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).

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.

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.