Active subnetwork recovery with a mechanism-dependent scoring function; with application to angiogenesis and organogenesis studies.

Background: The learning active subnetworks problem involves finding subnetworks of a bio-molecular network that are active in a particular condition. Many approaches integrate observation data (e.g.

Faster graphical models for point-pattern matching.

It has been shown that isometric matching problems can be solved exactly in polynomial time, by means of a Junction Tree with small maximal clique size. Recently, an iterative algorithm was presented which converges to the same solution an order of magnitude faster. Here, we build on both of these ideas to produce an algorithm with the same asymptotic running time as the iterative solution, but which requires only a single iteration of belief propagation.

Learning graph matching.

As a fundamental problem in pattern recognition, graph matching has applications in a variety of fields, from computer vision to computational biology. In graph matching, patterns are modeled as graphs and pattern recognition amounts to finding a correspondence between the nodes of different graphs. Many formulations of this problem can be cast in general as a quadratic assignment problem, where a linear term in the objective function encodes node compatibility and a quadratic term encodes edge compatibility.

Graph rigidity, cyclic belief propagation, and point pattern matching.

A recent paper [1] proposed a provably optimal polynomial time method for performing near-isometric point pattern matching by means of exact probabilistic inference in a chordal graphical model. Its fundamental result is that the chordal graph in question is shown to be globally rigid, implying that exact inference provides the same matching solution as exact inference in a complete graphical model. This implies that the algorithm is optimal when there is no noise in the point patterns.

Inferring differential leukocyte activity from antibody microarrays using a latent variable model.

Recent development of cluster of differentiation (CD) antibody arrays has enabled expression levels of many leukocyte surface CD antigens to be monitored simultaneously. Such membrane-proteome surveys have provided a powerful means to detect changes in leukocyte activity in various human diseases, such as cancer and cardiovascular diseases. The challenge is to devise a computational method to infer differential leukocyte activity among multiple biological states based on antigen expression profiles.

Graphical models and point pattern matching.

This paper describes a novel solution to the rigid point pattern matching problem in Euclidean spaces of any dimension. Although we assume rigid motion, jitter is allowed. We present a noniterative, polynomial time algorithm that is guaranteed to find an optimal solution for the noiseless case.