Markov random field modeling in computer vision
Markov random field modeling in computer vision
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Information-Theoretic Definition of Similarity
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
A tight bound on approximating arbitrary metrics by tree metrics
Proceedings of the thirty-fifth annual ACM symposium on Theory of computing
FOCS '99 Proceedings of the 40th Annual Symposium on Foundations of Computer Science
Automatic retrieval and clustering of similar words
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
The Hardness of Metric Labeling
FOCS '04 Proceedings of the 45th Annual IEEE Symposium on Foundations of Computer Science
A Linear Programming Formulation and Approximation Algorithms for the Metric Labeling Problem
SIAM Journal on Discrete Mathematics
Hierarchical multi-label prediction of gene function
Bioinformatics
Approximate Labeling via Graph Cuts Based on Linear Programming
IEEE Transactions on Pattern Analysis and Machine Intelligence
Seeing the forest for the trees
Bioinformatics
MAP estimation of semi-metric MRFs via hierarchical graph cuts
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Minimizing Energies with Hierarchical Costs
International Journal of Computer Vision
Consensus strategy for clustering using RC-images
Pattern Recognition
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Computational techniques have been successful at predicting protein function from relational data (functional or physical interactions). These techniques have been used to generate hypotheses and to direct experimental validation. With few exceptions, the task is modeled as multi-label classification problems where the labels (functions) are treated independently or semi-independently. However, databases such as the Gene Ontology provide information about the similarities between functions. We explore the use of the Metric Labeling combinatorial optimization problem to make use of heuristically computed distances between functions to make more accurate predictions of protein function in networks derived from both physical interactions and a combination of other data types. To do this, we give a new technique (based on convex optimization) for converting heuristic semimetric distances into a metric with minimum least-squared distortion (LSD). The Metric Labeling approach is shown to outperform five existing techniques for inferring function from networks. These results suggest Metric Labeling is useful for protein function prediction, and that LSD minimization can help solve the problem of converting heuristic distances to a metric