A Bayesian nonlinear support vector machine error correction model

The use of linear error correction models based on stationarity and cointegration analysis, typically estimated with least squares regression, is a common technique for financial time series prediction. In this paper, the same formulation is extended to a nonlinear error correction model using the idea of a kernel‐based implicit nonlinear mapping to a high‐dimensional feature space in which linear model formulations are specified. Practical expressions for the nonlinear regression are obtained in terms of the positive definite kernel function by solving a linear system. The nonlinear least squares support vector machine model is designed within the Bayesian evidence framework that allows us to find appropriate trade‐offs between model complexity and in‐sample model accuracy. From straightforward primal–dual reasoning, the Bayesian framework allows us to derive error bars on the prediction in a similar way as for linear models and to perform hyperparameter and input selection. Starting from the results of the linear modelling analysis, the Bayesian kernel‐based prediction is successfully applied to out‐of‐sample prediction of an aggregated equity price index for the European chemical sector. Copyright © 2006 John Wiley & Sons, Ltd.     

Nonsense-mediated decay microarray analysis identifies mutations of EPHB2 in human prostate cancer

The identification of tumor-suppressor genes in solid tumors by classical cancer genetics methods is difficult and slow. We combined nonsense-mediated RNA decay microarrays and array-based comparative genomic hybridization for the genome-wide identification of genes with biallelic inactivation involving nonsense mutations and loss of the wild-type allele. This approach enabled us to identify previously unknown mutations in the receptor tyrosine kinase gene EPHB2. The DU 145 prostate cancer cell line, originating from a brain metastasis, carries a truncating mutation of EPHB2 and a deletion of the remaining allele. Additional frameshift, splice site, missense and nonsense mutations are present in clinical prostate cancer samples. Transfection of DU 145 cells, which lack functional EphB2, with wild-type EPHB2 suppresses clonogenic growth. Taken together with studies indicating that EphB2 may have an essential role in cell migration and maintenance of normal tissue architecture, our findings suggest that mutational inactivation of EPHB2 may be important in the progression and metastasis of prostate cancer.     

Mutagenic insertion and chromosome engineering resource (MICER)

Embryonic stem cell technology revolutionized biology by providing a means to assess mammalian gene function in vivo. Although it is now routine to generate mice from embryonic stem cells, one of the principal methods used to create mutations, gene targeting, is a cumbersome process. Here we describe the indexing of 93,960 ready-made insertional targeting vectors from two libraries. 5,925 of these vectors can be used directly to inactivate genes with an average targeting efficiency of 28%. Combinations of vectors from the two libraries can be used to disrupt both alleles of a gene or engineer larger genomic changes such as deletions, duplications, translocations or inversions. These indexed vectors constitute a public resource (Mutagenic Insertion and Chromosome Engineering Resource; MICER) for high-throughput, targeted manipulation of the mouse genome.