In this paper we propose a forensic analysis system called CISRI that helps forensic investigators determine the most influential members of a criminal group, who are related to known members of the group, for the purposes of investigation. In the CISRI framework, we describe the structural relationships between the members of a criminal group in terms of a graph. In such a graph, a node represents a member of a criminal group, an edge connecting two nodes represents the relationship between two members of the group, and the weight of an edge represents the degree of the relationship between those two members. Using this representation, we propose a method that determines the relative importance of nodes in a graph with respect to a given set of query nodes. Most current approaches that study relative importance determine the relative importance of a node under consideration by estimating the contribution of each query node individually to the importance of this node while overlooking the contribution of the query nodes collectively to the importance of the node under consideration. This may lead to results with low precision. CISRI overcomes this limitation by: (1) computing the contribution of the overall set of query nodes to the importance of a node under consideration, and (2) adopting a tight constraint calculation that considers how much each query node contributes to the relative importance of a node under consideration. This leads to accurate identification of nodes in the graph that are important, in relation to the query nodes. In the framework of CISRI, a graph is constructed from mobile communication records (e.g., phone calls and messages), where a node represents a caller and the weight of an edge reflects the number of contacts between two callers. We evaluated the quality of CISRI by comparing it experimentally with three comparable methods. Our results showed marked improvement.
We propose a classifier system called iPFPi that predicts the functions of un-annotated proteins. iPFPi assigns an un-annotated protein P the functions of GO annotation terms that are semantically similar to P. An un-annotated protein P and a GO annotation term T are represented by their characteristics. The characteristics of P are GO terms found within the abstracts of biomedical literature associated with P. The characteristics of Tare GO terms found within the abstracts of biomedical literature associated with the proteins annotated with the function of T. Let F and F/ be the important (dominant) sets of characteristic terms representing T and P, respectively. iPFPi would annotate P with the function of T, if F and F/ are semantically similar. We constructed a novel semantic similarity measure that takes into consideration several factors, such as the dominance degree of each characteristic term t in set F based on its score, which is a value that reflects the dominance status of t relative to other characteristic terms, using pairwise beats and looses procedure. Every time a protein P is annotated with the function of T, iPFPi updates and optimizes the current scores of the characteristic terms for T based on the weights of the characteristic terms for P. Set F will be updated accordingly. Thus, the accuracy of predicting the function of T as the function of subsequent proteins improves. This prediction accuracy keeps improving over time iteratively through the cumulative weights of the characteristic terms representing proteins that are successively annotated with the function of T. We evaluated the quality of iPFPi by comparing it experimentally with two recent protein function prediction systems. Results showed marked improvement.
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