Daniele Raimondi

1.9k total citations
41 papers, 802 citations indexed

About

Daniele Raimondi is a scholar working on Molecular Biology, Genetics and Spectroscopy. According to data from OpenAlex, Daniele Raimondi has authored 41 papers receiving a total of 802 indexed citations (citations by other indexed papers that have themselves been cited), including 31 papers in Molecular Biology, 8 papers in Genetics and 7 papers in Spectroscopy. Recurrent topics in Daniele Raimondi's work include Protein Structure and Dynamics (14 papers), Bioinformatics and Genomic Networks (9 papers) and Genomics and Phylogenetic Studies (9 papers). Daniele Raimondi is often cited by papers focused on Protein Structure and Dynamics (14 papers), Bioinformatics and Genomic Networks (9 papers) and Genomics and Phylogenetic Studies (9 papers). Daniele Raimondi collaborates with scholars based in Belgium, Italy and France. Daniele Raimondi's co-authors include Gabriele Orlando, Wim Vranken, Yves Moreau, Tom Lenaerts, Arne Elofsson, Marcin J. Skwark, Mirco Michel, Andrea Gazzo, Marianne Rooman and Piero Fariselli and has published in prestigious journals such as Nucleic Acids Research, Nature Communications and Bioinformatics.

In The Last Decade

Daniele Raimondi

39 papers receiving 795 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Daniele Raimondi Belgium 16 604 169 110 69 49 41 802
Nadav Brandes Israel 11 823 1.4× 197 1.2× 50 0.5× 102 1.5× 36 0.7× 17 1.1k
Grigory Khimulya Russia 5 978 1.6× 101 0.6× 82 0.7× 129 1.9× 52 1.1× 5 1.1k
Hongjiu Zhang United States 12 311 0.5× 64 0.4× 59 0.5× 85 1.2× 42 0.9× 18 498
Gabriele Orlando Belgium 13 466 0.8× 98 0.6× 79 0.7× 44 0.6× 29 0.6× 25 619
Ian M. Overton United Kingdom 14 733 1.2× 148 0.9× 128 1.2× 40 0.6× 81 1.7× 35 984
Joan Teyra Canada 18 788 1.3× 78 0.5× 145 1.3× 124 1.8× 31 0.6× 33 927
Xiao Fan United States 13 677 1.1× 162 1.0× 148 1.3× 30 0.4× 34 0.7× 22 912
Sikander Hayat Germany 14 702 1.2× 74 0.4× 56 0.5× 24 0.3× 55 1.1× 41 926
András Aszódi Austria 15 694 1.1× 72 0.4× 194 1.8× 67 1.0× 57 1.2× 28 950
Nathalie Malo Canada 7 485 0.8× 107 0.6× 45 0.4× 176 2.6× 39 0.8× 7 797

Countries citing papers authored by Daniele Raimondi

Since Specialization
Citations

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

Fields of papers citing papers by Daniele Raimondi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Daniele Raimondi

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

All Works

20 of 20 papers shown
1.
Jatsenko, Tatjana, Adriaan Vanderstichele, An Coosemans, et al.. (2025). DAGIP: alleviating cell-free DNA sequencing biases with optimal transport. Genome biology. 26(1). 49–49.
2.
Pancotti, Corrado, et al.. (2025). The specification game: rethinking the evaluation of drug response prediction for precision oncology. Journal of Cheminformatics. 17(1). 33–33. 4 indexed citations
3.
Raimondi, Daniele, et al.. (2024). A quantitative benchmark of neural network feature selection methods for detecting nonlinear signals. Scientific Reports. 14(1). 31180–31180. 5 indexed citations
4.
Helmholz, Heike, Daniele Raimondi, Yves Moreau, et al.. (2024). Gene regulatory network analysis identifies MYL1, MDH2, GLS, and TRIM28 as the principal proteins in the response of mesenchymal stem cells to Mg2+ ions. Computational and Structural Biotechnology Journal. 23. 1773–1785. 1 indexed citations
5.
Arany, Ádám, et al.. (2023). Large sample size and nonlinear sparse models outline epistatic effects in inflammatory bowel disease. Genome biology. 24(1). 224–224. 7 indexed citations
6.
Raimondi, Daniele, et al.. (2023). Genome interpretation in a federated learning context allows the multi-center exome-based risk prediction of Crohn’s disease patients. Scientific Reports. 13(1). 19449–19449. 4 indexed citations
7.
Orlando, Gabriele, et al.. (2022). Prediction of Disordered Regions in Proteins with Recurrent Neural Networks and Protein Dynamics. Journal of Molecular Biology. 434(12). 167579–167579. 30 indexed citations
8.
Raimondi, Daniele, Jaak Simm, Ádám Arany, & Yves Moreau. (2021). A novel method for data fusion over entity-relation graphs and its application to protein–protein interaction prediction. Bioinformatics. 37(16). 2275–2281. 12 indexed citations
9.
Raimondi, Daniele, et al.. (2021). Current cancer driver variant predictors learn to recognize driver genes instead of functional variants. BMC Biology. 19(1). 3–3. 15 indexed citations
10.
Raimondi, Daniele, Gabriele Orlando, Emiel Michiels, et al.. (2021). In silico prediction of in vitro protein liquid–liquid phase separation experiments outcomes with multi-head neural attention. Bioinformatics. 37(20). 3473–3479. 18 indexed citations
11.
Orlando, Gabriele, et al.. (2019). Computational identification of prion-like RNA-binding proteins that form liquid phase-separated condensates. Bioinformatics. 35(22). 4617–4623. 44 indexed citations
12.
Raimondi, Daniele, Gabriele Orlando, Wim Vranken, & Yves Moreau. (2019). Exploring the limitations of biophysical propensity scales coupled with machine learning for protein sequence analysis. Scientific Reports. 9(1). 16932–16932. 20 indexed citations
13.
Orlando, Gabriele, Daniele Raimondi, & Wim Vranken. (2019). Auto-encoding NMR chemical shifts from their native vector space to a residue-level biophysical index. Nature Communications. 10(1). 2511–2511. 6 indexed citations
14.
Raimondi, Daniele, Gabriele Orlando, Yves Moreau, & Wim Vranken. (2018). Ultra-fast global homology detection with Discrete Cosine Transform and Dynamic Time Warping. Bioinformatics. 34(18). 3118–3125. 13 indexed citations
15.
Raimondi, Daniele, Gabriele Orlando, Francesco Tabaro, et al.. (2018). Large-scale in-silico statistical mutagenesis analysis sheds light on the deleteriousness landscape of the human proteome. Scientific Reports. 8(1). 16980–16980. 8 indexed citations
16.
Raimondi, Daniele, Gabriele Orlando, Rita Pancsa, Taushif Khan, & Wim Vranken. (2017). Exploring the Sequence-based Prediction of Folding Initiation Sites in Proteins. Scientific Reports. 7(1). 8826–8826. 32 indexed citations
17.
Gazzo, Andrea, Daniele Raimondi, Yves Moreau, et al.. (2017). Understanding mutational effects in digenic diseases. Nucleic Acids Research. 45(15). e140–e140. 42 indexed citations
18.
Orlando, Gabriele, Daniele Raimondi, & Wim Vranken. (2016). Observation selection bias in contact prediction and its implications for structural bioinformatics. Scientific Reports. 6(1). 18 indexed citations
19.
Pancsa, Rita, Daniele Raimondi, Elisa Cilia, & Wim Vranken. (2016). Early Folding Events, Local Interactions, and Conservation of Protein Backbone Rigidity. Biophysical Journal. 110(3). 572–583. 21 indexed citations
20.
Skwark, Marcin J., Daniele Raimondi, Mirco Michel, & Arne Elofsson. (2014). Improved Contact Predictions Using the Recognition of Protein Like Contact Patterns. PLoS Computational Biology. 10(11). e1003889–e1003889. 108 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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