This paper describes the system that we designed for solving the “Learning Language in Logic” Challenge task 2005 (LLL’05) concerning the extraction of directed genic interactions from sentences in Medline abstracts. We see this task as a classification problem: the system must separate the interacting pairs of genes/proteins from the non-interacting ones, and it achieves this by learning a mod...

Relational Bayesian networks are an extension of the method of probabilistic model construction by Bayes-ian networks. They deene probability distributions on nite relational structures by conditioning the probability of a ground atom r(a 1 ; : : : ; a n) on rst-order properties of a 1 ; : : : ; a n that have been established by previous random decisions. In this paper we investigate from a nit...

The goal of network clustering algorithms detect dense clusters in a network, and provide a first step towards the understanding of large scale biological networks. With numerous recent advances in biotechnologies, large-scale genetic interactions are widely available, but there is a limited understanding of which clustering algorithms may be most effective. In order to address this problem, we...

Credal networks are graph-based statistical models whose parameters take values in a set, instead of being sharply specified as in traditional statistical models (e.g., Bayesian networks). The computational complexity of inferences on such models depends on the irrelevance/independence concept adopted. In this paper, we study inferential complexity under the concepts of epistemic irrelevance an...

Prior work has shown that context-specific independence (CSI) in Bayes networks can be exploited to speed up belief updating. We examine how networks with variables exhibiting mutual exclusion (e.g. “selector variables”), as well as CSI, can be efficiently updated. In particular, singlyconnected networks, that have an additional common selector variable, can be updated in linear time, where qua...

The nodes awareness information efficiently distributed is one of the core features for wireless sensor networks. This paper systematically researched some key problems about the reliable data transmission, which is closely related with each other and may directly affect the transmission effectiveness and energy consumption. The main research of this paper is as follows: Mobile Agent (MA) is su...

Graphical models—especially probabilistic networks like Bayes networks and Markov networks—are very popular to make reasoning in highdimensional domains feasible. Since constructing them manually can be tedious and time consuming, a large part of recent research has been devoted to learning them from data. However, if the dataset to learn from contains imprecise information in the form of sets ...