Giuseppe Agapito

2.0k total citations
72 papers, 906 citations indexed

About

Giuseppe Agapito is a scholar working on Molecular Biology, Information Systems and Computational Theory and Mathematics. According to data from OpenAlex, Giuseppe Agapito has authored 72 papers receiving a total of 906 indexed citations (citations by other indexed papers that have themselves been cited), including 50 papers in Molecular Biology, 13 papers in Information Systems and 10 papers in Computational Theory and Mathematics. Recurrent topics in Giuseppe Agapito's work include Bioinformatics and Genomic Networks (39 papers), Gene expression and cancer classification (32 papers) and Data Mining Algorithms and Applications (10 papers). Giuseppe Agapito is often cited by papers focused on Bioinformatics and Genomic Networks (39 papers), Gene expression and cancer classification (32 papers) and Data Mining Algorithms and Applications (10 papers). Giuseppe Agapito collaborates with scholars based in Italy, Canada and United Kingdom. Giuseppe Agapito's co-authors include Mario Cannataro, Pietro Hiram Guzzi, Mariamena Arbitrio, Marianna Milano, Barbara Calabrese, Maria Teresa Di Martino, Davide Chicco, Pierfrancesco Tassone, Chiara Zucco and Francesca Scionti and has published in prestigious journals such as SHILAP Revista de lepidopterología, Bioinformatics and BMC Bioinformatics.

In The Last Decade

Giuseppe Agapito

67 papers receiving 884 citations

Peers

Giuseppe Agapito

ONCOL

CTM

Pierre-Yves Vandenbussche Belgium

Christine Chichester Switzerland

Marco Brandizi United Kingdom

Marco Masseroli Italy

Diane E. Oliver United States

Guoqian Jiang United States

Sven Laur Estonia

Camille Laibe United Kingdom

Daniel Himmelstein United States

Lixia Yao United States

Pierre-Yves Vandenbussche Belgium

Giuseppe Agapito

339 ×0.8

117 ×0.8

225 ×1.6

116 ×1.1

ONCOL

69 ×0.7

CTM

Citations per year, relative to Giuseppe Agapito Giuseppe Agapito (= 1×) peers Pierre-Yves Vandenbussche

Countries citing papers authored by Giuseppe Agapito

Since Specialization

Citations

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

Fields of papers citing papers by Giuseppe Agapito

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Giuseppe Agapito

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

Arbitrio, Mariamena, Marianna Milano, Maria Lucibello, et al.. (2025). Bioinformatic challenges for pharmacogenomic study: tools for genomic data analysis. Frontiers in Pharmacology. 16. 1548991–1548991. 1 indexed citations

Agapito, Giuseppe, Marianna Milano, Francesca Scionti, et al.. (2024). Modeling UGT2B7 and NR1I3 genes through multilayer network to highlight hidden link with taxane neurotoxicity. 4331–4337. 1 indexed citations

Milano, Marianna, Giuseppe Agapito, & Mario Cannataro. (2023). An Exploratory Application of Multilayer Networks and Pathway Analysis in Pharmacogenomics. Genes. 14(10). 1915–1915. 5 indexed citations

Agapito, Giuseppe & Mario Cannataro. (2023). An Overview on the Challenges and Limitations Using Cloud Computing in Healthcare Corporations. Big Data and Cognitive Computing. 7(2). 68–68. 20 indexed citations

Agapito, Giuseppe, Chiara Pastrello, Yun Niu, & Igor Jurišica. (2022). Pathway integration and annotation: building a puzzle with non-matching pieces and no reference picture. Briefings in Bioinformatics. 23(5). 2 indexed citations

Milano, Marianna, Giuseppe Agapito, & Mario Cannataro. (2022). Challenges and Limitations of Biological Network Analysis. BioTech. 11(3). 24–24. 18 indexed citations

Agapito, Giuseppe, Marianna Milano, & Mario Cannataro. (2022). A Python Clustering Analysis Protocol of Genes Expression Data Sets. Genes. 13(10). 1839–1839. 4 indexed citations

Milano, Marianna, Giuseppe Agapito, & Mario Cannataro. (2022). Application of CCTV Methodology to Analyze COVID-19 Evolution in Italy. BioTech. 11(3). 33–33. 1 indexed citations

Agapito, Giuseppe, Marianna Milano, & Mario Cannataro. (2022). A statistical network pre-processing method to improve relevance and significance of gene lists in microarray gene expression studies. BMC Bioinformatics. 23(S6). 393–393. 4 indexed citations

10.

Chicco, Davide & Giuseppe Agapito. (2022). Nine quick tips for pathway enrichment analysis. PLoS Computational Biology. 18(8). e1010348–e1010348. 45 indexed citations

11.

Agapito, Giuseppe & Giuseppe Fedele. (2021). Clustering Methods for Microarray Data Sets. Methods in molecular biology. 2401. 249–261. 1 indexed citations

12.

Agapito, Giuseppe & Mariamena Arbitrio. (2021). Microarray Data Analysis Protocol. Methods in molecular biology. 2401. 263–271. 7 indexed citations

13.

Agapito, Giuseppe. (2019). Computer Tools to Analyze Microarray Data. Methods in molecular biology. 1986. 267–282. 3 indexed citations

14.

Scionti, Francesca, Maria Teresa Di Martino, Simona Sestito, et al.. (2017). Genetic variants associated with Fabry disease progression despite enzyme replacement therapy. Oncotarget. 8(64). 107558–107564. 28 indexed citations

15.

Agapito, Giuseppe, Mariadelina Simeoni, Barbara Calabrese, et al.. (2017). DIETOS: A Recommender System for Health Profiling and Diet Management in Chronic Diseases.. Conference on Recommender Systems. 32–35. 8 indexed citations

16.

Agapito, Giuseppe, Mariadelina Simeoni, Barbara Calabrese, et al.. (2017). DIETOS: A dietary recommender system for chronic diseases monitoring and management. Computer Methods and Programs in Biomedicine. 153. 93–104. 74 indexed citations

17.

Milano, Marianna, Giuseppe Agapito, Pietro Hiram Guzzi, & Mario Cannataro. (2016). An experimental study of information content measurement of gene ontology terms. International Journal of Machine Learning and Cybernetics. 9(3). 427–439. 8 indexed citations

18.

Agapito, Giuseppe, Pietro Hiram Guzzi, & Mario Cannataro. (2015). DMET-Miner: Efficient discovery of association rules from pharmacogenomic data. Journal of Biomedical Informatics. 56. 273–283. 36 indexed citations

19.

Agapito, Giuseppe, Pietro Hiram Guzzi, & Mario Cannataro. (2013). Visualization of protein interaction networks: problems and solutions. BMC Bioinformatics. 14(S1). S1–S1. 40 indexed citations

20.

Guzzi, Pietro Hiram, Giuseppe Agapito, Maria Teresa Di Martino, et al.. (2012). DMET-Analyzer: automatic analysis of Affymetrix DMET Data. BMC Bioinformatics. 13(1). 258–258. 52 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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