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 under Markov assumptions. Suitable maximum likelihood algorithms for learning the parameters and the structure of the D-HRF are presented. The D-HRF inherits the computational efficiency and the modeling capabilities of HRFs, subsuming both dynamic Bayesian networks and Markov random fields. The behavior of the D-HRF is first evaluated empirically on synthetic data drawn from probabilistic distributions having known form. Then, D-HRFs (combined with a recurrent autoencoder) are successfully applied to the prediction of the disulfide-bonding state of cysteines from the primary structure of proteins in the Protein Data Bank.

Bongini, M., Freno, A., Laveglia, V., Trentin, E. (2018). Dynamic hybrid random fields for the probabilistic graphical modeling of sequential data: definitions, algorithms, and an application to bioinformatics. NEURAL PROCESSING LETTERS, 48(2), 733-768 [10.1007/s11063-017-9730-3].

Dynamic hybrid random fields for the probabilistic graphical modeling of sequential data: definitions, algorithms, and an application to bioinformatics

Freno, A.;Trentin, E.
2018-01-01

Abstract

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 under Markov assumptions. Suitable maximum likelihood algorithms for learning the parameters and the structure of the D-HRF are presented. The D-HRF inherits the computational efficiency and the modeling capabilities of HRFs, subsuming both dynamic Bayesian networks and Markov random fields. The behavior of the D-HRF is first evaluated empirically on synthetic data drawn from probabilistic distributions having known form. Then, D-HRFs (combined with a recurrent autoencoder) are successfully applied to the prediction of the disulfide-bonding state of cysteines from the primary structure of proteins in the Protein Data Bank.
2018
Bongini, M., Freno, A., Laveglia, V., Trentin, E. (2018). Dynamic hybrid random fields for the probabilistic graphical modeling of sequential data: definitions, algorithms, and an application to bioinformatics. NEURAL PROCESSING LETTERS, 48(2), 733-768 [10.1007/s11063-017-9730-3].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11365/1034831