Barbara Di Camillo

6.6k total citations · 1 hit paper
132 papers, 2.8k citations indexed

About

Barbara Di Camillo is a scholar working on Molecular Biology, Endocrinology, Diabetes and Metabolism and Epidemiology. According to data from OpenAlex, Barbara Di Camillo has authored 132 papers receiving a total of 2.8k indexed citations (citations by other indexed papers that have themselves been cited), including 75 papers in Molecular Biology, 23 papers in Endocrinology, Diabetes and Metabolism and 19 papers in Epidemiology. Recurrent topics in Barbara Di Camillo's work include Bioinformatics and Genomic Networks (18 papers), Diabetes Treatment and Management (14 papers) and Genomics and Phylogenetic Studies (14 papers). Barbara Di Camillo is often cited by papers focused on Bioinformatics and Genomic Networks (18 papers), Diabetes Treatment and Management (14 papers) and Genomics and Phylogenetic Studies (14 papers). Barbara Di Camillo collaborates with scholars based in Italy, United States and Austria. Barbara Di Camillo's co-authors include Francesca Finotello, Giacomo Baruzzo, Enrico Longato, Gianna Toffolo, Claudio Cobelli, Alessandra Dal Molin, Martina Vettoretti, Angelo Avogaro, Francesco Sambo and Giovanni Sparacino and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Blood and Bioinformatics.

In The Last Decade

Barbara Di Camillo

123 papers receiving 2.8k citations

Hit Papers

align trajectories sort by overperformance

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.

Challenges and opportunities for digital twins in precision medicine from a complex systems perspective

2025 34 citations Manlio De Domenico, Guido Caldarelli et al. npj Digital Medicine profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Barbara Di Camillo Italy	30	1.4k	402	345	299	245	132	2.8k
Kunlun He China	33	1.6k 1.1×	215 0.5×	281 0.8×	675 2.3×	390 1.6×	236	4.1k
Tao Chen China	28	970 0.7×	408 1.0×	291 0.8×	354 1.2×	633 2.6×	242	3.3k
Ping Zeng China	31	984 0.7×	188 0.5×	290 0.8×	336 1.1×	166 0.7×	168	3.2k
Bobbie‐Jo Webb‐Robertson United States	32	2.2k 1.5×	169 0.4×	163 0.5×	230 0.8×	295 1.2×	138	3.9k
Cheng Chang China	38	1.2k 0.8×	135 0.3×	282 0.8×	210 0.7×	223 0.9×	157	3.8k
Yu Ma China	34	814 0.6×	248 0.6×	211 0.6×	313 1.0×	166 0.7×	171	3.1k
Weiliang Qiu United States	40	1.5k 1.1×	152 0.4×	513 1.5×	296 1.0×	426 1.7×	115	4.2k
Yan Cai China	39	1.7k 1.2×	258 0.6×	555 1.6×	1.0k 3.5×	433 1.8×	157	4.8k
Adam Maciejewski Poland	14	2.2k 1.6×	93 0.2×	283 0.8×	208 0.7×	342 1.4×	62	4.0k
Ping Xu China	24	977 0.7×	152 0.4×	115 0.3×	448 1.5×	314 1.3×	64	2.5k

Countries citing papers authored by Barbara Di Camillo

Since Specialization

Citations

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

Fields of papers citing papers by Barbara Di Camillo

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Barbara Di Camillo. 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 Barbara Di Camillo. The network helps show where Barbara Di Camillo may publish in the future.

Co-authorship network of co-authors of Barbara Di Camillo

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

All Works

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20 of 20 papers shown

Domenico, Manlio De, et al.. (2025). Challenges and opportunities for digital twins in precision medicine from a complex systems perspective. npj Digital Medicine. 8(1). 37–37. 34 indexed citations breakdown →

Baruzzo, Giacomo, et al.. (2025). Differential cellular communication inference framework for large-scale single-cell RNA-sequencing data. NAR Genomics and Bioinformatics. 7(2). lqaf084–lqaf084.

Nagai, James S., Ettore Mosca, Ivan G. Costa, et al.. (2025). Advances and challenges in cell–cell communication inference: a comprehensive review of tools, resources, and future directions. Briefings in Bioinformatics. 26(3). 3 indexed citations

Camillo, Barbara Di, et al.. (2025). The impact of clinical history on the predictive performance of machine learning and deep learning models for renal complications of diabetes. Computer Methods and Programs in Biomedicine. 268. 108812–108812.

Tavazzi, Erica, Martina Vettoretti, Roberto Gatta, et al.. (2024). DYNAMITE: Integrating Archetypal Analysis and Process Mining for Interpretable Disease Progression Modelling. IEEE Journal of Biomedical and Health Informatics. 28(12). 7553–7564. 2 indexed citations

Trojani, Alessandra, Alessandro Beghini, Antonino Greco, et al.. (2024). Mutational Landscape of Bone Marrow CD19 and CD138 Cells in Waldenström Macroglobulinemia (WM) and IgM Monoclonal Gammopathy of Undetermined Significance (IgM MGUS). Cancer Medicine. 13(24). e70525–e70525.

Tavazzi, Erica, Enrico Longato, Umberto Manera, et al.. (2024). Predicting clinical events characterizing the progression of amyotrophic lateral sclerosis via machine learning approaches using routine visits data: a feasibility study. BMC Medical Informatics and Decision Making. 24(S4). 318–318. 2 indexed citations

Baruzzo, Giacomo, Agnese Serafini, Francesca Finotello, et al.. (2023). Role of the Extracytoplasmic Function Sigma Factor SigE in the Stringent Response of Mycobacterium tuberculosis. Microbiology Spectrum. 11(2). e0294422–e0294422. 10 indexed citations

Baruzzo, Giacomo, Piergiorgio Alotto, Noel F.C.C. de Miranda, et al.. (2022). MAST: a hybrid Multi-Agent Spatio-Temporal model of tumor microenvironment informed using a data-driven approach. Bioinformatics Advances. 2(1). vbac092–vbac092. 6 indexed citations

10.

Patuzzi, Ilaria, Tommaso Furlanello, Barbara Di Camillo, et al.. (2022). Machine Learning and Canine Chronic Enteropathies: A New Approach to Investigate FMT Effects. Veterinary Sciences. 9(9). 502–502. 13 indexed citations

11.

Camillo, Barbara Di, et al.. (2022). Machine learning–based feature selection to search stable microbial biomarkers: application to inflammatory bowel disease. GigaScience. 12. 13 indexed citations

12.

Longato, Enrico, Mario Luca Morieri, Giovanni Sparacino, et al.. (2022). Time-series analysis of multidimensional clinical-laboratory data by dynamic Bayesian networks reveals trajectories of COVID-19 outcomes. Computer Methods and Programs in Biomedicine. 221. 106873–106873. 5 indexed citations

13.

Longato, Enrico, Barbara Di Camillo, Giovanni Sparacino, et al.. (2020). Cardiovascular effectiveness of human-based vs. exendin-based glucagon like peptide-1 receptor agonists: a retrospective study in patients with type 2 diabetes. European Journal of Preventive Cardiology. 28(1). 22–29. 13 indexed citations

14.

Baruzzo, Giacomo, et al.. (2020). Modeling Microbial Community Networks: Methods and Tools. Current Genomics. 22(4). 267–290. 4 indexed citations

15.

Ciccarese, Francesco, Angela Grassi, Lorenza Pasqualini, et al.. (2020). Genetic perturbation of IFN-α transcriptional modulators in human endothelial cells uncovers pivotal regulators of angiogenesis. Computational and Structural Biotechnology Journal. 18. 3977–3986. 7 indexed citations

16.

Arğa, Kazım Yalçın, Duygu Dikicioǧlu, Serpil Eraslan, et al.. (2019). Identification of Novel Components of Target-of-Rapamycin Signaling Pathway by Network-Based Multi-Omics Integrative Analysis. OMICS A Journal of Integrative Biology. 23(5). 274–284. 1 indexed citations

17.

Grisan, Enrico, Alessandro Zandonà, & Barbara Di Camillo. (2019). Deep Convolutional Neural Network for Survival Estimation of Amyotrophic Lateral Sclerosis patients. Research Open (London South Bank University). 1 indexed citations

18.

Etna, Marilena P., Alessandro Sinigaglia, Angela Grassi, et al.. (2018). Mycobacterium tuberculosis-induced miR-155 subverts autophagy by targeting ATG3 in human dendritic cells. PLoS Pathogens. 14(1). e1006790–e1006790. 104 indexed citations

19.

Marini, Simone, Nicola Barbarini, Francesco Sambo, et al.. (2015). A Dynamic Bayesian Network model for long-term simulation of clinical complications in type 1 diabetes. Journal of Biomedical Informatics. 57. 369–376. 38 indexed citations

20.

Falda, Marco, Stefano Toppo, Enrico Lavezzo, et al.. (2012). Argot2: a large scale function prediction tool relying on semantic similarity of weighted Gene Ontology terms. BMC Bioinformatics. 13(S4). S14–S14. 109 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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