Softening Constraints in Constraint-Based Protein Topology Prediction.

Proceedings of the Twentieth International Conference on Machine Learning, 1995

Volume

Proceedings of the Twentieth International Conference on Machine Learning, 1995

Contents

This paper is concerned with the handling of uncertain data about the applicability of constraints in protein topology prediction. It discusses the use of novel methods of representing and reasoning with uncertain data, and presents the results of some experiments in using these methods to build probabilistic models of constraint application. It thus builds on work by other authors in both constraint satisfaction and probabilistic reasoning.