Vincenzo Lagani

2.8k total citations
72 papers, 1.2k citations indexed

About

Vincenzo Lagani is a scholar working on Molecular Biology, Artificial Intelligence and Physiology. According to data from OpenAlex, Vincenzo Lagani has authored 72 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 32 papers in Molecular Biology, 10 papers in Artificial Intelligence and 8 papers in Physiology. Recurrent topics in Vincenzo Lagani's work include Gene expression and cancer classification (15 papers), Bioinformatics and Genomic Networks (11 papers) and Single-cell and spatial transcriptomics (8 papers). Vincenzo Lagani is often cited by papers focused on Gene expression and cancer classification (15 papers), Bioinformatics and Genomic Networks (11 papers) and Single-cell and spatial transcriptomics (8 papers). Vincenzo Lagani collaborates with scholars based in Greece, Georgia and Saudi Arabia. Vincenzo Lagani's co-authors include Ioannis Tsamardinos, Alberto Montesanto, Michail Tsagris, Giuseppe Passarino, Franco Chiarugi, Serena Dato, Kaare Christensen, G Dolce, Loris Pignolo and Alessio Farcomeni and has published in prestigious journals such as Nucleic Acids Research, Journal of Clinical Oncology and SHILAP Revista de lepidopterología.

In The Last Decade

Vincenzo Lagani

65 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
Vincenzo Lagani Greece 22 430 205 138 112 104 72 1.2k
Andreas Schuppert Germany 21 668 1.6× 184 0.9× 82 0.6× 79 0.7× 113 1.1× 82 1.6k
Md Habibur Rahman Bangladesh 21 369 0.9× 180 0.9× 66 0.5× 83 0.7× 61 0.6× 111 1.2k
Kuljeet Singh India 18 329 0.8× 186 0.9× 101 0.7× 178 1.6× 68 0.7× 59 1.4k
Liqin Wang United States 24 324 0.8× 224 1.1× 161 1.2× 43 0.4× 112 1.1× 84 1.5k
Daniel Ziemek United States 18 498 1.2× 127 0.6× 154 1.1× 134 1.2× 62 0.6× 34 1.2k
Jason M. Laramie United States 18 737 1.7× 136 0.7× 170 1.2× 164 1.5× 248 2.4× 34 2.3k
Nophar Geifman United Kingdom 17 325 0.8× 76 0.4× 174 1.3× 56 0.5× 87 0.8× 66 1.0k
Wei‐Yi Cheng United States 16 636 1.5× 98 0.5× 52 0.4× 147 1.3× 113 1.1× 47 1.4k
George C. Linderman United States 11 443 1.0× 92 0.4× 87 0.6× 33 0.3× 130 1.3× 22 1.7k
Lan Yu China 28 729 1.7× 165 0.8× 238 1.7× 406 3.6× 194 1.9× 107 2.1k

Countries citing papers authored by Vincenzo Lagani

Since Specialization
Citations

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

Fields of papers citing papers by Vincenzo Lagani

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Vincenzo Lagani

This figure shows the co-authorship network connecting the top 25 collaborators of Vincenzo Lagani. A scholar is included among the top collaborators of Vincenzo Lagani 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 Vincenzo Lagani. Vincenzo Lagani 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.
Gamkrelidze, Georgi, et al.. (2025). Dose-Dependent Effects of Myo-Inositol on Kainic Acid-Induced Epilepsy: Electrophysiological, Behavioral, Transcriptomic, and DNA Methylome Studies. International Journal of Molecular Sciences. 26(22). 11102–11102.
2.
Khan, Sameer, et al.. (2025). Multimodal foundation transformer models for multiscale genomics. Nature Methods. 23(2). 299–311.
3.
Lagani, Vincenzo, et al.. (2024). Preventing epileptogenesis by interaction between inositol isomers and proteins. Epilepsia Open. 10(1). 120–133. 2 indexed citations
4.
Lakiotaki, Kleanthi, et al.. (2023). Automated machine learning for genome wide association studies. Bioinformatics. 39(9). 6 indexed citations
5.
Khan, Sameer, Vincenzo Lagani, Robert Lehmann, et al.. (2023). Reusability report: Learning the transcriptional grammar in single-cell RNA-sequencing data using transformers. Nature Machine Intelligence. 5(12). 1437–1446. 13 indexed citations
6.
Geracitano, Silvana, Vincenzo Lagani, Patrizia D’Aquila, et al.. (2022). A Blood-Based Molecular Clock for Biological Age Estimation. Cells. 12(1). 32–32. 10 indexed citations
7.
Tsamardinos, Ioannis, Γεώργιος Παπουτσόγλου, Giorgos Borboudakis, et al.. (2022). Just Add Data: automated predictive modeling for knowledge discovery and feature selection. npj Precision Oncology. 6(1). 38–38. 36 indexed citations
8.
Lagani, Vincenzo, et al.. (2022). Learning biologically-interpretable latent representations for gene expression data. Machine Learning. 112(11). 4257–4287. 4 indexed citations
9.
Παπουτσόγλου, Γεώργιος, Makrina Karaglani, Vincenzo Lagani, et al.. (2021). Automated machine learning optimizes and accelerates predictive modeling from COVID-19 high throughput datasets. Scientific Reports. 11(1). 15107–15107. 26 indexed citations
10.
Russa, Daniele La, et al.. (2021). Short and Long Time Bloodstains Age Determination by Colorimetric Analysis: A Pilot Study. Molecules. 26(20). 6272–6272. 10 indexed citations
11.
Ewing, Ewoud, Lara Kular, Nestoras Karathanasis, et al.. (2019). Combining evidence from four immune cell types identifies DNA methylation patterns that implicate functionally distinct pathways during Multiple Sclerosis progression. EBioMedicine. 43. 411–423. 46 indexed citations
12.
Markaki, Maria, et al.. (2019). P1.11-13 Mass Spectrometry Proteomics Analysis Discovers Biomarkers in Serum Months to Years Before Non-Small Cell Lung Cancer: The HUNT Study. Journal of Thoracic Oncology. 14(10). S520–S520. 1 indexed citations
13.
Morikawa, Hiromasa, Ewoud Ewing, Stephan Ruhrmann, et al.. (2019). Non-parametric combination analysis of multiple data types enables detection of novel regulatory mechanisms in T cells of multiple sclerosis patients. Scientific Reports. 9(1). 11996–11996. 11 indexed citations
14.
Markaki, Maria, Ioannis Tsamardinos, Arnulf Langhammer, et al.. (2018). A Validated Clinical Risk Prediction Model for Lung Cancer in Smokers of All Ages and Exposure Types: A HUNT Study. EBioMedicine. 31. 36–46. 47 indexed citations
15.
Colantonio, Sara, Massimo Martinelli, Davide Moroni, et al.. (2018). DECISION SUPPORT AND IMAGE & SIGNAL ANALYSIS IN HEART FAILURE - A Comprehensive Use Case. 5(9). 288–295.
16.
Lagani, Vincenzo, et al.. (2018). On scoring Maximal Ancestral Graphs with the Max–Min Hill Climbing algorithm. International Journal of Approximate Reasoning. 102. 74–85. 21 indexed citations
17.
Triantafillou, Sofia, Vincenzo Lagani, Christina Heinze‐Deml, et al.. (2017). Predicting Causal Relationships from Biological Data: Applying Automated Causal Discovery on Mass Cytometry Data of Human Immune Cells. Scientific Reports. 7(1). 22 indexed citations
18.
Koumakis, Lefteris, Franco Chiarugi, Vincenzo Lagani, Angelina Kouroubali, & Ioannis Tsamardinos. (2012). Risk Assessment Models for Diabetes Complications: A Survey of Available Online Tools. 1 indexed citations
19.
Borboudakis, Giorgos, et al.. (2011). A constraint-based approach to incorporate prior knowledge in causal models. The European Symposium on Artificial Neural Networks. 8 indexed citations
20.
Dolce, G, et al.. (2008). Dysautonomia and Clinical Outcome in Vegetative State. Journal of Neurotrauma. 38(10). 1441–1444. 59 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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