In this new release, batch training has been optimised and now it is faster than previous versions (computational time has been decreased on about 60%). RMSE (squared residuals between sampels and weights of winning neurons) and the average changing of the topological distance between previous and updated winning neurons can be plotted as a function of epochs during training.
The toolbox can be downloaded here.
News
Prof. Todeschini awarded with Medaglia Canneri
Durante l’Assemblea dei Soci della Divisione di Chimica Analiticadella Società Chimica Italiana è stata conferita al Prof. Roberto Todeschini la Medaglia Canneri in riconoscimento del suo contributo allo sviluppo e alla divulgazione della chemiometria.
QSAR models to predict Acute Oral Toxicity
Our latest publication on QSAR models to predict Acute Oral Toxicity: D. Ballabio, F. Grisoni, V. Consonni, R. Todeschini (2019), Integrated QSAR models to predict acute oral systemic toxicity, Molecular Informatics, 38, 1800124 [link]. Data and code to reproduce models availbale here!
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!
Collaboration with Azuay is now official!
We had a collaboration with Univerisity of Azuay since the late ’80. Today, we participated to the cerimony for the official signature of the Memorandum of Understanding between our University and the Univerisity of Azuay (Cuenca, Equador), represented by its Rector, Prof. Francisco Salgado Arteaga.
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 release of our MATLAB toolboxes
we would like to announce that new versions of our toolboxes for MATLAB have been released. A graphical user interface (GUI), which allows an easy model calculation and analysis of results, is provided with all the toolboxes.
The Classification toolbox for MATLAB is a collection of MATLAB modules for calculating classification (supervised pattern recognition) multivariate models.
In this new release, Backpropagation Neural Networks (BPNN) was added as classification method and the maximum number of classes that can be loaded (and modelled) has been extended to 20.
The toolbox can be downloaded here.
The Kohonen and CPANN toolbox for MATLAB is a collection of MATLAB modules for training Kohonen Maps (Self Organising Maps, SOMs) and supervised SOMs.
In this new release, the maximum number of classes that can be loaded (and modelled) has been extended to 20. Numerical results related to the position of samples in the top map and net weights have been added to the results menu.
The toolbox can be downloaded here.
The PCA toolbox for MATLAB is a collection of MATLAB modules for calculating unsupervised multivariate models (PCA, MDS and Cluster Analysis).
In this new release, the maximum number of classes that can be loaded has been extended to 20.
The toolbox can be downloaded here.
Finally, have a look to our data repository where you can freely download available data.
Introduzione alla chemiometria
E’ disponibile in formato pdf Introduzione alla Chemiometria di R. Todeschini, precedentemente pubblicato da Edises Napoli
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!