Pathway expression analysis.

This paper introduces a pathway expression framework as an approach for constructing derived biomarkers. The pathway expression framework incorporates the biological connections of genes leading to a biologically relevant model. Using this framework, we distinguish between shedding subjects post-infection and all subjects pre-infection in human blood transcriptomic samples challenged with various respiratory viruses: H1N1, H3N2, HRV (Human Rhinoviruses), and RSV (Respiratory Syncytial Virus). Additionally, pathway expression data is used for selecting discriminatory pathways from these experiments. The classification results and selected pathways are benchmarked against standard gene expression based classification and pathway ranking methodologies. We find that using the pathway expression data along with selected pathways, which have minimal overlap with high ranking pathways found by traditional methods, improves classification rates across experiments.

Biomarker selection and a prospective metabolite-based machine learning diagnostic for lyme disease.

We provide a pipeline for data preprocessing, biomarker selection, and classification of liquid chromatography-mass spectrometry (LCMS) serum samples to generate a prospective diagnostic test for Lyme disease. We utilize tools of machine learning (ML), e.g., sparse support vector machines (SSVM), iterative feature removal (IFR), and k-fold feature ranking to select several biomarkers and build a discriminant model for Lyme disease. We report a 98.13% test balanced success rate (BSR) of our model based on a sequestered test set of LCMS serum samples. The methodology employed is general and can be readily adapted to other LCMS, or metabolomics, data sets.

Quantifying Protein Concentrations Using Smartphone Colorimetry: A New Method for an Established Test.

Proteins are involved in nearly every biological process, which makes them of interest to a range of scientists. Previous work has shown that hand-held cameras can be used to determine the concentration of colored analytes in solution, and this paper extends the approach to reactions involving a color change in order to quantify protein concentration (e.g., green to blue). Herein, we describe the successful use of smartphone colorimetry to quantify protein concentration using two common colorimetric biochemical methods, the Bradford and biuret assays. The ease of the experimental setup makes these lab experiments accessible to a wide range of students and can be used as both high school and college level laboratory experiments.