We introduce a probabilistic generative model for protein localization, and develop a system based on it—which we call MDLoc—that utilizes inter-dependencies among locations to predict multiple locations for proteins. The model captures location inter-dependencies using Bayesian networks and represents dependency between features and locations using a mixture model. Our results, obtained by using MDLoc, significantly improve upon results obtained by an initial simpler classifier, as well as on results reported by other top systems.
We then introduce the notion of association rules for multi-valued attributes. The association rules for multi-valued attributes are integrated in building a novel directed hypergraph-based model for databases that allows to capture attribute-level associations and their strength. We present an association-based classifier that can be used to predict values of attributes. We demonstrate the effectiveness of our proposed model through experiments on a financial time series data set (S&P 500).
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