Improving stability of prediction models based on correlated omics data by using network approaches

Building prediction models based on complex omics datasets such as transcriptomics, proteomics, metabolomics remains a challenge in bioinformatics and biostatistics. Regularized regression techniques are typically used to deal with the high dimensionality of these datasets. However, due to the presence of correlation in the datasets, it is difficult to select the best model and application of these methods yields unstable results. We propose a novel strategy for model selection where the obtained models also perform well in terms of overall predictability. Several three step approaches are considered, where the steps are 1) network construction, 2) clustering to empirically derive modules or pathways, and 3) building a prediction model incorporating the information on the modules. For the first step, we use weighted correlation networks and Gaussian graphical modelling. Identification of groups of features is performed by hierarchical clustering. The grouping information is included in the prediction model by using group-based variable selection or group-specific penalization. We compare the performance of our new approaches with standard regularized regression via simulations. Based on these results we provide recommendations for selecting a strategy for building a prediction model given the specific goal of the analysis and the sizes of the datasets. Finally we illustrate the advantages of our approach by application of the methodology to two problems, namely prediction of body mass index in the DIetary, Lifestyle, and Genetic determinants of Obesity and Metabolic syndrome study (DILGOM) and prediction of response of each breast cancer cell line to treatment with specific drugs using a breast cancer cell lines pharmacogenomics dataset

Variation in imperfections level due to winding of ring yarn

290-294The variation in imperfections level due to winding of carded and combed ring yarns and the effect of foreign matters, such as black specks of broken seeds, lead bits and trashes present in the ring cop yarn, on imperfections during winding process for carded cotton ring yarn has been studied. The results show that the thin places increase, and thick places and neps decrease due to the fall of foreign matters during winding in the carded yarn. In the combed yarn, the thin places, thick places and neps increase during windin

High T<SUB>c</SUB> copper-oxide superconductors of thallium, bismuth and lead

A brief survey of compositions and structures of high-temperature copper oxide superconductors containing thallium, bismuth and lead is presented. All these compounds possess CuO2 sheets but not Cu[sbnd]O chains. Another structural feature common to all these compounds is that the part of the structure between nonconsecutive CuO2 sheets is ill-defined. Possible correlations of Tc with the structure and bonding of these compounds are pointed out