Dario Ghersi

1.8k total citations
53 papers, 1.2k citations indexed

About

Dario Ghersi is a scholar working on Molecular Biology, Immunology and Oncology. According to data from OpenAlex, Dario Ghersi has authored 53 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 33 papers in Molecular Biology, 17 papers in Immunology and 14 papers in Oncology. Recurrent topics in Dario Ghersi's work include T-cell and B-cell Immunology (12 papers), Immune Cell Function and Interaction (11 papers) and Protein Structure and Dynamics (7 papers). Dario Ghersi is often cited by papers focused on T-cell and B-cell Immunology (12 papers), Immune Cell Function and Interaction (11 papers) and Protein Structure and Dynamics (7 papers). Dario Ghersi collaborates with scholars based in United States, Italy and Germany. Dario Ghersi's co-authors include Roberto Sánchez, Mona Singh, Liisa K. Selin, Raymond M. Welsh, Franco Celada, Claudia Calcagno, Roberto Puzone, Anna Gil, Sushil Kumar and Surinder K. Batra and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nucleic Acids Research and Bioinformatics.

In The Last Decade

Dario Ghersi

51 papers receiving 1.2k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Dario Ghersi United States 19 658 339 243 232 94 53 1.2k
Paola Lo Surdo Italy 19 1.2k 1.9× 281 0.8× 103 0.4× 308 1.3× 111 1.2× 38 1.9k
Özlem Demir United States 22 1.1k 1.7× 125 0.4× 250 1.0× 282 1.2× 93 1.0× 74 1.8k
Kanin Wichapong Netherlands 18 468 0.7× 339 1.0× 157 0.6× 75 0.3× 56 0.6× 55 988
S. Betzi France 22 951 1.4× 110 0.3× 342 1.4× 215 0.9× 48 0.5× 38 1.5k
Mark Ammirati United States 14 598 0.9× 121 0.4× 121 0.5× 169 0.7× 134 1.4× 20 1.2k
Vincent Mikol France 23 1.1k 1.6× 230 0.7× 133 0.5× 261 1.1× 42 0.4× 37 1.6k
Kristen M. Varney United States 22 1.4k 2.1× 234 0.7× 144 0.6× 130 0.6× 240 2.6× 59 1.7k
Michael J. Miley United States 21 659 1.0× 571 1.7× 66 0.3× 208 0.9× 30 0.3× 33 1.5k
Jishnu Das United States 23 1.4k 2.1× 324 1.0× 187 0.8× 130 0.6× 122 1.3× 80 2.0k
Dinler A. Antunes United States 17 644 1.0× 319 0.9× 164 0.7× 116 0.5× 17 0.2× 48 945

Countries citing papers authored by Dario Ghersi

Since Specialization
Citations

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

Fields of papers citing papers by Dario Ghersi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Dario Ghersi

This figure shows the co-authorship network connecting the top 25 collaborators of Dario Ghersi. A scholar is included among the top collaborators of Dario Ghersi 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 Dario Ghersi. Dario Ghersi 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.
Ghersi, Dario, et al.. (2025). Toward equitable major histocompatibility complex binding predictions. Proceedings of the National Academy of Sciences. 122(8). e2405106122–e2405106122.
2.
Ghersi, Dario, et al.. (2025). VIPER: Virus Inhibition Via Peptide Engineering and Receptor Mimicry. Journal of Computational Biology. 32(4). 362–373. 1 indexed citations
3.
Gil, Anna, et al.. (2025). TRain: T-cell receptor automated immunoinformatics. BMC Bioinformatics. 26(1). 76–76.
4.
Atri, Pranita, Ashu Shah, Satyanarayana Rachagani, et al.. (2024). Connectivity mapping-based identification of pharmacological inhibitor targeting HDAC6 in aggressive pancreatic ductal adenocarcinoma. npj Precision Oncology. 8(1). 66–66. 2 indexed citations
5.
Atri, Pranita, Ashu Shah, Jesse L. Cox, et al.. (2023). A Novel HOXA10 -Associated 5-Gene–Based Prognostic Signature for Stratification of Short-term Survivors of Pancreatic Ductal Adenocarcinoma. Clinical Cancer Research. 29(18). 3759–3770. 7 indexed citations
6.
Gil, Anna, et al.. (2021). SwarmTCR: a computational approach to predict the specificity of T cell receptors. BMC Bioinformatics. 22(1). 422–422. 17 indexed citations
7.
Cannon, Andrew, Christopher M. Thompson, H. Carlo Maurer, et al.. (2020). CXCR3 and Cognate Ligands are Associated with Immune Cell Alteration and Aggressiveness of Pancreatic Ductal Adenocarcinoma. Clinical Cancer Research. 26(22). 6051–6063. 15 indexed citations
8.
Gautam, Shailendra K., et al.. (2020). MUCIN-4 (MUC4) is a novel tumor antigen in pancreatic cancer immunotherapy. Seminars in Immunology. 47. 101391–101391. 46 indexed citations
9.
Hale, Matthew L., Ishwor Thapa, & Dario Ghersi. (2019). FunSet: an open-source software and web server for performing and displaying Gene Ontology enrichment analysis. BMC Bioinformatics. 20(1). 359–359. 22 indexed citations
10.
Castiglione, Filippo, Dario Ghersi, & Franco Celada. (2019). Computer Modeling of Clonal Dominance: Memory-Anti-Naïve and Its Curbing by Attrition. Frontiers in Immunology. 10. 1513–1513. 4 indexed citations
11.
Kumar, Sushil, et al.. (2019). Uncovering and characterizing splice variants associated with survival in lung cancer patients. PLoS Computational Biology. 15(10). e1007469–e1007469. 5 indexed citations
12.
Munro, Daniel, Dario Ghersi, & Mona Singh. (2018). Two critical positions in zinc finger domains are heavily mutated in three human cancer types. PLoS Computational Biology. 14(6). e1006290–e1006290. 31 indexed citations
13.
Gil, Anna, et al.. (2017). Broad TCR repertoire and diverse structural solutions for recognition of an immunodominant CD8+ T cell epitope. Nature Structural & Molecular Biology. 24(4). 395–406. 64 indexed citations
14.
Bastola, Dhundy R., et al.. (2016). Forecasting the Spread of Mosquito-Borne Disease using Publicly Accessible Data: A Case Study in Chikungunya.. PubMed. 2016. 431–440. 3 indexed citations
15.
Ghersi, Dario, et al.. (2016). Building towards precision medicine: empowering medical professionals for the next revolution. BMC Medical Genomics. 9(1). 23–23. 66 indexed citations
16.
Ghersi, Dario & Mona Singh. (2013). Disentangling function from topology to infer the network properties of disease genes. BMC Systems Biology. 7(1). 5–5. 15 indexed citations
17.
Chakravarty, Suvobrata, Dario Ghersi, & Roberto Sánchez. (2011). Systematic assessment of accuracy of comparative model of proteins belonging to different structural fold classes. Journal of Molecular Modeling. 17(11). 2831–2837. 2 indexed citations
18.
Ghersi, Dario, et al.. (2009). SITEHOUND-web: a server for ligand binding site identification in protein structures. Nucleic Acids Research. 37(Web Server). W413–W416. 167 indexed citations
19.
20.
Ghersi, Dario & Roberto Sánchez. (2008). Improving accuracy and efficiency of blind protein‐ligand docking by focusing on predicted binding sites. Proteins Structure Function and Bioinformatics. 74(2). 417–424. 85 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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