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Background Tag-based techniques, such as SAGE, are commonly used to sample the mRNA pool of an organism's transcriptome. Incomplete digestion during the tag formation process may allow for multiple tags to be generated from a given mRNA transcript. The probability of forming a tag varies with its relative location. As a result, the observed tag counts represent a biased sample of the actual transcript pool. In SAGE this bias can be avoided by ignoring all but the 3' most tag but will discard a large fraction of the observed data. Taking this bias into account should allow more of the available data to be used leading to increased statistical power. Results Three new hierarchical models, which directly embed a model for the variation in tag formation probability, are proposed and their associated Bayesian inference algorithms are developed. These models may be applied to libraries at both the tag and aggregate level. Simulation experiments and analysis of real data are used to contrast the accuracy of the various methods. The consequences of tag formation bias are discussed in the context of testing differential expression. A description is given as to how these algorithms can be applied in that context. Conclusions Several Bayesian inference algorithms that account for tag formation effects are compared with the DPB algorithm providing clear evidence of superior performance. The accuracy of inferences when using a particular non-informative prior is found to depend on the expression level of a given gene. The multivariate nature of the approach easily allows both univariate and joint tests of differential expression. Calculations demonstrate the potential for false positive and negative findings due to variation in tag formation probabilities across samples when testing for differential expression.

Predictive boosted regression tree (BRT) models were developed to predict modulus of rupture (MOR) and internal bond (IB) for a US particleboard manufacturer. The temporal process data consisted of 4,307 records and spanned the time frame from March 2009 to June 2010. This study builds on previous published research by developing BRT models across all product types of MOR and IB produced by the particleboard manufacturer. A total of 189 continuous variables from the process line were used as possible predictor variables. BRT model comparisons were made using the root mean squared error for prediction (RMSEP) and the RMSEP relative to the mean of the response variable as a percent (RMSEP%) for the validation data sets. For MOR, RMSEP values ranged from 1.051 to 1.443 MPa, and RMSEP% values ranged from 8.5 to 11.6 percent. For IB, RMSEP values ranged from 0.074 to 0.108 MPa, and RMSEP% values ranged from 12.7 to 18.6 percent. BRT models for MOR and IB predicted better than respective regression tree models without boosting. For MOR, key predictors in the BRT models were related to “pressing temperature zones,” “thickness of pressing,” and “pressing pressure.” For IB, key predictors in the BRT models were related to “thickness of pressing.” The BRT predictive models offer manufacturers an opportunity to improve the understanding of processes and be more predictive in the outcomes of product quality attributes. This may help manufacturers reduce rework and scrap and also improve production efficiencies by avoiding unnecessarily high operating targets.

We discuss a model selection procedure, the adaptive ridge selector , derived from a hierarchical Bayes argument, which results in a simple and efficient fitting algorithm. The hierarchical model utilized resembles an un-replicated variance components model and leads to weighting of the covariates. We discuss the intuition behind this type estimator and investigate its behavior as a regularized least squares procedure. While related alternatives were recently exploited to simultaneously fit and select variablses/features in regression models (Tipping in J Mach Learn Res 1:211–244, 2001; Figueiredo in IEEE Trans Pattern Anal Mach Intell 25:1150–1159, 2003), the extension presented here shows considerable improvement in model selection accuracy in several important cases. We also compare this estimator’s model selection performance to those offered by the lasso and adaptive lasso solution paths. Under randomized experimentation, we show that a fixed choice of tuning parameter leads to results in terms of model selection accuracy which are superior to the entire solution paths of lasso and adaptive lasso when the underlying model is a sparse one. We provide a robust version of the algorithm which is suitable in cases where outliers may exist.

Zaretzki reviews Applied Categorical and Count Data Analysis by Wan Tang, Hua He, and Xin M. Tu.

Zaretzki reviews Business Intelligence: Data Mining and Optimization for Decision Making by Carlo Vercellis.

Withdrawn due to extensions and submission as another paper.

Extracting biologically meaningful information from the continuing flood of genomic data is a major challenge in the life sciences. Codon usage bias (CUB) is a general feature of most genomes and is thought to reflect the effects of both natural selection for efficient translation and mutation bias. Here we present a mechanistically interpretable, Bayesian model (ROC SEMPPR) to extract biologically meaningful information from patterns of CUB within a genome. ROC SEMPPR, is grounded in population genetics and allows us to separate the contributions of mutational biases and natural selection against translational inefficiency on a gene by gene and codon by codon basis. Until now, the primary disadvantage of similar approaches was the need for genome scale measurements of gene expression. Here we demonstrate that it is possible to both extract accurate estimates of codon specific mutation biases and translational efficiencies while simultaneously generating accurate estimates of gene expression, rather than requiring such information. We demonstrate the utility of ROC SEMPPR using the Saccharomyces cerevisiae S288c genome. When we compare our model fits with previous approaches we observe an exceptionally high agreement between estimates of both codon specific parameters and gene expression levels ( > 0.99 in all cases). We also observe strong agreement between our parameter estimates and those derived from alternative datasets. For example, our estimates of mutation bias and those from mutational accumulation experiments are highly correlated ( =0.95). Our estimates of codon specific translational inefficiencies are tRNA copy number based estimates of ribosome pausing time ( = 0.64), and mRNA and ribosome profiling footprint based estimates of gene expression ( =0.53-0.74) are also highly correlated, thus supporting the hypothesis that selection against translational inefficiency is an important force driving the evolution of CUB. Surprisingly, we find that for particular amino acids, codon usage in highly expressed genes can still be largely driven by mutation bias and that failing to take mutation bias into account can lead to the misidentification of an amino acid's `optimal' codon. In conclusion, our method demonstrates that an enormous amount of biologically important information is encoded within genome scale patterns of codon usage, accessing this information does not require gene expression measurements, but instead carefully formulated biologically interpretable models.