Proposing the deep dynamic Bayesian network as a future computer based medical system

The development of new learning models has been of great importance throughout recent years, with a focus on creating advances in the area of deep learning. Deep learning was first noted in 2006, and has since become a major area of research in a number of disciplines. This paper will delve into the area of deep learning to present its current limitations and provide a new idea for a fully integrated deep and dynamic probabilistic system. The new model will be applicable to a vast number of areas initially focusing on applications into medical image analysis with an overall goal of utilising this approach for prediction purposes in computer based medical systems.

Efficient Learning of Bayesian Networks with Bounded Tree-width

Learning Bayesian networks with bounded tree-width has attracted much attention recently, because low tree-width allows exact inference to be performed efficiently. Some existing methods \cite{korhonen2exact, nie2014advances} tackle the problem by using $k$-trees to learn the optimal Bayesian network with tree-width up to $k$. Finding the best $k$-tree, however, is computationally intractable. In this paper, we propose a sampling method to efficiently find representative $k$-trees by introducing an informative score function to characterize the quality of a $k$-tree. To further improve the quality of the $k$-trees, we propose a probabilistic hill climbing approach that locally refines the sampled $k$-trees. The proposed algorithm can efficiently learn a quality Bayesian network with tree-width at most $k$. Experimental results demonstrate that our approach is more computationally efficient than the exact methods with comparable accuracy, and outperforms most existing approximate methods.

Learning Treewidth-Bounded Bayesian Networks with Thousands of Variables

We present a method for learning treewidth-bounded Bayesian networks from data sets containing thousands of variables. Bounding the treewidth of a Bayesian network greatly reduces the complexity of inferences. Yet, being a global property of the graph, it considerably increases the difficulty of the learning process. Our novel algorithm accomplishes this task, scaling both to large domains and to large treewidths. Our novel approach consistently outperforms the state of the art on experiments with up to thousands of variables.

CTCF modulates Estrogen Receptor function through specific chromatin and nuclear matrix interactions

Enhancer regions and transcription start sites of estrogen-target regulated genes are connected by means of Estrogen Receptor long-range chromatin interactions. Yet, the complete molecular mechanisms controlling the transcriptional output of engaged enhancers and subsequent activation of coding genes remain elusive. Here, we report that CTCF binding to enhancer RNAs is enriched when breast cancer cells are stimulated with estrogen. CTCF binding to enhancer regions results in modulation of estrogen-induced gene transcription by preventing Estrogen Receptor chromatin binding and by hindering the formation of additional enhancer-promoter ER looping. Furthermore, the depletion of CTCF facilitates the expression of target genes associated with cell division and increases the rate of breast cancer cell proliferation. We have also uncovered a genomic network connecting loci enriched in cell cycle regulator genes to nuclear lamina that mediates the CTCF function. The nuclear lamina and chromatin interactions are regulated by estrogen-ER. We have observed that the chromatin loops formed when cells are treated with estrogen establish contacts with the nuclear lamina. Once there, the portion of CTCF associated with the nuclear lamina interacts with enhancer regions, limiting the formation of ER loops and the induction of genes present in the loop. Collectively, our results reveal an important, unanticipated interplay between CTCF and nuclear lamina to control the transcription of ER target genes, which has great implications in the rate of growth of breast cancer cells.

Cell cycle-coupled expansion of AR activity promotes cancer progression

The androgen receptor (AR) is required for prostate cancer (PCa) survival and progression, and ablation of AR activity is the first line of therapeutic intervention for disseminated disease. While initially effective, recurrent tumors ultimately arise for which there is no durable cure. Despite the dependence of PCa on AR activity throughout the course of disease, delineation of the AR-dependent transcriptional network that governs disease progression remains elusive, and the function of AR in mitotically active cells is not well understood. Analyzing AR activity as a function of cell cycle revealed an unexpected and highly expanded repertoire of AR-regulated gene networks in actively cycling cells. New AR functions segregated into two major clusters: those that are specific to cycling cells and retained throughout the mitotic cell cycle ('Cell Cycle Common'), versus those that were specifically enriched in a subset of cell cycle phases ('Phase Restricted'). Further analyses identified previously unrecognized AR functions in major pathways associated with clinical PCa progression. Illustrating the impact of these unmasked AR-driven pathways, dihydroceramide desaturase 1 was identified as an AR-regulated gene in mitotically active cells that promoted pro-metastatic phenotypes, and in advanced PCa proved to be highly associated with development of metastases, recurrence after therapeutic intervention and reduced overall survival. Taken together, these findings delineate AR function in mitotically active tumor cells, thus providing critical insight into the molecular basis by which AR promotes development of lethal PCa and nominate new avenues for therapeutic intervention.