Sparse regulatory networks

In many organisms the expression levels of each gene are controlled by the activation levels of known "Transcription Factors" (TF). A problem of considerable interest is that of estimating the "Transcription Regulation Networks" (TRN) relating the TFs and genes. While the expression levels of genes can be observed, the activation levels of the corresponding TFs are usually unknown, greatly increasing the difficulty of the problem. Based on previous experimental work, it is often the case that partial information about the TRN is available. For example, certain TFs may be known to regulate a given gene or in other cases a connection may be predicted with a certain probability. In general, the biology of the problem indicates there will be very few connections between TFs and genes. Several methods have been proposed for estimating TRNs. However, they all suffer from problems such as unrealistic assumptions about prior knowledge of the network structure or computational limitations. We propose a new approach that can directly utilize prior information about the network structure in conjunction with observed gene expression data to estimate the TRN. Our approach uses $L_1$ penalties on the network to ensure a sparse structure. This has the advantage of being computationally efficient as well as making many fewer assumptions about the network structure. We use our methodology to construct the TRN for E. coli and show that the estimate is biologically sensible and compares favorably with previous estimates.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS350 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Recent Advances and New Perspectives in Surgery of Renal Cell Carcinoma

Renal cell carcinoma (RCC) is one of the most common types of cancer in the urogenital system. For localized renal cell carcinoma, nephron-sparing surgery (NSS) is becoming the optimal choice because of its advantage in preserving renal function. Traditionally, partial nephrectomy is performed with renal pedicle clamping to decrease blood loss. Furthermore, both renal pedicle clamping and the subsequent warm renal ischemia time affect renal function and increase the risk of postoperative renal failure. More recently, there has also been increasing interest in creating surgical methods to meet the requirements of nephron preservation and shorten the renal warm ischemia time including assisted or unassisted zero-ischemia surgery. As artificial intelligence increasingly integrates with surgery, the three-dimensional visualization technology of renal vasculature is applied in the NSS to guide surgeons. In addition, the renal carcinoma complexity scoring system is also constantly updated to guide clinicians in the selection of appropriate treatments for patients individually. In this article, we provide an overview of recent advances and new perspectives in NSS

Sparse Model Identification for High Dimensional Data.

In this thesis, several methods are proposed to construct sparse models in different situations with the special structures of group variable selection, partial correlation estimation and transcript regulation network construction. Traditional variable selection methods do not consider the special structures or cannot directly be applied to these situations. The methods proposed in this thesis are extensions and improvements of the Lasso method done by considering the special structures of different problems. These methods can be applied to high-dimensional data where the number of parameters are much larger than the number of observations. Simulation studies and real data analysis showed that these methods performed favorably, compared with some other recently developed models.Ph.D.StatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/63691/1/nfzhou_1.pd