We develop and test new machine learning methods for the predic- tion of topological representations of protein structures in the form of coarse- or fine-grained contact or distance maps that are transla- tion and rotation invariant. The methods are based on generalized input-output hidden Markov models (GIOHMMs) and generalized recursive neural networks (GRNNs). The methods are used to pre- dict topology directly in the fine-grained case and, in the coarse- grained case, indirectly by first learning how to score candidate graphs and then using the scoring function to search the space of possible configurations. Computer simulations show that the pre- dictors achieve state-of-the-art performance.

Prediction of Protein Topologies Using GIOHMMs and GRNNs / Pollastri, Gianluca; Baldi, Pierre ; Vullo, Alessandro; Frasconi, Paolo. - STAMPA. - (2003), pp. 1473-1480. (Intervento presentato al convegno Neural Information Processing Systems nel 2002).

Prediction of Protein Topologies Using GIOHMMs and GRNNs

FRASCONI, PAOLO
2003

Abstract

We develop and test new machine learning methods for the predic- tion of topological representations of protein structures in the form of coarse- or fine-grained contact or distance maps that are transla- tion and rotation invariant. The methods are based on generalized input-output hidden Markov models (GIOHMMs) and generalized recursive neural networks (GRNNs). The methods are used to pre- dict topology directly in the fine-grained case and, in the coarse- grained case, indirectly by first learning how to score candidate graphs and then using the scoring function to search the space of possible configurations. Computer simulations show that the pre- dictors achieve state-of-the-art performance.
2003
Advances in Neural Information Processing Systems 15
Neural Information Processing Systems
2002
Pollastri, Gianluca; Baldi, Pierre ; Vullo, Alessandro; Frasconi, Paolo
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/394600
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