|The Regression toolbox (for MATLAB) is a collection of MATLAB modules for calculating regression multivariate models: Ordinary Least Squares (OLS), Partial Least Squares (PLS), Principal Component Regression (PCR), Ridge regression, local regression based on K Nearest Neighbours (KNN) and Binned Nearest Neighbours (BNN) approaches, and variable selection approaches (All Subset Models, Forward selection, Genetic Algorithms and Reshaped Sequential Replacement).
The toolbox is freely available and can be downloaded here
The manuscript about our latest partecipation in the CATMoS collaborative modelling project to predict Acute Oral Toxicity is not out, have a look: CATMoS: Collaborative Acute Toxicity Modeling Suite, Environmental Health Perspectives, 129, 47013 [link]
Special Issue “UHPLC-MS/MS Methods for the Identification of Emerging Contaminant Transformation Products in Surface Water”, editor: Fabio Gosetti, have a look here if interested: https://www.mdpi.com/journal/separations/special_issues/UHPLC_Water
We welcome Leonardo Fedrigotti, Martina Barbagallo and Davide crucitti, who will start their master thesis on chemometrics related topics. In particular, Martina will develop and validate proper chemometrics strategies based on advanced neural networks and deep-learning models to predict the molecular structure of some substances starting from their LC-MS/MS spectra, which were previously organized in a database. Davide will work in the field of QSAR, developing a model to classify substrates, inhibitors and non-active compounds of P-Glycoprotein using artificial neural networks. His project will be carried out at the Slovenian National Institute of Chemistry. Leonardo will develop a model which is able to couple GC-MS data and features of encephalographic responses (EEG) measured with portable low-cost devices to assess odor-stimulated emotions and to ensure scientific measurements of such signals. Coupling and comparison of analytical sources will be carried out through chemometrics methods. This project will be carried out at P&G (Bruxelles).
Congratulations to Pietro Bertani for the “Premio Giorgio Squinzi” for his master thesis “A multivariate approach at the thermodynamic properties of polyamino polycarboxylic complexes with paramagnetic and other endogenous metal ions” (which was developed in collabroation with Bracco) and congratulations also to his supervisors Roberto Todeschini and Alessandro Maiocchi.
NURA (curated NUclear Receptor Activity dataset) is a curated dataset of nuclear receptor modulators. It contains bioactivity annotations for 15,206 molecules and 11 selected Nuclear Receptors (NRs) obtained by integrating and curating data from toxicological and pharmacological databases. The data can be dowloaded at Zenodo: https://doi.org/10.5281/zenodo.3991561; details on this dataset can be found here: Valsecchi, C., Grisoni, F., Motta, S., Bonati, L., Ballabio, D. (2020) NURA: a curated dataset of nuclear receptor modulators, Toxicology and Applied Pharmacology, 407, 115244 [link]
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]
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]
New Matlab toolbox available for the calculation of high level data fusion (consensus) approaches (majority voting and Bayes consensus with discrete probability distributions): https://michem.unimib.it/download/data/bayes-and-majority-voting-consensus-for-matlab/
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]