Gianvito Pio

1.1k total citations
38 papers, 610 citations indexed

About

Gianvito Pio is a scholar working on Artificial Intelligence, Molecular Biology and Statistical and Nonlinear Physics. According to data from OpenAlex, Gianvito Pio has authored 38 papers receiving a total of 610 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Artificial Intelligence, 15 papers in Molecular Biology and 6 papers in Statistical and Nonlinear Physics. Recurrent topics in Gianvito Pio's work include Gene expression and cancer classification (7 papers), Complex Network Analysis Techniques (6 papers) and Bioinformatics and Genomic Networks (6 papers). Gianvito Pio is often cited by papers focused on Gene expression and cancer classification (7 papers), Complex Network Analysis Techniques (6 papers) and Bioinformatics and Genomic Networks (6 papers). Gianvito Pio collaborates with scholars based in Italy, Slovenia and Czechia. Gianvito Pio's co-authors include Michelangelo Ceci, Donato Malerba, Domenica D’Elia, Nicoletta Del Buono, Roberto Corizzo, Massimo Bilancia, Sašo Džeroski, Nathalie Japkowicz, Vladimir Kuzmanovski and Corrado Appice Annalisa Malerba Donato Loglisci and has published in prestigious journals such as Bioinformatics, PLoS ONE and Scientific Reports.

In The Last Decade

Gianvito Pio

32 papers receiving 596 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Gianvito Pio Italy	17	281	206	101	78	72	38	610
Qinke Peng China	15	332 1.2×	314 1.5×	216 2.1×	100 1.3×	46 0.6×	94	929
Guangtao Wang China	20	651 2.3×	110 0.5×	157 1.6×	211 2.7×	68 0.9×	39	875
Ricardo J. G. B. Campello Brazil	10	205 0.7×	190 0.9×	93 0.9×	66 0.8×	33 0.5×	15	472
Guobing Zou China	15	245 0.9×	160 0.8×	354 3.5×	70 0.9×	66 0.9×	98	728
Dawn Wilkins United States	14	211 0.8×	175 0.8×	149 1.5×	132 1.7×	18 0.3×	47	741
Xiaohua Hu United States	16	677 2.4×	199 1.0×	323 3.2×	119 1.5×	95 1.3×	94	1.0k
Yanglan Gan China	15	173 0.6×	296 1.4×	211 2.1×	52 0.7×	15 0.2×	75	658
Mingxin Gan China	16	248 0.9×	167 0.8×	375 3.7×	79 1.0×	59 0.8×	68	672
Aman Sharma India	14	247 0.9×	138 0.7×	112 1.1×	66 0.8×	13 0.2×	71	657
Ning Yang China	14	183 0.7×	52 0.3×	132 1.3×	57 0.7×	48 0.7×	62	478

Countries citing papers authored by Gianvito Pio

Since Specialization

Citations

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

Fields of papers citing papers by Gianvito Pio

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Gianvito Pio

This figure shows the co-authorship network connecting the top 25 collaborators of Gianvito Pio. A scholar is included among the top collaborators of Gianvito Pio 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 Gianvito Pio. Gianvito Pio 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

Pio, Gianvito, et al.. (2025). Handling complex backgrounds and light perturbations for enhancing learning tasks from images of vegetables. Journal of Intelligent Information Systems. 64(1). 215–238.

Aloi, Gianluca, Fabio Frustaci, Raffaele Gravina, et al.. (2025). COCOWEARS: A framework for COntinuum COmputing WEARable Systems. Procedia Computer Science. 253. 1525–1534. 1 indexed citations

Pio, Gianvito, et al.. (2024). A novel random forest-based approach for the non-destructive and explainable estimation of ammonia and chlorophyll in fresh-cut rocket leaves. Information Processing in Agriculture. 12(2). 221–231.

Pio, Gianvito, et al.. (2024). CARROT: Simultaneous prediction of anomalies from groups of correlated cryptocurrency trends. Expert Systems with Applications. 260. 125457–125457. 2 indexed citations

Pio, Gianvito, et al.. (2023). Forecasting and what-if analysis of new positive COVID-19 cases during the first three waves in Italy. Medical & Biological Engineering & Computing. 61(8). 2051–2066. 2 indexed citations

Corizzo, Roberto, et al.. (2023). HURI: Hybrid user risk identification in social networks. World Wide Web. 26(5). 3409–3439. 4 indexed citations

Pio, Gianvito, et al.. (2023). Multi-view overlapping clustering for the identification of the subject matter of legal judgments. Information Sciences. 638. 118956–118956. 7 indexed citations

Petković, Matej, Michelangelo Ceci, Gianvito Pio, et al.. (2022). Relational tree ensembles and feature rankings. Knowledge-Based Systems. 251. 109254–109254. 4 indexed citations

Pio, Gianvito, et al.. (2022). Distributed Heterogeneous Transfer Learning for Link Prediction in the Positive Unlabeled Setting. 2022 IEEE International Conference on Big Data (Big Data). 5 indexed citations

10.

Hess, Sibylle, Gianvito Pio, Michiel E. Hochstenbach, & Michelangelo Ceci. (2021). BROCCOLI: overlapping and outlier-robust biclustering through proximal stochastic gradient descent. Data Mining and Knowledge Discovery. 35(6). 2542–2576. 8 indexed citations

11.

Pio, Gianvito, et al.. (2021). Integrating genome-scale metabolic modelling and transfer learning for human gene regulatory network reconstruction. Bioinformatics. 38(2). 487–493. 26 indexed citations

12.

Pio, Gianvito, et al.. (2020). Multi-task learning for the simultaneous reconstruction of the human and mouse gene regulatory networks. Scientific Reports. 10(1). 22295–22295. 25 indexed citations

13.

Pio, Gianvito, et al.. (2019). Exploiting causality in gene network reconstruction based on graph embedding. Machine Learning. 109(6). 1231–1279. 25 indexed citations

14.

Corizzo, Roberto, Gianvito Pio, Michelangelo Ceci, & Donato Malerba. (2019). DENCAST: distributed density-based clustering for multi-target regression. Journal Of Big Data. 6(1). 36 indexed citations

15.

Ceci, Michelangelo, Gianvito Pio, Vladimir Kuzmanovski, & Sašo Džeroski. (2015). Semi-Supervised Multi-View Learning for Gene Network Reconstruction. PLoS ONE. 10(12). e0144031–e0144031. 30 indexed citations

16.

Buono, Nicoletta Del & Gianvito Pio. (2015). Non-negative Matrix Tri-Factorization for co-clustering: An analysis of the block matrix. Information Sciences. 301. 13–26. 51 indexed citations

17.

Pio, Gianvito, Michelangelo Ceci, Domenica D’Elia, & Donato Malerba. (2014). Learning to Combine miRNA Target Predictions: a Semi-supervised Ensemble Learning Approach.. SEBD. 21–28. 1 indexed citations

18.

Pio, Gianvito, Donato Malerba, Domenica D’Elia, & Michelangelo Ceci. (2014). Integrating microRNA target predictions for the discovery of gene regulatory networks: a semi-supervised ensemble learning approach. BMC Bioinformatics. 15(S1). S4–S4. 43 indexed citations

19.

Pio, Gianvito, Michelangelo Ceci, Domenica D’Elia, Corrado Appice Annalisa Malerba Donato Loglisci, & Donato Malerba. (2013). HOCCLUS2: A Biclustering Algorithm for the Discovery of miRNA: mRNA regulatory modules.. SEBD. 445–452. 1 indexed citations

20.

Pio, Gianvito, Michelangelo Ceci, Domenica D’Elia, Corrado Appice Annalisa Malerba Donato Loglisci, & Donato Malerba. (2013). A Novel Biclustering Algorithm for the Discovery of Meaningful Biological Correlations between microRNAs and their Target Genes. BMC Bioinformatics. 14(S7). S8–S8. 29 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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