‘Gene shaving’ as a method for identifying distinct sets of genes with similar expression patterns

Background: Large gene expression studies, such as those conducted using DNA arrays, often provide millions of different pieces of data. To address the problem of analyzing such data, we describe a statistical method, which we have called ‘gene shaving’. The method identifies subsets of genes with coherent expression patterns and large variation across conditions. Gene shaving differs from hierarchical clustering and other widely used methods for analyzing gene expression studies in that genes may belong to more than one cluster, and the clustering may be supervised by an outcome measure. The technique can be ‘unsupervised’, that is, the genes and samples are treated as unlabeled, or partially or fully supervised by using known properties of the genes or samples to assist in finding meaningful groupings. Results: We illustrate the use of the gene shaving method to analyze gene expression measurements made on samples from patients with diffuse large B-cell lymphoma. The method identifies a small cluster of genes whose expression is highly predictive of survival. Conclusions: The gene shaving method is a potentially useful tool for exploration of gene expression data and identification of interesting clusters of genes worth further investigation.

Evaluation Method for Strategic Investments

Real Options Analysis (ROA) has become a complimentary tool for engineering economics. It has become popular due to the limitations of conventional engineering valuation methods; specifically, the assumptions of uncertainty. Industry is seeking to quantify the value of engineering investments with uncertainty. One problem with conventional tools are that they may assume that cash flows are certain, therefore minimizing the possibility of the uncertainty of future values. Real options analysis provides a solution to this problem, but has been used sparingly by practitioners. This paper seeks to provide a new model, referred to as the Beta Distribution Real Options Pricing Model (BDROP), which addresses these limitations and can be easily used by practitioners. The positive attributes of this new model include unconstrained market assumptions, robust representation of the underlying asset‟s uncertainty, and an uncomplicated methodology. This research demonstrates the use of the model to evaluate the use of automation for inventory control.