A novel application of quantile regression for identification of biomarkers exemplified by equine cartilage microarray data

<p>Abstract</p> <p>Background</p> <p>Identification of biomarkers among thousands of genes arrayed for disease classification has been the subject of considerable research in recent years. These studies have focused on disease classification, comparing experimental groups of effected to normal patients. Related experiments can be done to identify tissue-restricted biomarkers, genes with a high level of expression in one tissue compared to other tissue types in the body.</p> <p>Results</p> <p>In this study, cartilage was compared with ten other body tissues using a two color array experimental design. Thirty-seven probe sets were identified as cartilage biomarkers. Of these, 13 (35%) have existing annotation associated with cartilage including several well-established cartilage biomarkers. These genes comprise a useful database from which novel targets for cartilage biology research can be selected. We determined cartilage specific Z-scores based on the observed M to classify genes with Z-scores ≥ 1.96 in all ten cartilage/tissue comparisons as cartilage-specific genes.</p> <p>Conclusion</p> <p>Quantile regression is a promising method for the analysis of two color array experiments that compare multiple samples in the absence of biological replicates, thereby limiting quantifiable error. We used a nonparametric approach to reveal the relationship between percentiles of M and A, where M is log₂(R/G) and A is 0.5 log₂(RG) with R representing the gene expression level in cartilage and G representing the gene expression level in one of the other 10 tissues. Then we performed linear quantile regression to identify genes with a cartilage-restricted pattern of expression.</p

Molecular modeling of temperature dependence of solubility parameters for amorphous polymers

A molecular modeling strategy is proposed to describe the temperature (T) dependence of solubility parameter (δ) for the amorphous polymers which exhibit glass-rubber transition behavior. The commercial forcefield “COMPASS” is used to support the atomistic simulations of the polymer. The temperature dependence behavior of δ for the polymer is modeled by running molecular dynamics (MD) simulation at temperatures ranging from 250 up to 650 K. Comparing the MD predicted δ value at 298 K and the glass transition temperature (Tg) of the polymer determined from δ–T curve with the experimental value confirm the accuracy of our method. The MD modeled relationship between δ and T agrees well with the previous theoretical works. We also observe the specific volume (v), cohesive energy (Ucoh), cohesive energy density (ECED) and δ shows a similar temperature dependence characteristics and a drastic change around the Tg. Meanwhile, the applications of δ and its temperature dependence property are addressed and discussed