Giuseppina Gini

4.0k total citations
142 papers, 2.7k citations indexed

About

Giuseppina Gini is a scholar working on Computational Theory and Mathematics, Biomedical Engineering and Artificial Intelligence. According to data from OpenAlex, Giuseppina Gini has authored 142 papers receiving a total of 2.7k indexed citations (citations by other indexed papers that have themselves been cited), including 71 papers in Computational Theory and Mathematics, 30 papers in Biomedical Engineering and 26 papers in Artificial Intelligence. Recurrent topics in Giuseppina Gini's work include Computational Drug Discovery Methods (68 papers), Analytical Chemistry and Chromatography (17 papers) and Muscle activation and electromyography studies (16 papers). Giuseppina Gini is often cited by papers focused on Computational Drug Discovery Methods (68 papers), Analytical Chemistry and Chromatography (17 papers) and Muscle activation and electromyography studies (16 papers). Giuseppina Gini collaborates with scholars based in Italy, United States and France. Giuseppina Gini's co-authors include Emilio Benfenati, Andrey A. Toropov, Alla P. Toropova, Michele Folgheraiter, Danuta Leszczyńska, Jerzy Leszczyński, Alberto Manganaro, Maria Gabriella Mulas, Alessandra Roncaglioni and Maria Gini and has published in prestigious journals such as SHILAP Revista de lepidopterología, Environmental Science & Technology and Biochemical and Biophysical Research Communications.

In The Last Decade

Giuseppina Gini

139 papers receiving 2.6k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Giuseppina Gini Italy 29 1.4k 524 521 365 331 142 2.7k
David T. Manallack Australia 28 706 0.5× 113 0.2× 1.1k 2.1× 66 0.2× 227 0.7× 87 2.6k
Francesca Grisoni Italy 32 1.8k 1.2× 351 0.7× 1.5k 2.8× 110 0.3× 939 2.8× 89 3.3k
Dávid Bajusz Hungary 21 1.1k 0.8× 202 0.4× 1.1k 2.1× 87 0.2× 383 1.2× 55 2.5k
Andrea Mauri Italy 14 1.2k 0.9× 245 0.5× 885 1.7× 160 0.4× 371 1.1× 31 2.6k
Wei Lan China 35 326 0.2× 289 0.6× 1.7k 3.3× 115 0.3× 205 0.6× 179 3.8k
Ivo Provazník Czechia 26 197 0.1× 536 1.0× 954 1.8× 234 0.6× 130 0.4× 159 3.0k
Anita Rácz Hungary 22 1.0k 0.7× 173 0.3× 949 1.8× 42 0.1× 363 1.1× 58 2.3k
David T. Stanton United States 22 829 0.6× 231 0.4× 525 1.0× 76 0.2× 213 0.6× 39 1.7k
Jens Sadowski Germany 19 2.0k 1.4× 131 0.3× 1.6k 3.0× 37 0.1× 481 1.5× 28 3.1k
Haiyun Zhou China 27 933 0.7× 196 0.4× 180 0.3× 41 0.1× 138 0.4× 127 2.1k

Countries citing papers authored by Giuseppina Gini

Since Specialization
Citations

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

Fields of papers citing papers by Giuseppina Gini

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Giuseppina Gini

This figure shows the co-authorship network connecting the top 25 collaborators of Giuseppina Gini. A scholar is included among the top collaborators of Giuseppina Gini 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 Giuseppina Gini. Giuseppina Gini 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.
Banerjee, Arkaprava, Supratik Kar, Kunal Roy, et al.. (2025). From Feature‐Based Chemical Similarity to Chemical Language Models—A Paradigm Shift in Computer‐Aided Molecular Design and Property Predictions. Wiley Interdisciplinary Reviews Computational Molecular Science. 15(6).
2.
Tan, Haoyue, Qinchang Chen, Huixiao Hong, et al.. (2021). Structures of Endocrine-Disrupting Chemicals Correlate with the Activation of 12 Classic Nuclear Receptors. Environmental Science & Technology. 55(24). 16552–16562. 38 indexed citations
3.
Gini, Giuseppina, et al.. (2021). QSAR modeling without descriptors using graph convolutional neural networks: the case of mutagenicity prediction. Molecular Diversity. 25(3). 1283–1299. 31 indexed citations
4.
Gini, Giuseppina. (2020). The QSAR similarity principle in the deep learning era: Confirmation or revision?. Foundations of Chemistry. 22(3). 383–402. 6 indexed citations
5.
Tan, Haoyue, Xiaoxiang Wang, Huixiao Hong, et al.. (2020). Structures of Endocrine-Disrupting Chemicals Determine Binding to and Activation of the Estrogen Receptor α and Androgen Receptor. Environmental Science & Technology. 54(18). 11424–11433. 63 indexed citations
6.
Pavan, E., et al.. (2018). Analysis and Comparison of Features and Algorithms to Classify Shoulder Movements From sEMG Signals. IEEE Sensors Journal. 18(9). 3714–3721. 27 indexed citations
7.
Gini, Giuseppina, et al.. (2016). Learning and executing rhythmic movements through chaotic neural networks: a new method for walking humanoid robots. Virtual Community of Pathological Anatomy (University of Castilla La Mancha). 528–533. 3 indexed citations
8.
Benfenati, Emilio, et al.. (2016). New clues on carcinogenicity-related substructures derived from mining two large datasets of chemical compounds. Journal of Environmental Science and Health Part C. 34(2). 97–113. 25 indexed citations
9.
Lombardo, Anna, et al.. (2014). A new in silico classification model for ready biodegradability, based on molecular fragments. Chemosphere. 108. 10–16. 34 indexed citations
10.
Toropov, Andrey A., Alla P. Toropova, Emilio Benfenati, et al.. (2013). CORAL: Classification Model for Predictions of Anti-Sarcoma Activity. Current Topics in Medicinal Chemistry. 12(24). 2741–2744. 8 indexed citations
11.
Gini, Giuseppina, et al.. (2012). Acquisition and analysis of EMG signals to recognize multiple hand movements for prosthetic applications. SHILAP Revista de lepidopterología. 9(2). 145–155. 16 indexed citations
12.
Toropov, Andrey A., Alla P. Toropova, Emilio Benfenati, et al.. (2012). Calculation of Molecular Features with Apparent Impact on Both Activity of Mutagens and Activity of Anticancer Agents. Anti-Cancer Agents in Medicinal Chemistry. 12(7). 807–817. 8 indexed citations
13.
Toropov, Andrey A., Alla P. Toropova, Emilio Benfenati, et al.. (2011). Comparison of SMILES and molecular graphs as the representation of the molecular structure for QSAR analysis for mutagenic potential of polyaromatic amines. Chemometrics and Intelligent Laboratory Systems. 109(1). 94–100. 48 indexed citations
14.
Toropova, Alla P., Andrey A. Toropov, Emilio Benfenati, et al.. (2011). CORAL: Quantitative structure–activity relationship models for estimating toxicity of organic compounds in rats. Journal of Computational Chemistry. 32(12). 2727–2733. 87 indexed citations
15.
Ferrigno, Giancarlo, G. Baroni, Elena De Momi, et al.. (2011). Medical Robotics. IEEE Pulse. 5 indexed citations
16.
Gini, Giuseppina, et al.. (2010). A biomimetic upper body for humanoids. Virtual Community of Pathological Anatomy (University of Castilla La Mancha). 1–8. 1 indexed citations
17.
Gini, Giuseppina, et al.. (2010). The in silico model for mutagenicity. Virtual Community of Pathological Anatomy (University of Castilla La Mancha). 27. 117–125. 1 indexed citations
18.
Gini, Giuseppina, Umberto Scarfogliero, & Michele Folgheraiter. (2009). New Joint Design to Create a More Natural and Efficient Biped. Applied Bionics and Biomechanics. 6(1). 27–42. 2 indexed citations
19.
Benfenati, Emilio, et al.. (2002). Neuro-fuzzy knowledge representation for toxicity prediction of organic compounds. European Conference on Artificial Intelligence. 498–502. 7 indexed citations
20.
Gini, Giuseppina & Alan R. Katritzky. (1999). Predictive toxicology of chemicals : experiences and impact of AI tools : papers from the 1999 AAAI Symposium : March 22-24, Stanford, California. 6 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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