In computational physics and chemistry, there is often a need to perform linear algebra operations with large matrices. As researchers move to more realistic models, the size of the model grows, and so does the size of the matrices, slowing the computations down.
Many research investigations focus on changes within subjects over time, with an emphasis on how the pattern of such longitudinal changes varies among individuals. State of the art longitudinal modeling techniques allow these effects to be assessed efficiently.
Modern internal combustion engines utilize sophisticated control units to precisely time the ignition of fuel during each engine cycle. Researchers aiming to improve engine performance are able to collect detailed data on hundreds of engine cycles using instruments that capture multiple parameters at high temporal resolution.
Censored data arise when a value of interest, such as the concentration of a chemical in a specimen, lies within a known set of values, but the specific value is not known. For example, assays used in environmental toxicology typically have a lower limit of detection.
To enable early cancer detection, researchers have long sought to identify biochemical markers that discriminate people with early stage cancer from risk-matched controls. High throughput screening allows large collections of candidate biomarkers to be assessed for potential use as cancer biomarkers.
Diabetes, hypertension, and chronic kidney disease (CKD) commonly co-occur within individuals. A team of University of Michigan and external researchers including CSCAR consultant Brady West recently demonstrated that periodontal disease may also play an important role in this comorbid system.
Magnetoencephalography (MEG) is a form of neuroimaging that can be used to assess activity in various brain regions. Working with researchers from the University of Michigan Department of Psychiatry, CSCAR consultant Dave Childers used spectral coherence analysis to examine connections between pairs of brain regions.