Výsledky vyhledávání - "Deriu, Marco A."

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Explainable Machine Learning and Deep Learning Models for Predicting TAS2R-Bitter Molecule Interactions

Autor: Ferri, Francesco, Cannariato, Marco, Pallante, Lorenzo, Zizzi, Eric A., Deriu, Marco A.

This work aims to develop explainable models to predict the interactions between bitter molecules and TAS2Rs via traditional machine-learning and deep-learning methods starting from experimentally validated data. Bitterness is one of the five basic t

Externí odkaz: http://arxiv.org/abs/2406.15039

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LightCPPgen: An Explainable Machine Learning Pipeline for Rational Design of Cell Penetrating Peptides

Autor: Maroni, Gabriele, Stojceski, Filip, Pallante, Lorenzo, Deriu, Marco A., Piga, Dario, Grasso, Gianvito

Cell-penetrating peptides (CPPs) are powerful vectors for the intracellular delivery of a diverse array of therapeutic molecules. Despite their potential, the rational design of CPPs remains a challenging task that often requires extensive experiment

Externí odkaz: http://arxiv.org/abs/2406.01617

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Dataset of Fluorescence Spectra and Chemical Parameters of Olive Oils

Autor: Venturini, Francesca, Sperti, Michela, Michelucci, Umberto, Gucciardi, Arnaud, Martos, Vanessa M., Deriu, Marco A.

This dataset encompasses fluorescence spectra and chemical parameters of 24 olive oil samples from the 2019-2020 harvest provided by the producer Conde de Benalua, Granada, Spain. The oils are characterized by different qualities: 10 extra virgin oli

Externí odkaz: http://arxiv.org/abs/2301.04471

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Physico-chemical properties extraction from the fluorescence spectrum with 1D-convolutional neural networks: application to olive oil

Autor: Venturini, Francesca, Sperti, Michela, Michelucci, Umberto, Gucciardi, Arnaud, Martose, Vanessa M., Deriu, Marco A.

The olive oil sector produces a substantial impact in the Mediterranean's economy and lifestyle. Many studies exist which try to optimize the different steps in the olive oil's production process. One of the main challenges for olive oil producers is

Externí odkaz: http://arxiv.org/abs/2203.07229

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A Model-Agnostic Algorithm for Bayes Error Determination in Binary Classification

Autor: Michelucci, Umberto, Sperti, Michela, Piga, Dario, Venturini, Francesca, Deriu, Marco A.

This paper presents the intrinsic limit determination algorithm (ILD Algorithm), a novel technique to determine the best possible performance, measured in terms of the AUC (area under the ROC curve) and accuracy, that can be obtained from a specific

Externí odkaz: http://arxiv.org/abs/2107.11609

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