. (1992). Biological determination of diaphyseal thickness according to mechanical quality of bone material in several vertebrate species. Bone and Mineral. 17. 133–133. 5 indexed citations

Macchi, M., Jean‐Marc Bornert, Irwin Davidson, et al.. (1989). The SV40 TC-II(kappa B) enhanson binds ubiquitous and cell type specifically inducible nuclear proteins from lymphoid and non-lymphoid cell lines.. The EMBO Journal. 8(13). 4215–4227. 58 indexed citations

Macchi, M., et al.. (1988). Mutational analysis of the contribution of sequence motifs within the IgH enhancer to tissue specific transcriptional activation. Nucleic Acids Research. 16(13). 6085–6096. 46 indexed citations

Rosales, Ricardo, Marc Vigneron, M. Macchi, et al.. (1987). In vitro binding of cell-specific and ubiquitous nuclear proteins to the octamer motif of the SV40 enhancer and related motifs present in other promoters and enhancers.. The EMBO Journal. 6(10). 3015–3025. 138 indexed citations

Xiao, J.H., Irwin Davidson, Dominique Ferrandon, et al.. (1987). One cell-specific and three ubiquitous nuclear proteins bind in vitro to overlapping motifs in the domain B1 of the SV40 enhancer.. The EMBO Journal. 6(10). 3005–3013. 124 indexed citations

Xiao, J.H., Irwin Davidson, M. Macchi, et al.. (1987). In vitro binding of several cell-specific and ubiquitous nuclear proteins to the GT-I motif of the SV40 enhancer.. Genes & Development. 1(8). 794–807. 122 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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