Differential expression of microRNAs in bovine papillomavirus type 1 transformed equine cells

Bovine papillomavirus (BPV) types 1 and 2 play an important role in the pathogenesis of equine sarcoids (ES), the most common cutaneous tumour affecting horses. MicroRNAs (miRNAs), small non-coding RNAs that regulate essential biological and cellular processes, have been found dysregulated in a wide range of tumours. The aim of this study was to identify miRNAs associated with ES. Differential expression of miRNAs was assessed in control equine fibroblasts (EqPalFs) and EqPalFs transformed with the BPV-1 genome (S6-2 cells). Using a commercially available miRNA microarray, 492 mature miRNAs were interrogated. In total, 206 mature miRNAs were differentially expressed in EqPalFs compared with S6-2 cells. Aberrant expression of these miRNAs in S6-2 cells can be attributed to the presence of BPV-1 genomes. Furthermore, we confirm the presence of 124 miRNAs previously computationally predicted in the horse. Our data supports the involvement of miRNAs in the pathogenesis of ES

Individual Factors Affecting Entrepreneurship in Hispanics

A model of entrepreneurship (Baron & Henry, 2011) is used to understand and explain the factors related to the behaviors of Hispanic entrepreneurs. Testable hypotheses to guide future research are presented

Large-scale Nonlinear Variable Selection via Kernel Random Features

We propose a new method for input variable selection in nonlinear regression. The method is embedded into a kernel regression machine that can model general nonlinear functions, not being a priori limited to additive models. This is the first kernel-based variable selection method applicable to large datasets. It sidesteps the typical poor scaling properties of kernel methods by mapping the inputs into a relatively low-dimensional space of random features. The algorithm discovers the variables relevant for the regression task together with learning the prediction model through learning the appropriate nonlinear random feature maps. We demonstrate the outstanding performance of our method on a set of large-scale synthetic and real datasets.Comment: Final version for proceedings of ECML/PKDD 201

Determining appropriate approaches for using data in feature selection

Feature selection is increasingly important in data analysis and machine learning in big data era. However, how to use the data in feature selection, i.e. using either ALL or PART of a dataset, has become a serious and tricky issue. Whilst the conventional practice of using all the data in feature selection may lead to selection bias, using part of the data may, on the other hand, lead to underestimating the relevant features under some conditions. This paper investigates these two strategies systematically in terms of reliability and effectiveness, and then determines their suitability for datasets with different characteristics. The reliability is measured by the Average Tanimoto Index and the Inter-method Average Tanimoto Index, and the effectiveness is measured by the mean generalisation accuracy of classification. The computational experiments are carried out on ten real-world benchmark datasets and fourteen synthetic datasets. The synthetic datasets are generated with a pre-set number of relevant features and varied numbers of irrelevant features and instances, and added with different levels of noise. The results indicate that the PART approach is more effective in reducing the bias when the size of a dataset is small but starts to lose its advantage as the dataset size increases