Francesca Martella

483 total citations
20 papers, 150 citations indexed

About

Francesca Martella is a scholar working on Artificial Intelligence, Molecular Biology and Computer Vision and Pattern Recognition. According to data from OpenAlex, Francesca Martella has authored 20 papers receiving a total of 150 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Artificial Intelligence, 8 papers in Molecular Biology and 3 papers in Computer Vision and Pattern Recognition. Recurrent topics in Francesca Martella's work include Bayesian Methods and Mixture Models (10 papers), Gene expression and cancer classification (7 papers) and Bioinformatics and Genomic Networks (4 papers). Francesca Martella is often cited by papers focused on Bayesian Methods and Mixture Models (10 papers), Gene expression and cancer classification (7 papers) and Bioinformatics and Genomic Networks (4 papers). Francesca Martella collaborates with scholars based in Italy, United Kingdom and Netherlands. Francesca Martella's co-authors include Marco Alfò, Maurizio Vichi, Maria Brigida Ferraro, Paolo Giordani, Antonello Maruotti, Francesco Lagona, Michele De Sanctis, Roberto Valenti, Fabio Francesconi and Jan Bulla and has published in prestigious journals such as Bioinformatics, Cancer Research and Pharmacology & Therapeutics.

In The Last Decade

Francesca Martella

18 papers receiving 148 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Francesca Martella Italy 7 54 45 16 12 12 20 150
Jonathan McPherson 3 25 0.5× 43 1.0× 16 1.0× 7 0.6× 5 0.4× 3 187
Daniel J. McDonald United States 8 16 0.3× 44 1.0× 24 1.5× 4 0.3× 5 0.4× 32 188
Paul Cabilio Canada 10 52 1.0× 11 0.2× 136 8.5× 26 2.2× 3 0.3× 32 273
Shuqian Shen Hong Kong 3 134 2.5× 33 0.7× 92 5.8× 6 0.5× 11 0.9× 4 268
Prashanti Manda United States 9 79 1.5× 145 3.2× 3 0.2× 3 0.3× 4 0.3× 31 276
Simone Gittelson Switzerland 10 45 0.8× 85 1.9× 8 0.5× 10 0.8× 18 231
Graham Jackson United Kingdom 10 70 1.3× 53 1.2× 18 1.1× 29 2.4× 17 300
Daniele Durante Italy 10 89 1.6× 14 0.3× 77 4.8× 3 0.3× 2 0.2× 26 213
Frédéric Achard France 6 61 1.1× 186 4.1× 3 0.2× 5 0.4× 2 0.2× 13 251
Toby Kenney Canada 7 11 0.2× 59 1.3× 6 0.4× 3 0.3× 2 0.2× 23 130

Countries citing papers authored by Francesca Martella

Since Specialization
Citations

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

Fields of papers citing papers by Francesca Martella

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Francesca Martella

This figure shows the co-authorship network connecting the top 25 collaborators of Francesca Martella. A scholar is included among the top collaborators of Francesca Martella 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 Francesca Martella. Francesca Martella 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.
Martella, Francesca, et al.. (2025). Biomarker identification in bipolar disorder. Pharmacology & Therapeutics. 268. 108823–108823. 1 indexed citations
2.
Martella, Francesca, et al.. (2025). Extending Cluster-Weighted Factor Analyzers for Multivariate Prediction and Interpretability. Journal of Classification. 43(1). 117–145.
3.
Martella, Francesca, et al.. (2024). Finite Mixtures of Latent Trait Analyzers With Concomitant Variables for Bipartite Networks: An Analysis of COVID-19 Data. Multivariate Behavioral Research. 59(4). 801–817. 1 indexed citations
4.
5.
Alfò, Marco, Margaret F. Marino, & Francesca Martella. (2023). Biclustering multivariate discrete longitudinal data. Statistics and Computing. 34(1). 1 indexed citations
6.
Giordani, Paolo, Maria Brigida Ferraro, & Francesca Martella. (2020). An Introduction to Clustering with R. CERN Document Server (European Organization for Nuclear Research). 26 indexed citations
7.
Martella, Francesca, et al.. (2018). Probabilistic Disjoint Principal Component Analysis. Multivariate Behavioral Research. 54(1). 47–61. 4 indexed citations
8.
Maruotti, Antonello, Jan Bulla, Francesco Lagona, Marco Picone, & Francesca Martella. (2017). Dynamic mixtures of factor analyzers to characterize multivariate air pollutant exposures. The Annals of Applied Statistics. 11(3). 15 indexed citations
9.
Martella, Francesca & Marco Alfò. (2017). A finite mixture approach to joint clustering of individuals and multivariate discrete outcomes. Journal of Statistical Computation and Simulation. 87(11). 2186–2206. 3 indexed citations
10.
Pelosi, Michele, Marco Alfò, Francesca Martella, Elisa Pappalardo, & Antonio Musarò. (2015). Finite mixture clustering of human tissues with different levels of IGF-1 splice variants mRNA transcripts. BMC Bioinformatics. 16(1). 289–289. 9 indexed citations
11.
Martella, Francesca, et al.. (2015). A biclustering approach to university performances: an Italian case study. Journal of Applied Statistics. 43(1). 31–45. 14 indexed citations
12.
Martella, Francesca, Donatella Vicari, & Maurizio Vichi. (2013). Partitioning predictors in multivariate regression models. Statistics and Computing. 25(2). 261–272. 3 indexed citations
13.
Attorre, Fabio, Fabio Francesconi, Michele De Sanctis, et al.. (2012). Classifying and Mapping Potential Distribution of Forest Types Using a Finite Mixture Model. Folia Geobotanica. 49(3). 313–335. 19 indexed citations
14.
Martella, Francesca & Maurizio Vichi. (2012). Clustering microarray data using model-based doubleK-means. Journal of Applied Statistics. 39(9). 1853–1869. 5 indexed citations
15.
Maruotti, Antonello & Francesca Martella. (2012). Clustering Multivariate Longitudinal Data: Hidden Markov of Factor Analyzers. Iris (Roma Tre University). 1 indexed citations
16.
Martella, Francesca & Jeroen K. Vermunt. (2011). Model-based approaches to synthesize microarray data: a unifying review using mixture of SEMs. Statistical Methods in Medical Research. 22(6). 567–582. 1 indexed citations
17.
Martella, Francesca, Jeroen K. Vermunt, Marian Beekman, et al.. (2011). A mixture model with random‐effects components for classifying sibling pairs. Statistics in Medicine. 30(27). 3252–3264. 3 indexed citations
18.
Martella, Francesca, Marco Alfò, & Maurizio Vichi. (2011). Hierarchical mixture models for biclustering in microarray data. Statistical Modelling. 11(6). 489–505. 6 indexed citations
19.
Martella, Francesca, Marco Alfò, & Maurizio Vichi. (2008). Biclustering of Gene Expression Data by an Extension of Mixtures of Factor Analyzers. The International Journal of Biostatistics. 4(1). Article 3–Article 3. 16 indexed citations
20.
Martella, Francesca. (2005). Classification of microarray data with factor mixture models. Bioinformatics. 22(2). 202–208. 22 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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