SpotOn is a robust algorithm developed to identify and classify the interfacial residues as Hot-Spots (HS) and Null-Spots (NS) with a final accuracy of 0.95 and a sensitivity of 0.95 on an independent test set.


SPOTONE is a new Machine-Learning (ML) predictor able to accurately classify protein Hot-Spots (HS) via sequence-only features. This algorithm shows an accuracy, AUROC, precision, recall and F1-score of 0.82, 0.83, 0.91, 0.82 and 0.85, respectively, in an independent testing set.

Machine Learning

mc This 3 day course will introduce participants to the machine learning taxonomy and the applications of common machine learning algorithms to omics data. The course will cover the common methods being used to analyse different omics data sets by providing a practical context through the use of basic but widely used R libraries.


WebSpecmine is a web-based application designed to perform the analysis of metabolomics data based on spectroscopic and chromatographic techniques (NMR, Infrared, UV-visible, and Raman, and LC/GC-MS)) and compound concentrations.