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Engineering and Technology

D-Index
42
Citations
17745
World Ranking
6353
National Ranking
208

Overview

Viviana Consonni is affiliated with the University of Milano-Bicocca in Italy. Their research spans multiple fields with a focus on computer science and chemistry, particularly at the intersection of computational methods and analytical sciences.

The primary fields of study in their work include:

  • Computer Science
  • Chemistry

Within these fields, they have contributed extensively to subfields such as:

  • Computational Theory and Mathematics
  • Molecular Biology
  • Analytical Chemistry
  • Spectroscopy
  • Artificial Intelligence

The main research topics covered in their publications include:

  • Computational Drug Discovery Methods
  • Analytical Chemistry and Chromatography
  • Machine Learning in Materials Science
  • Metabolomics and Mass Spectrometry Studies
  • Spectroscopy and Chemometric Analyses
  • Advanced Chemical Sensor Technologies
  • Analytical chemistry methods development

Viviana Consonni has contributed to multiple scientific journals, with frequent publications in the following venues:

  • Chemometrics and Intelligent Laboratory Systems
  • Environmental Health Perspectives
  • Molecules
  • Separations
  • Biomedical Science and Engineering

Some representative recent papers authored or co-authored by them include:

  • A MATLAB toolbox for multivariate regression coupled with variable selection (2021), Chemometrics and Intelligent Laboratory Systems
  • CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity (2020), Environmental Health Perspectives
  • CATMoS: Collaborative Acute Toxicity Modeling Suite (2021), Environmental Health Perspectives
  • Geographical identification of Chianti red wine based on ICP-MS element composition (2020), Food Chemistry
  • Consensus versus Individual QSARs in Classification: Comparison on a Large-Scale Case Study (2020), Journal of Chemical Information and Modeling

Frequent collaborators in their scientific work include:

  • Davide Ballabio
  • Roberto Todeschini
  • Francesca Grisoni
  • Fabio Gosetti
  • Cecile Valsecchi

Best Publications

  • Handbook of Molecular Descriptors

    Roberto Todeschini;Viviana Consonni

  • QSAR Modeling: Where have you been? Where are you going to?

    Artem Cherkasov;Eugene N. Muratov;Eugene N. Muratov;Denis Fourches;Alexandre Varnek

  • Molecular Descriptors for Chemoinformatics : Volume I : Alphabetical Listing / Volume II : Appendices, References

    Roberto Todeschini;Viviana Consonni

  • Classification tools in chemistry. Part 1: linear models. PLS-DA

    Davide Ballabio;Viviana Consonni

  • Molecular descriptors for chemoinformatics

    Roberto Todeschini;Viviana Consonni

  • DRAGON SOFTWARE: AN EASY APPROACH TO MOLECULAR DESCRIPTOR CALCULATIONS

    Andrea Mauri;Viviana Consonni;Manuela Pavan;Roberto Todeschini

  • Comparison of different approaches to define the applicability domain of QSAR models.

    F Sahigara;K Mansouri;D Ballabio;A Mauri

  • Structure/Response Correlations and Similarity/Diversity Analysis by GETAWAY Descriptors. 1. Theory of the Novel 3D Molecular Descriptors

    Viviana Consonni;Roberto Todeschini;Manuela Pavan

  • Evaluation of model predictive ability by external validation techniques

    Viviana Consonni;Davide Ballabio;Roberto Todeschini

  • Structure/Response Correlations and Similarity/Diversity Analysis by GETAWAY Descriptors. 2. Application of the Novel 3D Molecular Descriptors to QSAR/QSPR Studies

    Viviana Consonni;Roberto Todeschini;Manuela Pavan;Paola Gramatica

  • Quantitative structure-activity relationship models for ready biodegradability of chemicals.

    Kamel Mansouri;Tine Ringsted;Davide Ballabio;Roberto Todeschini

  • Similarity coefficients for binary chemoinformatics data: overview and extended comparison using simulated and real data sets.

    Roberto Todeschini;Viviana Consonni;Hua Xiang;John D. Holliday

  • The K correlation index: theory development and its application in chemometrics

    R. Todeschini;V. Consonni;A. Maiocchi

  • Modelling and prediction of soil sorption coefficients of non-ionic organic pesticides by molecular descriptors

    P Gramatica;M Corradi;V Consonni

  • Detecting bad regression models: multicriteria fitness functions in regression analysis

    Roberto Todeschini;Viviana Consonni;Andrea Mauri;Manuela Pavan

  • CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity.

    Kamel Mansouri;Nicole Kleinstreuer;Ahmed M. Abdelaziz;Domenico Alberga

  • Particle size, chemical composition, seasons of the year and urban, rural or remote site origins as determinants of biological effects of particulate matter on pulmonary cells.

    M.G. Perrone;Maurizio Gualtieri;V. Consonni;L. Ferrero

  • The Kohonen and CP-ANN toolbox: A collection of MATLAB modules for Self Organizing Maps and Counterpropagation Artificial Neural Networks

    Davide Ballabio;Viviana Consonni;Roberto Todeschini

  • CATMoS: Collaborative Acute Toxicity Modeling Suite.

    Kamel Mansouri;Agnes L. Karmaus;Jeremy Fitzpatrick;Grace Patlewicz

  • Defining a novel k-nearest neighbours approach to assess the applicability domain of a QSAR model for reliable predictions

    Faizan Sahigara;Davide Ballabio;Roberto Todeschini;Viviana Consonni

  • A novel variable reduction method adapted from space-filling designs

    Davide Ballabio;Viviana Consonni;Andrea Mauri;Magalie Claeys-Bruno

  • Genetic Algorithms for architecture optimisation of Counter-Propagation Artificial Neural Networks

    Davide Ballabio;Mahdi Vasighi;Viviana Consonni;Mohsen Kompany-Zareh

  • MobyDigs: software for regression and classification models by genetic algorithms

    Roberto Todeschini;Viviana Consonni;Andrea Mauri;Manuela Pavan

  • In Silico Prediction of Cytochrome P450-Drug Interaction: QSARs for CYP3A4 and CYP2C9

    Serena Nembri;Francesca Grisoni;Viviana Consonni;Roberto Todeschini

  • Machine Learning Consensus To Predict the Binding to the Androgen Receptor within the CoMPARA Project.

    Francesca Grisoni;Viviana Consonni;Davide Ballabio

  • Integrated QSAR Models to Predict Acute Oral Systemic Toxicity

    Davide Ballabio;Francesca Grisoni;Viviana Consonni;Roberto Todeschini

  • A QSTR-based expert system to predict sweetness of molecules

    Cristian Rojas;Roberto Todeschini;Davide Ballabio;Andrea Mauri

  • CAIMAN (Classification and Influence Matrix Analysis) : A new approach to the classification based on leverage-scaled functions

    R. Todeschini;D. Ballabio;V. Consonni;A. Mauri

  • Steric Control of Conductivity in Highly Conjugated Polythiophenes

    T. Benincori;V. Consonni;P. Gramatica;T. Pilati

  • Resolution of mixtures of three nonsteroidal anti-inflammatory drugs by fluorescence using partial least squares multivariate calibration with previous wavelength selection by Kohonen artificial neural networks.

    L F Capitán-Vallvey;N Navas;M Del Olmo;V Consonni

  • Prediction of aromatic amines mutagenicity from theoretical molecular descriptors.

    P Gramatica;V Consonni;M Pavan

Frequent Co-Authors

Roberto Todeschini
Roberto Todeschini University of Milano-Bicocca
Davide Ballabio
Davide Ballabio University of Milano-Bicocca
Eugene N. Muratov
Eugene N. Muratov University of North Carolina at Chapel Hill
Alexandre Varnek
Alexandre Varnek University of Strasbourg
Denis Fourches
Denis Fourches North Carolina State University
Igor V. Tetko
Igor V. Tetko Helmholtz Zentrum München
Alexander Tropsha
Alexander Tropsha University of North Carolina at Chapel Hill
Sean Ekins
Sean Ekins University of Arizona
Thomas Hartung
Thomas Hartung Johns Hopkins University

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