Todeschini, R., Consonni, V., Ballabio, D., Grisoni, F. (2020) Chemometrics for QSAR Modeling. In Comprehensive Chemometrics (Second Edition), S. Brown, R. Tauler, B. Walczak (Eds.), Elsevier [link]
Category Archives: Scientific publications
New chapter on distance and similarity measures
Here a new chapter just published in the Encyclopedia of Analytical Chemistry (Wiley) on distance and simialrity measures for chemical data: Todeschini, R., Ballabio, D. and Consonni, V. (2020). Distances and Similarity Measures in Chemometrics and Chemoinformatics. In Encyclopedia of Analytical Chemistry, R.A. Meyers (Ed.) [link]
Consensus to enanche QSAR modelling: a large-scale case study
Our latest publication on data fusion, consensus and QSAR is available on line: Valsecchi, C., Grisoni, F., Consonni, V., Ballabio, D. (2020), Consensus versus individual QSARs in classification: comparison on a large-scale case study, Journal of chemical information and modeling, 60, 1215-1223 [link]
CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity
This publication was the outcome of a great collaborative project for the identification of Androgen Receptor Activity through machine learning and consensus analysis, check it out: Mansouri, K. et al. (2020) CoMPARA : Collaborative Modeling Project for Androgen Receptor Activity. Environmental Health Perspectives, 128, 027002 [link]
Geographical identification of Chianti red wines
This scientific publication was the outcome of a long collaboration with Ruffino, check it out: Bronzi, B., Brilli, C., Beone, G.M., Fontanella, M.C., Ballabio, D., Todeschini, R., Consonni, V., Grisoni, F., Parri, F., Buscema, M. (2020), Geographical identification of Chianti red wine based on ICP-MS element composition, Food Chemistry, 315, 126248 [link]
Deep Ranking Analysis by Power Eigenvectors (DRAPE)
New publication: Deep Ranking Analysis by Power Eigenvectors (DRAPE): A wizard for ranking and multi-criteria decision making, have a look here!
Machine Learning to predict the binding to the Androgen Receptor
Have a look to our latest publication: Grisoni, F., Consonni, V., Ballabio, D. (2019) Machine Learning Consensus to Predict the Binding to the Androgen Receptor within the CoMPARA project. Journal of chemical information and modeling, 59, 1839-1848 [link]
Data related to these models are available for download.
High-level data fusion
New chapter: Recent advances in High-Level Fusion Methods to classify multiple analytical chemical data (published in in Data Fusion Methodology and Applications, Elsevier).
Have a look!!!
New Michem publication on misleading use of regression metrics
Glad our publication is now availale: On the misleading use of Q2F3 for QSAR model comparison, Molecular Informatics (2019), 38, 1800029. Have a look!
Structural alerts for the identification of bioaccumulative compounds
Now publushed on Integrated Environmental Assessment and Management a paper on structural alerts for the identification of bioaccumulative compounds, check it out here. Data and a KNIME workflow to apply the proposed decision system based on structural alerts are available for download.