Aurélien Naldi

5.6k citations
42 papers · 2.5k indexed · 1 hit paper · h-index 18

Aurélien Naldi

41 papers receiving 2.4k citations

Hit Papers

GOATOOLS: A Python library for Gene Ontology analyses7242018202620202023200400600

Peers

Aurélien Naldi
Comparison fields: 5 of 135
  • Molecular Biology 1.9k
  • Biophysics 106
  • Aging 32
  • Computational Theory and Mathematics 254
  • Modeling and Simulation 58
Replace Jerome T. Mettetal with:
Jerome T. Mettetal United States
Eric Batchelor United States
Luis Mendoza Mexico
Christoph Wierling Germany
Jörg Stelling Switzerland
Yukiko Matsuoka Japan
Sandeep Krishna India
Edda Klipp Germany
Kanae Oda Japan
Élisabeth Rémy France
Aurélien Naldi relative to Jerome T. Mettetal United States Jerome T. Mettetal's profile →
Citations per field
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Jerome T. Mettetal · 1×
Citations per year

Countries citing papers authored by Aurélien Naldi

Since Specialization
Citations

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

Fields of papers citing papers by Aurélien Naldi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

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

All Works

20 of 20 papers shown
#Work
1 20250
2 202312
3 20226
4 20213
5 20217
6 202013
7 202012
8 201914
9 20196
10 201912
11
GOATOOLS: A Python library for Gene Ontology analysesbreakdown →
2018724
12 201863
13 20187
14 201713
15
Dynamical modeling and analysis of large cellular regulatory networks.: D. Bérenguier, C. Chaouiya, P. T. Monteiro, A. Naldi, E. Remy, D. Thieffry and L. Tichit
20132
16 201317
17 201198
18 201089
19 200943
20 2009112

About Aurélien Naldi

Aurélien Naldi is a scholar working on Biophysics, Molecular Biology, Immunology, Modeling and Simulation and Computational Theory and Mathematics, having authored 42 papers that have together received 2.5k indexed citations. Recurring topics across this work include Gene Regulatory Network Analysis (26 papers), Bioinformatics and Genomic Networks (23 papers), Microbial Metabolic Engineering and Bioproduction (13 papers), Receptor Mechanisms and Signaling (4 papers), Single-cell and spatial transcriptomics (3 papers), Protein Structure and Dynamics (2 papers), Computational Drug Discovery Methods (2 papers) and Immune Cell Function and Interaction (2 papers). The work is most often cited by research in Molecular Biology (1.9k citations), Biophysics (106 citations), Aging (32 citations), Computational Theory and Mathematics (254 citations) and Modeling and Simulation (58 citations). Aurélien Naldi has collaborated with scholars based in France, Portugal and Switzerland. Frequent co-authors include Denis Thieffry, Claudine Chaouiya, Adrien Fauré, Chris Mungall, Olga Botvinnik, Christophe Dessimoz, Brent S. Pedersen, Will Dampier, Jeffrey M. Yunes and Fidel Ramírez. Their work appears in journals such as Frontiers in Physiology, Bioinformatics, Biosystems, Scientific Reports and Natural Computing.

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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