Emmanuel Cruz brilliantly defended his PhD thesis ‘Geochemical Study of Pyrite Persistence in Sedimentary Records’ at the Università degli Studi di Milano-Bicocca. Emmanuel undertook an impressive interdisciplinary effort, bridging two fields that often speak different scientific “languages.” He facilitated their interaction by developing new analytical methods and applying techniques such as RAMAN imaging, Design of Experiments (DoE), and chemometrics to investigate chemical factors linked to climate change. To top it all off, Emmanuel earned his PhD in Chemical, Geological, and Environmental Sciences with honors! The PhD thesis is available for downlaod here: https://michem.unimib.it/download/phd-thesis/
News
Condensed phase membrane introduction mass spectrometry
Condensed phase membrane introduction mass spectrometry: A new frontier for the real-time monitoring of hazardous chemical migration from food contact materials:check the manuscript (open access) at the following link: https://doi.org/10.1016/j.greeac.2024.100199
Multivariate comparison of cluster validity indices
Cluster validity indices (CVIs) are used to detect a reliable number of clusters. We revised and evaluated 68 validity indices for crisp clustering by comparison on 21 real and simulated datasets. Have a look to the paper: https://doi.org/10.1016/j.chemolab.2024.105117
Molecular fingerprints for exploring the chemical space of natural products
We evaluated the effectiveness of multiple types of fingerprints for representing the chemical space of natural substances. The code and data to reproduce the results are also available in the study: Boldini, D., Ballabio, D., Consonni, V., Todeschini, R., Grisoni, F., Sieber, S.A. (2024) Effectiveness of molecular fingerprints for exploring the chemical space of natural products, Journal of Cheminformatics 16, 35 (2024), https://doi.org/10.1186/s13321-024-00830-3
A new metric to assess the degree of accuracy of consensus predictions
We proposed a new heuristic metric to assess the degree of accuracy of consensus predictions. It can assist the mapping of reliability in prediction and enhance the delineation of a safe zone, where consensus predictions are expected to have better accuracy. All details are available in the following publication, have a look! We also provide data and code to calculate it, here!
V. Consonni, R. Todeschini, M. Orlandi, D. Ballabio (2024) Kernel-based mapping of reliability in predictions for consensus modelling, Chemometrics and Intelligent Laboratory Systems 246, 105085, https://doi.org/10.1016/j.chemolab.2024.105085
Lecture of prof. Todeschini at the Istituto Lombardo – Accademia di Scienze e Lettere
Thursday 9 November 2023 at the Istituto Lombardo – Accademia di Scienze e Lettere in Milan prof. Todeschini was invited to hold a conference on the scientific activity of his great-grandfather prof. Wilhelm Körner entitled “Wilhelm Körner: dalla molecola alla struttura molecolare”. Slides are available at this link.
Which is the effect of different molecular fingerprints for exploring the chemical space?
Which is the effect of different types of molecular fingerprints for exploring the chemical space of natural products? Have a look here: https://doi.org/10.26434/chemrxiv-2023-0m355
Classification toolbox: new release!
Random Forest has been added as classification method in the latest release of the Classification toolbox for MATLAB. The toolbox is available for download at this link: https://michem.unimib.it/download/matlab-toolboxes/classification-toolbox-for-matlab/
Chemometrics to predict the taste of molecules
We have published a comprehensive review on classification-based chemoemtric approaches to predict taste of molecules, have a look!
Rojas, C., Ballabio, D., Consonni, V., Suárez-Estrella, D., Todeschini, R. (2023) Classification-based machine learning approaches to predict the taste of molecules: a review. Food Research International, 171, 113036 [link]
Kohonen and CPANN toolbox: new release!
A new release of the Kohonen and CPANN toolbox (4.6) is now available. In this release, GPU and CPU calculation has been improved. The Kohonen and CPANN toolbox for MATLAB is a collection of MATLAB modules for training Kohonen Maps (Self Organising Maps, SOMs), Counterpropagation Artificial Neural networs (CPANNs), Supervised Kohonen networks (SKN), XY-fused networks (XY-F). It can be downloaded here: https://michem.unimib.it/download/matlab-toolboxes/kohonen-and-cpann-toolbox-for-matlab/