Simple and Effective Visual Models for Gene Expression Cancer Diagnostics

In the paper we show that diagnostic classes in cancer gene expression data sets, which most often include thousands of features (genes), may be effectively separated with simple two-dimensional plots such as scatterplot and radviz graph. The principal innovation proposed in the paper is a method called VizRank, which is able to score and identify the best among possibly millions of candidate projections for visualizations. Compared to recently much applied techniques in the field of cancer genomics that include neural networks, support vector machines and various ensemble-based approaches, VizRank is fast and finds visualization models that can be easily examined and interpreted by domain experts. Our experiments on a number of gene expression data sets show that VizRank was always able to find data visualizations with a small number of (two to seven) genes and excellent class separation. In addition to providing grounds for gene expression cancer diagnosis, VizRank and its visualizations also identify small sets of relevant genes, uncover interesting gene interactions and point to outliers and potential misclassifications in cancer data sets

VizRank: Data Visualization Guided by Machine Learning

Data visualization plays a crucial role in identifying interesting patterns in exploratory data analysis. Its use is, however, made difficult by the large number of possible data projections showing different attribute subsets that must be evaluated by the data analyst. In this paper, we introduce a method called VizRank, which is applied on classified data to automatically select the most useful data projections. VizRank can be used with any visualization method that maps attribute values to points in a two-dimensional visualization space. It assesses possible data projections and ranks them by their ability to visually discriminate between classes. The quality of class separation is estimated by computing the predictive accuracy of k-nearest neighbor classifier on the data set consisting of x and y positions of the projected data points and their class information. The paper introduces the method and presents experimental results which show that VizRank's ranking of projections highly agrees with subjective rankings by data analysts. The practical use of VizRank is also demonstrated by an application in the field of functional genomics

Double transverse spin asymmetry in the p↑pˉ↑p^\uparrow\bar{p}^\uparrow Drell-Yan process from Sivers functions

We show that the transverse double spin asymmetry (DSA) in the Drell-Yan process contributed only from the Sivers functions can be picked out by the weighting function $\frac{Q_T}{M^2}(\cos(\phi-\phi_{S_1})\cos(\phi-\phi_{S_2})+3\sin(\phi-\phi_{S_1})\sin(\phi-\phi_{S_2}))$. The asymmetry is proportional to the product of two Sivers functions from each hadron $f_{1T}^{\perp(1)}\times f_{1T}^{\perp (1)}$. Using two sets of Sivers functions extracted from the semi-inclusive deeply elastic scattering data at HERMES, we estimate this asymmetry in the $p^\uparrow\bar{p}^\uparrow$ Drell-Yan process which is possible to be performed in HESR at GSI. The prediction of DSA in the Drell-Yan process contributed by the function g_{1T}(x,\Vec k_T^2), which can be extracted by the weighting function $\frac{Q_T}{M^2}(3\cos(\phi-\phi_{S_1})\cos(\phi-\phi_{S_2})+\sin(\phi-\phi_{S_1})\sin(\phi-\phi_{S_2}))$, is also given at GSI.Comment: 6 latex pages, 2 figures, to appear in PR

Measurement of setting process of cement pastes using non-destructive ultrasonic shear wave reflection technique

In this paper a new setup for measuring, setting and hardening process of cementitious materials, using a non-destructive ultrasonic shear wave reflection technique and design with the objective to be easily used in-situ, is described. Using the developed setup, the measurements can be performed by slight deepening of a measuring head into a paste in a mold or by placing the paste into a mold fixed on a measuring head. To test the proposed methodology, cement pastes with different compositions were prepared and exposed to different curing temperatures. Significant differences in the evolution of a change of a shear wave reflection coefficient Δr in time were observed, indicating the ability of the method to monitor setting process of cement pastes. Moreover, some interesting phenomena in the solidification process of the materials can be identified. A linear relationship between development of Δr and penetration resistance dP values in time was found, allowing development of a simplified procedure to determine both initial and final setting times of the material

Genetic differentiation of the indigenous Norway Spruce (Picea abies (L.) Karst) populations in Slovenia investigated by means of isoenzyme gene markers

Raziskava obravnava genetsko diferenciacijo 22 populacij smreke (Picea abies (L.) Karst.) z njenih naravnih rastišč v Sloveniji in 4 na Hrvaškem z analizo izoencimskih genskih označevalcev. Razlike med populacijami smo ocenili z genetskimi razdaljami po Gregoriusu (1974) za 15 polimorfnih genskih lokusov. Vrednosti genetskih razdalj se gibljejo med 0,021 in 0,073 (v Sloveniji do 0,063). Izoencimska genetska diferenciranost smreke je razmeroma majhna. Rezultati hierarhične klasifikacije nakazujejo geografsko odvisno združevanjepopulacij v dve skupini: alpsko skupino s Trnovskim gozdom in osrednje dinarsko skupino. Skupini se na območju Snežnika tudi prekrivata.Genetic differentiation of 22 indigenous Norway spruce (Picea abies (L.) Karst) populations from Slovenia and 4 from Croatia has been investigated by means of isoenzyme gene markers. The degree of differentiation among populations has been measured with genetic distances proposed by Gregorius (1974) for 15 polymorphic gene loci. Multilocus estimates of genetic distancesrange between 0.021 and 0.073 (in Slovenia up to 0.063). Norway spruce populations did not show a strong genetic differentiation by isozymes. The results of hierarchical classification indicated a geographically dependent pooling of populations into two distinct groups: Alpine group including Trnovski gozd and Central Dinaric group, with the two groups overlapping in the Snežnik area