抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Summary. We propose the elastic net, a new regularization and variable selection method. Real world data and a simulation study show that the elastic net often outperforms the lasso, while enjoying a similar sparsity of representation. In addition, the elastic net encourages a grouping effect, where strongly correlated predictors tend to be in or out of the model together.The elastic net is particularly useful when the number of predictors (p) is much bigger than the number of observations (n). By contrast, the lasso is not a very satisfactory variable selection method in the

/pdf/regularization-and-variable-selection-via-the-elastic-net-4z8q6j5o0i.pdf

Regularization and variable selection via the elastic net

Variable and feature selection have become the focus of much research in areas of application for which datasets with tens or hundreds of thousands of variables are available. These areas include text processing of internet documents, gene expression array analysis, and combinatorial chemistry. The objective of variable selection is three-fold: improving the prediction performance of the predictors, providing faster and more cost-effective predictors, and providing a better understanding of the underlying process that generated the data. The contributions of this special issue cover a wide range of aspects of such problems: providing a better definition of the objective function, feature construction, feature ranking, multivariate feature selection, efficient search methods, and feature validity assessment methods.

/pdf/an-introduction-to-variable-and-feature-selection-22eu49r0zd.pdf

An introduction to variable and feature selection

Microarrays can measure the expression of thousands of genes to identify changes in expression between different biological states. Methods are needed to determine the significance of these changes while accounting for the enormous number of genes. We describe a method, Significance Analysis of Microarrays (SAM), that assigns a score to each gene on the basis of change in gene expression relative to the standard deviation of repeated measurements. For genes with scores greater than an adjustable threshold, SAM uses permutations of the repeated measurements to estimate the percentage of genes identified by chance, the false discovery rate (FDR). When the transcriptional response of human cells to ionizing radiation was measured by microarrays, SAM identified 34 genes that changed at least 1.5-fold with an estimated FDR of 12%, compared with FDRs of 60 and 84% by using conventional methods of analysis. Of the 34 genes, 19 were involved in cell cycle regulation and 3 in apoptosis. Surprisingly, four nucleotide excision repair genes were induced, suggesting that this repair pathway for UV-damaged DNA might play a previously unrecognized role in repairing DNA damaged by ionizing radiation.

/pdf/significance-analysis-of-microarrays-applied-to-the-ionizing-10j4beasgw.pdf

Significance analysis of microarrays applied to the ionizing radiation response

The problem of identifying differentially expressed genes in designed microarray experiments is considered. Lonnstedt and Speed (2002) derived an expression for the posterior odds of differential expression in a replicated two-color experiment using a simple hierarchical parametric model. The purpose of this paper is to develop the hierarchical model of Lonnstedt and Speed (2002) into a practical approach for general microarray experiments with arbitrary numbers of treatments and RNA samples. The model is reset in the context of general linear models with arbitrary coefficients and contrasts of interest. The approach applies equally well to both single channel and two color microarray experiments. Consistent, closed form estimators are derived for the hyperparameters in the model. The estimators proposed have robust behavior even for small numbers of arrays and allow for incomplete data arising from spot filtering or spot quality weights. The posterior odds statistic is reformulated in terms of a moderated t-statistic in which posterior residual standard deviations are used in place of ordinary standard deviations. The empirical Bayes approach is equivalent to shrinkage of the estimated sample variances towards a pooled estimate, resulting in far more stable inference when the number of arrays is small. The use of moderated t-statistics has the advantage over the posterior odds that the number of hyperparameters which need to estimated is reduced; in particular, knowledge of the non-null prior for the fold changes are not required. The moderated t-statistic is shown to follow a t-distribution with augmented degrees of freedom. The moderated t inferential approach extends to accommodate tests of composite null hypotheses through the use of moderated F-statistics. The performance of the methods is demonstrated in a simulation study. Results are presented for two publicly available data sets.

/pdf/linear-models-and-empirical-bayes-methods-for-assessing-2yc9s2cjo3.pdf

Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments

We have devised an approach to cancer class prediction from gene expression profiling, based on an enhancement of the simple nearest prototype (centroid) classifier. We shrink the prototypes and hence obtain a classifier that is often more accurate than competing methods. Our method of "nearest shrunken centroids" identifies subsets of genes that best characterize each class. The technique is general and can be used in many other classification problems. To demonstrate its effectiveness, we show that the method was highly efficient in finding genes for classifying small round blue cell tumors and leukemias.

https://www.pnas.org/content/pnas/99/10/6567.full.pdf

Diagnosis of multiple cancer types by shrunken centroids of gene expression

Electric fields can be manipulated by a method in which multiple electrodes are arranged along a closed contour and clamped to predetermined electric potentials. This method may be applied to a broad range of problems in the separation of macromolecules by gel electrophoresis. DNA molecules as large as 2 megabases can be well separated with a contour-clamped homogeneous electric field alternating between two orientations 120 degrees apart. The pattern of separation is independent of position in the gel, which is an advantage over previous methods. DNA less than 50 kilobases can be separated without distortion even at high voltage with a nonalternating contour-clamped homogeneous field. Decreased band broadening in DNA less than 200 bases can be achieved with a contour-clamped inhomogeneous field.

http://science.sciencemag.org/content/sci/234/4783/1582.full.pdf

Separation of large DNA molecules by contour-clamped homogeneous electric fields

A simple and reproducible procedure for the introduction of DNA into mammalian cells by electroporation is described. The parameters involving the cells, the DNA, and the electric field are investigated. The procedure has been applied to a broad range of animal cells. It is capable of transforming more than 1% of the viable cells to the stable expression of a selectable marker.

/pdf/electroporation-for-the-efficient-transfection-of-mammalian-4nexxfhnf2.pdf

Gilbert Chu

Papers

Significance analysis of microarrays applied to the ionizing radiation response

Diagnosis of multiple cancer types by shrunken centroids of gene expression

Separation of large DNA molecules by contour-clamped homogeneous electric fields

Electroporation for the efficient transfection of mammalian cells with DNA.

Cellular responses to cisplatin. The roles of DNA-binding proteins and DNA repair.