Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Vincenzo Laveglia"'
Autor:
Vincenzo Laveglia, Edmondo Trentin
Publikováno v:
Entropy, Vol 25, Iss 5, p 733 (2023)
A major issue in the application of deep learning is the definition of a proper architecture for the learning machine at hand, in such a way that the model is neither excessively large (which results in overfitting the training data) nor too small (w
Externí odkaz:
https://doaj.org/article/e2f23612ba3d47eaa8c4acb7149e7ad3
Autor:
Kevin Haubrich, Marco Fragai, Linda Cerofolini, Andrea Giachetti, Vincenzo Laveglia, Antonio Rosato, Alessio Ciulli
Publikováno v:
Andrea Giachetti
Journal of Chemical Information and Modeling
Journal of Chemical Information and Modeling
Nuclear magnetic resonance (NMR) is an effective, commonly used experimental approach to screen small organic molecules against a protein target. A very popular method consists of monitoring the changes of the NMR chemical shifts of the protein nucle
Publikováno v:
Journal of inorganic biochemistry. 238
Metalloproteins are ubiquitous in all kingdoms of life. Their role and function are tightly related to the local structure of the metal-binding site. In this regard, the MetalPDB database is an invaluable tool since it stores the 3D structure of meta
Publikováno v:
SSRN Electronic Journal.
Autor:
Edmondo Trentin, Vincenzo Laveglia
Publikováno v:
Artificial Neural Networks in Pattern Recognition ISBN: 9783319999777
ANNPR
ANNPR
Target propagation in deep neural networks aims at improving the learning process by determining target outputs for the hidden layers of the network. To date, this has been accomplished via gradient-descent or relying on autoassociative networks appl
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::24f53b335f79323c5462302908aee2e6
https://doi.org/10.1007/978-3-319-99978-4_6
https://doi.org/10.1007/978-3-319-99978-4_6
The paper introduces a dynamic extension of the hybrid random field (HRF), called dynamic HRF (D-HRF). The D-HRF is aimed at the probabilistic graphical modeling of arbitrary-length sequences of sets of (time-dependent) discrete random variables unde
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4bd6305dae88248055b8934ba0bb6bea
http://hdl.handle.net/11365/1034831
http://hdl.handle.net/11365/1034831
Publikováno v:
Artificial Neural Networks in Pattern Recognition ISBN: 9783319461816
ANNPR
ANNPR
Cysteines in a protein have a tendency to form mutual disulfide bonds. This affects the secondary and tertiary structure of the protein. Therefore, automatic prediction of the bonding state of cysteines from the primary structure of proteins has long
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b0f27b18ffdb1ccb178f547106008c98
http://hdl.handle.net/11365/1007284
http://hdl.handle.net/11365/1007284
Publikováno v:
Journal of Chemical Information and Modeling. 62(12):2951-2960
Thirty-eight percent of protein structures in the Protein Data Bank contain at least one metal ion. However, not all these metal sites are biologically relevant. Cations present as impurities during sample preparation or in the crystallization buffer