Structure Learning Constrained by Node-Specific Degree Distribution

Jianzhu Ma TTIC, Qingming Tang TTIC, Sheng Wang TTIC, Feng Zhao TTIC, Jinbo Xu TTIC
Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:299-307, 2015.

Abstract

We consider the problem of learning the structure of a Markov Random Field (MRF) when the node-specific degree distribution is provided. The problem setting is inspired by protein contact map prediction in which residue-specific contact number distribution can be estimated without predicting individual contacts beforehand. We replace the widely used l_1 regularization with a node-specific regularization derived from the predicted degree distribution and optimize the objective function using an Iterative Maximum Cost Bipartite Matching algorithm. When a node is predicted to have k edges, its largest k regularization coefficients are reduced, promoting appearance of k edges for that node. We predict node-specific degree distribution using multiple 2nd-order Conditional Neural Fields integrating both local and global information of a protein. Experimental results show that for protein contact prediction our approach yields a significant accuracy improvement when the predicted contact number is reasonably good.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR13-ttic15a, title = {Structure Learning Constrained by Node-Specific Degree Distribution}, author = {TTIC, Jianzhu Ma and TTIC, Qingming Tang and TTIC, Sheng Wang and TTIC, Feng Zhao and TTIC, Jinbo Xu}, booktitle = {Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence}, pages = {299--307}, year = {2015}, editor = {Meila, Marina and Heskes, Tom}, volume = {R13}, series = {Proceedings of Machine Learning Research}, month = {12--16 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r13/main/assets/ttic15a/ttic15a.pdf}, url = {https://proceedings.mlr.press/r13/ttic15a.html}, abstract = {We consider the problem of learning the structure of a Markov Random Field (MRF) when the node-specific degree distribution is provided. The problem setting is inspired by protein contact map prediction in which residue-specific contact number distribution can be estimated without predicting individual contacts beforehand. We replace the widely used l_1 regularization with a node-specific regularization derived from the predicted degree distribution and optimize the objective function using an Iterative Maximum Cost Bipartite Matching algorithm. When a node is predicted to have k edges, its largest k regularization coefficients are reduced, promoting appearance of k edges for that node. We predict node-specific degree distribution using multiple 2nd-order Conditional Neural Fields integrating both local and global information of a protein. Experimental results show that for protein contact prediction our approach yields a significant accuracy improvement when the predicted contact number is reasonably good.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Structure Learning Constrained by Node-Specific Degree Distribution %A Jianzhu Ma TTIC %A Qingming Tang TTIC %A Sheng Wang TTIC %A Feng Zhao TTIC %A Jinbo Xu TTIC %B Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2015 %E Marina Meila %E Tom Heskes %F pmlr-vR13-ttic15a %I PMLR %P 299--307 %U https://proceedings.mlr.press/r13/ttic15a.html %V R13 %X We consider the problem of learning the structure of a Markov Random Field (MRF) when the node-specific degree distribution is provided. The problem setting is inspired by protein contact map prediction in which residue-specific contact number distribution can be estimated without predicting individual contacts beforehand. We replace the widely used l_1 regularization with a node-specific regularization derived from the predicted degree distribution and optimize the objective function using an Iterative Maximum Cost Bipartite Matching algorithm. When a node is predicted to have k edges, its largest k regularization coefficients are reduced, promoting appearance of k edges for that node. We predict node-specific degree distribution using multiple 2nd-order Conditional Neural Fields integrating both local and global information of a protein. Experimental results show that for protein contact prediction our approach yields a significant accuracy improvement when the predicted contact number is reasonably good. %Z Reissued by PMLR on 04 October 2026.
APA
TTIC, J.M., TTIC, Q.T., TTIC, S.W., TTIC, F.Z. & TTIC, J.X.. (2015). Structure Learning Constrained by Node-Specific Degree Distribution. Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R13:299-307 Available from https://proceedings.mlr.press/r13/ttic15a.html. Reissued by PMLR on 04 October 2026.

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