A Bayesian derived network of breast pathology co-occurrence.

In this paper, we present the validation and verification of a machine-learning based Bayesian network of breast pathology co-occurrence. The present/not present occurrences of 29 common breast pathologies from 1631 pathology reports were used to build the network. All pathology reports were developed by a single pathologist.

Co-occurrence analysis for discovery of novel breast cancer pathology patterns.

To discover novel patterns in pathology co-occurrence, we have developed algorithms to analyze and visualize pathology co-occurrence. With access to a database of pathology reports, collected under a single protocol and reviewed by a single pathologist, we can conduct an analysis greater in its scope than previous studies looking at breast pathology co-occurrence. Because this data set is unique, specialized methods for pathology co-occurrence analysis and visualization are developed.

Growth cone pathfinding: a competition between deterministic and stochastic events.

Background: Growth cone migratory patterns show evidence of both deterministic and stochastic search modes.

Results: We quantitatively examine how these two different migration modes affect the growth cone's pathfinding response, by simulating growth cone contact with a repulsive cue and measuring the resultant turn angle. We develop a dimensionless number, we call the determinism ratio Psi, to define the ratio of deterministic to stochastic influences driving the growth cone's migration in response to an external guidance cue.