World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
32
Citations
4047
World Ranking
13216
National Ranking
467

Overview

Giuseppina Gini is affiliated with the Polytechnic University of Milan in Italy. Their research primarily spans the fields of Computer Science and Biochemistry, Genetics and Molecular Biology. Within these domains, Gini has concentrated on several subfields, including Computational Theory and Mathematics, Materials Chemistry, Molecular Biology, Health, Toxicology and Mutagenesis, and Genetics.

Their work addresses topics such as Computational Drug Discovery Methods, Machine Learning in Materials Science, Metabolomics and Mass Spectrometry Studies, Effects and Risks of Endocrine Disrupting Chemicals, Estrogen and Related Hormone Effects, Chemistry and Chemical Engineering, and Analytical Chemistry and Chromatography.

Gini's recent scholarly contributions include papers published between 2020 and 2023. Notable publications include:

  • Active upper limb prostheses: a review on current state and upcoming breakthroughs, 2022, Progress in Biomedical Engineering
  • Structures of Endocrine-Disrupting Chemicals Determine Binding to and Activation of the Estrogen Receptor α and Androgen Receptor, 2020, Environmental Science & Technology
  • Structures of Endocrine-Disrupting Chemicals Correlate with the Activation of 12 Classic Nuclear Receptors, 2021, Environmental Science & Technology
  • QSAR modeling without descriptors using graph convolutional neural networks: the case of mutagenicity prediction, 2021, Molecular Diversity
  • Evaluation of QSAR models for predicting mutagenicity: outcome of the Second Ames/QSAR international challenge project, 2023, SAR and QSAR in environmental research

Frequent coauthors collaborating with Gini include Emilio Benfenati, Huixiao Hong, Haoyue Tan, Hongxia Yu, and Chiakang Hung.

Their research has been frequently published in journals such as Environmental Science & Technology, Methods in Molecular Biology, Progress in Biomedical Engineering, Molecular Diversity, and SAR and QSAR in Environmental Research.

Best Publications

  • An EMG-controlled exoskeleton for hand rehabilitation

    M. Mulas;M. Folgheraiter;G. Gini

  • QSAR as a random event: modeling of nanoparticles uptake in PaCa2 cancer cells.

    Andrey A. Toropov;Alla P. Toropova;Tomasz Puzyn;Emilio Benfenati

  • Automatic knowledge extraction from chemical structures: the case of mutagenicity prediction.

    T. Ferrari;D. Cattaneo;G. Gini;N. Golbamaki Bakhtyari

  • VEGA-QSAR: AI Inside a Platform for Predictive Toxicology.

    Emilio Benfenati;Alberto Manganaro;Giuseppina C. Gini

  • Computational predictive programs (expert systems) in toxicology

    Emilio Benfenati;Giuseppina Gini

  • Predicting logP of pesticides using different software.

    E. Benfenati;G. Gini;N. Piclin;N. Piclin;A. Roncaglioni

  • Novel application of the CORAL software to model cytotoxicity of metal oxide nanoparticles to bacteria Escherichia coli.

    Andrey A. Toropov;Alla P. Toropova;Emilio Benfenati;Giuseppina Gini

  • CORAL: quantitative structure-activity relationship models for estimating toxicity of organic compounds in rats.

    Alla P. Toropova;Andrey A. Toropov;Emilio Benfenati;Giuseppina C. Gini

  • An open source multistep model to predict mutagenicity from statistical analysis and relevant structural alerts

    Thomas Ferrari;Giuseppina Gini

  • Predictive carcinogenicity: a model for aromatic compounds, with nitrogen-containing substituents, based on molecular descriptors using an artificial neural network.

    Giuseppina C. Gini;Marco Lorenzini;Emilio Benfenati;Paola Grasso

  • Integrating in silico models and read-across methods for predicting toxicity of chemicals: A step-wise strategy.

    Emilio Benfenati;Qasim Chaudhry;Giuseppina Gini;Jean Lou Dorne

  • Towards automatic error recovery in robot programs

    Maria L. Gini;Giuseppina C. Gini

  • CORAL: Building up the model for bioconcentration factor and defining it’s applicability domain

    A.A. Toropov;A.P. Toropova;A. Lombardo;A. Roncaglioni

  • Comparison of SMILES and molecular graphs as the representation of the molecular structure for QSAR analysis for mutagenic potential of polyaromatic amines

    A.A. Toropov;A.P. Toropova;S.E. Martyanov;E. Benfenati

  • Structures of Endocrine-Disrupting Chemicals Determine Binding to and Activation of the Estrogen Receptor α and Androgen Receptor

    Haoyue Tan;Xiaoxiang Wang;Huixiao Hong;Emilio Benfenati

  • CORAL: QSAR modeling of toxicity of organic chemicals towards Daphnia magna

    A.P. Toropova;A.A. Toropov;S.E. Martyanov;E. Benfenati

  • Description of the electronic structure of organic chemicals using semiempirical and ab initio methods for development of toxicological QSARs.

    Tatiana I. Netzeva;Aynur O. Aptula;Emilio Benfenati;Mark T. D. Cronin

  • The Importance of Scaling in Data Mining for Toxicity Prediction

    Paolo Mazzatorta;Emilio Benfenati;Daniel Neagu;Giuseppina C. Gini

  • Robotic hands: design review and proposal of new design process

    Jimmy W. Soto Martell;Giuseppina Gini

  • ToxRead: A tool to assist in read across and its use to assess mutagenicity of chemicals

    Giuseppina Gini;Alessio Mauro Franchi;A. Manganaro;A. Golbamaki

  • Indoor Robot Navigation with Single Camera Vision

    Giuseppina C. Gini;Alberto Marchi

  • Medical Robotics

    G Ferrigno;G Baroni;F Casolo;E De Momi

Frequent Co-Authors

Maria Gini
Maria Gini University of Minnesota
Franca Garzotto
Franca Garzotto Polytechnic University of Milan
Marek Perkowski
Marek Perkowski Portland State University
Giancarlo Ferrigno
Giancarlo Ferrigno Polytechnic University of Milan
John P. Giesy
John P. Giesy University of Saskatchewan
Huixiao Hong
Huixiao Hong United States Food and Drug Administration
Ivan Raška
Ivan Raška Charles University
Xiaowei Zhang
Xiaowei Zhang Nanjing University
Gilberto De Nucci
Gilberto De Nucci Universidade de São Paulo
Kai Goebel
Kai Goebel Palo Alto Research Center

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Exploring online education in computer science offers flexibility and affordability for many students. If you want to accelerate your academic journey, consider the shortest masters degree programs online. These allow you to earn a recognized qualification in less time, helping you enter the workforce or advance in your career sooner.

Choosing the right degree is crucial. Focus on masters degrees that are worth it to maximize career opportunities and earning potential after graduation. For those seeking a quicker or more affordable entry point into computing careers, pursuing an associate degree online provides foundational skills and opens doors to entry-level tech roles or further studies.

Additionally, cost is a key factor for many learners. Finding the cheapest online colleges helps you minimize debt while still obtaining a quality education. These pathways support diverse goals, from starting a new career in computer science to building on existing skills through advanced online degrees.

Best Scientists Citing Giuseppina Gini

Trending Scientists

Recently Published Articles