Finding maximum edge bicliques in convex bipartite graphs

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 εA, vertices adjacent to v are consecutive in B. A complete bipartite subgraph of a graph G is called a biclique of G. In this paper, we study the problem of finding the maximum edge-cardinality biclique in convex bipartite graphs. Given a bipartite graph G=(A, B, E) which is convex on B, we present a new algorithm that computes the maximum edge-cardinality biclique of G in O(n log3 n loglogn) time and O(n) space, where n=|A|. This improves the current O(n 2) time bound available for the problem

Finding maximum edge bicliques in convex bipartite graphs

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(nlog3 n log log n) time and O(n) space, where n = |A|. This improves the current O(n 2) 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 ed

Презентация «Завершение Великой Отечественной войны и Второй мировой войн»

<p><b>A</b>: Proteins interact at different levels, from the low level of stable complex cores to the high level of temporarily interacting complexes. The different interaction types lead to different protein similarity levels in the context of the IP/MS data. Proteins of complex cores have a high similarity, while proteins of higher interaction levels have a lower similarity to each other. <b>B</b>: Two independent protein assemblies (depicted as green and yellow) and how they split in lower interaction levels. Protein complexes at different interaction levels can have the same similarity level. The clusters from a clustering method at one level (left) can represent complexes of different types for this reason, and it is unclear what each cluster represents. Our strategy (right) captures complexes at different similarity levels for this reason and creates trees that allow for predicting the interaction level.</p

Assessment of a method to characterize antibody selectivity and specificity for use in immunoprecipitation

Antibodies are used in multiple cell biology applications, but there are no standardized methods to assess antibody quality—an absence that risks data integrity and reproducibility. We describe a mass spectrometry–based standard operating procedure for scoring immunoprecipitation antibody quality. We quantified the abundance of all the proteins in immunoprecipitates of 1,124 new recombinant antibodies for 152 chromatin-related human proteins by comparing normalized spectral abundance factors from the target antigen with those of all other proteins. We validated the performance of the standard operating procedure in blinded studies in five independent laboratories. Antibodies for which the target antigen or a member of its known protein complex was the most abundant protein were classified as 'IP gold standard'. This method generates quantitative outputs that can be stored and archived in public databases, and it represents a step toward a platform for community benchmarking of antibody quality