Annalisa Barla

1.7k total citations
67 papers, 1.1k citations indexed

About

Annalisa Barla is a scholar working on Molecular Biology, Artificial Intelligence and Computer Vision and Pattern Recognition. According to data from OpenAlex, Annalisa Barla has authored 67 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Molecular Biology, 13 papers in Artificial Intelligence and 9 papers in Computer Vision and Pattern Recognition. Recurrent topics in Annalisa Barla's work include Gene expression and cancer classification (11 papers), Bioinformatics and Genomic Networks (11 papers) and Computational Drug Discovery Methods (5 papers). Annalisa Barla is often cited by papers focused on Gene expression and cancer classification (11 papers), Bioinformatics and Genomic Networks (11 papers) and Computational Drug Discovery Methods (5 papers). Annalisa Barla collaborates with scholars based in Italy, United States and United Kingdom. Annalisa Barla's co-authors include Alessandro Verri, Francesca Odone, Sofia Mosci, Margherita Squillario, Lorenzo Rosasco, Cesare Furlanello, Giuseppe Jurman, Stefano Merler, Luigi Varesio and Paolo Fardin and has published in prestigious journals such as SHILAP Revista de lepidopterología, Bioinformatics and PLoS ONE.

In The Last Decade

Annalisa Barla

64 papers receiving 1.0k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Annalisa Barla Italy 19 357 248 151 144 94 67 1.1k
Pei Chen China 15 196 0.5× 194 0.8× 154 1.0× 64 0.4× 26 0.3× 79 854
Sofia Mosci Italy 13 147 0.4× 101 0.4× 106 0.7× 102 0.7× 78 0.8× 22 487
Francisco Tirado Spain 19 825 2.3× 325 1.3× 188 1.2× 201 1.4× 17 0.2× 97 2.0k
Rubén Armañanzas Spain 17 755 2.1× 100 0.4× 308 2.0× 212 1.5× 66 0.7× 37 1.5k
Bing Yang China 13 272 0.8× 247 1.0× 120 0.8× 32 0.2× 17 0.2× 42 1.2k
Hongbin Zha China 20 1.0k 2.8× 600 2.4× 176 1.2× 98 0.7× 42 0.4× 59 2.2k
Haofan Wang China 19 298 0.8× 327 1.3× 423 2.8× 168 1.2× 15 0.2× 53 1.6k
Xiao Han China 25 185 0.5× 350 1.4× 738 4.9× 121 0.8× 80 0.9× 145 1.9k
Andrea Tangherloni Italy 15 316 0.9× 279 1.1× 276 1.8× 49 0.3× 9 0.1× 37 966
Wen‐Bin Zou China 21 162 0.5× 156 0.6× 117 0.8× 61 0.4× 35 0.4× 82 1.3k

Countries citing papers authored by Annalisa Barla

Since Specialization
Citations

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

Fields of papers citing papers by Annalisa Barla

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Annalisa Barla

This figure shows the co-authorship network connecting the top 25 collaborators of Annalisa Barla. A scholar is included among the top collaborators of Annalisa Barla 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 Annalisa Barla. Annalisa Barla 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.
Verri, Alessandro, et al.. (2024). Deep learning-based Alzheimer's disease detection: reproducibility and the effect of modeling choices. Frontiers in Computational Neuroscience. 18. 1360095–1360095. 3 indexed citations
2.
Trucco, Andrea, et al.. (2023). Introducing Temporal Correlation in Rainfall and Wind Prediction From Underwater Noise. IEEE Journal of Oceanic Engineering. 48(2). 349–364. 5 indexed citations
3.
Squillario, Margherita, et al.. (2023). An Overview of Data Integration in Neuroscience With Focus on Alzheimer's Disease. IEEE Journal of Biomedical and Health Informatics. 28(4). 1824–1835. 9 indexed citations
4.
Barla, Annalisa, et al.. (2023). Geometric Deep Learning Strategies for the Characterization of Academic Collaboration Networks. IEEE Transactions on Emerging Topics in Computing. 12(3). 840–851. 2 indexed citations
5.
Trucco, Andrea, et al.. (2022). Compounding Approaches for Wind Prediction From Underwater Noise by Supervised Learning. IEEE Journal of Oceanic Engineering. 47(4). 1172–1187. 5 indexed citations
6.
Azencott, Chloé‐Agathe, et al.. (2021). Where Do We Stand in Regularization for Life Science Studies?. Journal of Computational Biology. 29(3). 213–232. 3 indexed citations
7.
Trucco, Andrea, et al.. (2021). A Supervised Learning Approach for Rainfall Detection From Underwater Noise Analysis. IEEE Journal of Oceanic Engineering. 47(1). 213–225. 8 indexed citations
8.
Barla, Annalisa, et al.. (2021). Temporal Pattern Detection in Time-Varying Graphical Models. CINECA IRIS Institutial Research Information System (University of Genoa). 4481–4488. 4 indexed citations
9.
Cilloni, Daniela, Jessica Petiti, Marina Podestà, et al.. (2020). Transplantation Induces Profound Changes in the Transcriptional Asset of Hematopoietic Stem Cells: Identification of Specific Signatures Using Machine Learning Techniques. Journal of Clinical Medicine. 9(6). 1670–1670. 4 indexed citations
10.
Ravera, Silvia, Marina Podestà, Federica Sabatini, et al.. (2019). Discrete Changes in Glucose Metabolism Define Aging. Scientific Reports. 9(1). 10347–10347. 45 indexed citations
11.
Piaggio, Francesca, Cinzia Bernardi, Michela Croce, et al.. (2019). Secondary Somatic Mutations in G-Protein-Related Pathways and Mutation Signatures in Uveal Melanoma. Cancers. 11(11). 1688–1688. 22 indexed citations
12.
Verri, Alessandro, et al.. (2017). Temporal prediction of multiple sclerosis evolution from patient-centered outcomes. CINECA IRIS Institutial Research Information System (University of Genoa). 68. 112–125. 5 indexed citations
13.
Squillario, Margherita, Matteo Barbieri, Alessandro Verri, & Annalisa Barla. (2016). Enhancing Interpretability of Gene Signatures with Prior Biological Knowledge. SHILAP Revista de lepidopterología. 5(2). 15–15. 1 indexed citations
14.
Mosci, Carlo, Francesco Lanza, Sofia Mosci, & Annalisa Barla. (2014). Quantitative echography in primary uveal melanoma treated by proton beam therapy. Canadian Journal of Ophthalmology. 49(1). 60–65. 8 indexed citations
15.
Salzo, Saverio, et al.. (2013). A dictionary learning based method for aCGH segmentation. CINECA IRIS Institutial Research Information System (University of Genoa). 461–466. 2 indexed citations
16.
17.
Barla, Annalisa & Marco Ferrante. (2009). Deployment of a Regularized Feature Selection Framework on an Overlay Desktop Grid. CINECA IRIS Institutial Research Information System (University of Genoa). 103–104. 2 indexed citations
18.
Barla, Annalisa, Sofia Mosci, Lorenzo Rosasco, & Alessandro Verri. (2008). A method for robust variable selection with significance assessment. CINECA IRIS Institutial Research Information System (University of Genoa). 83–88. 14 indexed citations
19.
Baldassarre, Luca, et al.. (2008). Vector valued regression for iron overload estimation. Proceedings - International Conference on Pattern Recognition. 98. 1–4. 3 indexed citations
20.
Odone, Francesca, Annalisa Barla, & Alessandro Verri. (2005). Building kernels from binary strings for image matching. IEEE Transactions on Image Processing. 14(2). 169–180. 75 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.

Explore authors with similar magnitude of impact

Breakdown of academic impact, for papers by Pei Chen Breakdown of academic impact, for papers by Sofia Mosci Breakdown of academic impact, for papers by Francisco Tirado Breakdown of academic impact, for papers by Rubén Armañanzas Breakdown of academic impact, for papers by Bing Yang Breakdown of academic impact, for papers by Hongbin Zha Breakdown of academic impact, for papers by Haofan Wang Breakdown of academic impact, for papers by Xiao Han Breakdown of academic impact, for papers by Andrea Tangherloni Breakdown of academic impact, for papers by Wen‐Bin Zou
Rankless by CCL
2026