18453149
Using Gene Clustering to Identify Discriminatory Genes with Higher Classification Accuracy
A single DNA microarray measures thousands to tens of thousands of gene expression levels, but experimental datasets normally consist of much fewer such arrays, typically in tens to hundreds, taken over a selection of tissue samples. The biological interpretation of these data relies on identifying subsets of induced or repressed genes that can be used to discriminate various categories of tissue, to provide experimental evidence for connections between a subset of genes and the tissue pathology. A variety of methods can be used to identify discriminatory gene subsets, which can be ranked by classification accuracy. But the high dimensionality of the gene expression space, coupled with relatively fewer tissue samples, creates the dimensionality problem: gene subsets that are too large to provide convincing evidence for any plausible causal connection between that gene subset and the tissue pathology. We propose a new gene selection method, clustered gene selection (CGS) which, when coupled with existing methods, can identify gene subsets that overcome the dimensionality problem and improve classification accuracy. Experiments on eight real datasets showed that CGS can identify many more cancer related genes and clearly improve classification accuracy, compared with three other non-CGS based gene selection methods
11252523
Selecting Genes with Dissimilar Discrimination Strength for Sample Class Prediction
them all in classication is largely redundant. Furthermore, these selected genes can prevent the consideration of other individually-less but collectively-more dieren tially expressed genes. We propose to cluster genes in terms of their class discrimination strength and to limit the number of selected genes per cluster. By combining this idea with several existing single gene scoring methods, we show by experiments on two cancer microarray datasets that our methods identify gene subsets which collectively have signican tly higher classication accuracies.
5133584
Xenopus sonic hedgehog as a potential morphogen during embryogenesis and thyroid hormone-dependent metamorphosis
The hedgehog family of proteins have been implicated as important signaling molecules in establishing cell positional information and tissue patterning. Here we present the cloning and characterization of a hedgehog homologue from Xenopus laevis similar to the sonic class of vertebrate hedgehog genes. We isolated Xenopus hedgehog (Xhh) from a subtractive hybridization screen designed to identify genes induced by thyroid hormone during metamorphosis of the X.laevis gastrointestinal tract. In the intestine, Xhh mRNA expression was up-regulated at the climax of metamorphosis (stage 62) when intestinal epithelium underwent morphogenesis. Treatment of pre-metamorphic tadpoles with exogenous thyroid hormone (TH) resulted in a similar pattern of Xhh induction. Furthermore, TH induction was resistant to inhibitors of protein synthesis suggesting that Xhh is a direct thyroid hormone response gene. The expression and TH regulation of Xhh was not limited to the intestine, but was also observed in the limb and a mixture of pancreas and stomach. Throughout development, Xhh mRNA was present at varying levels with the earliest expression being detected at neurula stage. The highest levels of Xhh were observed between stages 33 and 40 shortly before tadpole feeding begins. Whole mount in situ hybridization analysis of Xhh expression in pre-hatching, stage 32 tadpoles demonstrated staining in the notochord and floor plate similar to that observed for other vertebrate hedgehog genes. Together, these data suggest a putative role for Xhh in organ development during both amphibian embryogenesis and metamorphosis.
36645601
Dynamic tomography using curve evolution with spatial-temporal regularization
We develop a variational method for dynamic tomography using curve evolution with spatial-temporal regularization. We take into account both the dynamics of the object boundaries as well as the dynamics of region intensities. These quantities are jointly estimated through minimization of an appropriately defined energy function. The temporal correlation of the object boundary curves are included through a weighted area difference distance measure between curves. The intensity dynamics are captured through a simple autoregressive model. Curve evolution methods are used to solve for the optimal reconstruction, which are implemented with efficient level set techniques. The strength of this approach is demonstrated through the successful reconstruction of an object sequence based on observations of only a single projection angle over time.
8067500
UMBC at TREC 12
We present the results of UMBC’s participation in the Web and Nov- elty tracks. We explored various heuristics-based link analysis approaches to the Topic Distillation task. For the novelty task we tried several methods for exploit- ing semantic information of sentences based on the SVD technique. We used SVD to expand the query and to filter redundant sentences. We also used a clus- tering algorithm that is also based on SVD.
13717616
Quantum Complexities of Ordered Searching, Sorting, and Element Distinctness 


Abstract. We consider the quantum complexities of the following three problems: searching an ordered list, sorting an un-ordered list, and deciding whether the numbers in a list are all distinct. Letting N be the number of elements in the input list, we prove a lower bound of (1/π )(ln(N )-1) accesses to the list elements for ordered searching, a lower bound of Ω(N logN ) binary comparisons for sorting, and a lower bound of $\Omega(\sqrt{N}\log{N})$ binary comparisons for element distinctness. The previously best known lower bounds are 1/12 log2(N) - O (1) due to Ambainis, Ω(N) , and $\Omega(\sqrt{N})$ , respectively. Our proofs are based on a weighted all-pairs inner product argument. In addition to our lower bound results, we give an exact quantum algorithm for ordered searching using roughly 0.631 log2(N) oracle accesses. Our algorithm uses a quantum routine for traversing through a binary search tree faster than classically, and it is of a nature very different {from} a faster exact algorithm due to Farhi, Goldstone, Gutmann, and Sipser. 
