Results 1 
7 of
7
Uncovering Genomic Reassortments Among Influenza Strains by Enumerating Maximal Bicliques
"... The evolutionary histories of viral genomes have received significant recent attention due to their importance in understanding virulence and the corresponding ramifications to public health. We present a novel framework to detect reassortment events in influenza based on the comparison of two distr ..."
Abstract

Cited by 11 (2 self)
 Add to MetaCart
(Show Context)
The evolutionary histories of viral genomes have received significant recent attention due to their importance in understanding virulence and the corresponding ramifications to public health. We present a novel framework to detect reassortment events in influenza based on the comparison of two distributions of phylogenetic trees, rather than a pair of, possibly unreliable, consensus trees. We show how to detect all highprobability inconsistencies between two distributions of trees by enumerating maximal bicliques within a defined incompatibility graph. In the process, we give the first quadratic delay algorithm for enumerating maximal bicliques within general bipartite graphs. We demonstrate the utility of our approach by applying it to several sets of influenza genomes (both human and avianhosted) and successfully identify all known reassortment events and a few novel candidate reassortments. In addition, on simulated datasets, our approach correctly finds implanted reassortments and rarely detects reassortments where none were introduced. 1.
1 Computational Systems Biology
"... 1.3 Gene Function Prediction............................ 14 ..."
(Show Context)
Finding Maximum Edge Bicliques in Convex Bipartite Graphs?
"... Abstract. A bipartite graph G = (A,B,E) is convex on B if there exists an ordering of the vertices of B such that for any vertex v ∈ A, vertices adjacent to v are consecutive in B. A complete bipartite subgraph of a graph G is called a biclique of G. Motivated by an application to analyzing DNA mic ..."
Abstract
 Add to MetaCart
Abstract. A bipartite graph G = (A,B,E) is convex on B if there exists an ordering of the vertices of B such that for any vertex v ∈ A, vertices adjacent to v are consecutive in B. A complete bipartite subgraph of a graph G is called a biclique of G. Motivated by an application to analyzing DNA microarray data, we study the problem of finding maximum edge bicliques in convex bipartite graphs. Given a bipartite graph G = (A,B,E) which is convex on B, we present a new algorithm that computes a maximum edge biclique of G in O(n log3 n log log n) time and O(n) space, where n = A. This improves the current O(n2) time bound available for the problem. We also show that for two special subclasses of convex bipartite graphs, namely for biconvex graphs and bipartite permutation graphs, a maximum edge biclique can be computed in O(nα(n)) and O(n) time, respectively, where n = min(A, B) and α(n) is the slowly growing inverse of the Ackermann function. 1
UtilityMaximizing Event Stream Suppression ∗
"... Complex Event Processing (CEP) has emerged as a technology for monitoring event streams in search of user specified event patterns. When a CEP system is deployed in sensitive environments the user may wish to mitigate leaks of private information while ensuring that useful nonsensitive patterns are ..."
Abstract
 Add to MetaCart
Complex Event Processing (CEP) has emerged as a technology for monitoring event streams in search of user specified event patterns. When a CEP system is deployed in sensitive environments the user may wish to mitigate leaks of private information while ensuring that useful nonsensitive patterns are still reported. In this paper we consider how to suppress events in a stream to reduce the disclosure of sensitive patterns while maximizing the detection of nonsensitive patterns. We first formally define the problem of utilitymaximizing event suppression with privacy preferences, and analyze its computational hardness. We then design a suite of realtime solutions to solve this problem. Our first solution optimally solves the problem at the eventtype level. The second solution, at the eventinstance level, further optimizes the eventtype level solution by exploiting runtime event distributions using advanced pattern match cardinality estimation techniques. Our user study and experimental evaluation over both realworld and synthetic event streams show that our algorithms are effective in maximizing utility yet still efficient enough to offer near realtime system responsiveness.