Estimating Dependency Structure as a Hidden Variable

This paper introduces a probability model, the mixture of trees that can account for sparse, dynamically changing dependence relationships. We present a family of efficient algorithms that use EMand the Minimum Spanning Tree algorithm to find the ML and MAP mixtureof trees for a variety of priors, including the Dirichlet and the MDL priors.

Estimating Dependency Structure as a Hidden Variable

This paper introduces a probability model, the mixture of trees that can account for sparse, dynamically changing dependence relationships. We present a family of efficient algorithms that use EM and the Minimum Spanning Tree algorithm to find the ML and MAP mixture of trees for a variety of priors, including the Dirichlet and the MDL priors. We also show that the single tree classifier acts like an implicit feature selector, thus making the classification performance insensitive to irrelevant attributes. Experimental results demonstrate the excellent performance of the new model both in density estimation and in classification.

Matching Interest Points Using Projective Invariant Concentric Circles

We present a new method to perform reliable matching between different images. This method exploits a projective invariant property between concentric circles and the corresponding projected ellipses to find complete region correspondences centered on interest points. The method matches interest points allowing for a full perspective transformation and exploiting all the available luminance information in the regions. Experiments have been conducted on many different data sets to compare our approach to SIFT local descriptors. The results show the new method offers increased robustness to partial visibility, object rotation in depth, and viewpoint angle change.

Variability in the Stability and Productivity of Transfected Genes in Chinese Hamster Ovary (CHO) cells

In the field of biologics production, productivity and stability of the transfected gene of interest are two very important attributes that dictate if a production process is viable. To further understand and improve these two traits, we would need to further our understanding of the factors affecting them. These would include integration site of the gene, gene copy number, cell phenotypic variation and cell environment. As these factors play different parts in the development process, they lead to variable productivity and stability of the transfected gene between clones, the well-known phenomenon of “clonal variation”. A study of this phenomenon and how the various factors contribute to it will thus shed light on strategies to improve productivity and stability in the production cell line. Of the four factors, the site of gene integration appears to be one of the most important. Hence, it is proposed that work is done on studying how different integration sites affect the productivity and stability of transfected genes in the development process. For the study to be more industrially relevant, it is proposed that the Chinese Hamster Ovary dhfr-deficient cell line, CHO-DG44, is used as the model system.