Prediction of Protein Topologies Using Generalized IOHMMs and RNNs
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 (cid:12)ne-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 (cid:12)ne-grained case and, in the coarse- grained case, indirectly by (cid:12)rst learning how to score candidate graphs and then using the scoring function to search the space of possible con(cid:12)gurations. Computer simulations show that the pre- dictors achieve state-of-the-art performance.
Cite
Text
Pollastri et al. "Prediction of Protein Topologies Using Generalized IOHMMs and RNNs." Neural Information Processing Systems, 2002.Markdown
[Pollastri et al. "Prediction of Protein Topologies Using Generalized IOHMMs and RNNs." Neural Information Processing Systems, 2002.](https://mlanthology.org/neurips/2002/pollastri2002neurips-prediction/)BibTeX
@inproceedings{pollastri2002neurips-prediction,
title = {{Prediction of Protein Topologies Using Generalized IOHMMs and RNNs}},
author = {Pollastri, Gianluca and Baldi, Pierre and Vullo, Alessandro and Frasconi, Paolo},
booktitle = {Neural Information Processing Systems},
year = {2002},
pages = {1473-1480},
url = {https://mlanthology.org/neurips/2002/pollastri2002neurips-prediction/}
}