The integration of gene expression data to predict systemic lupus erythematosus (SLE) disease activity is a significant challenge because of the high degree of heterogeneity among patients and study cohorts, especially those collected on different microarray platforms. Here we deployed machine learning approaches to integrate gene expression data from three SLE data sets and used it to classify patients as having active or inactive disease as characterized by standard clinical composite outcome measures. Both raw whole blood gene expression data and informative gene modules generated by Weighted Gene Co-expression Network Analysis from purified leukocyte populations were employed with various classification algorithms. Classifiers were evaluated by 10-fold cross-validation across three combined data sets or by training and testing in independent data sets, the latter of which amplified the effects of technical variation. A random forest classifier achieved a peak classification accuracy of 83 percent under 10-fold cross-validation, but its performance could be severely affected by technical variation among data sets. The use of gene modules rather than raw gene expression was more robust, achieving classification accuracies of approximately 70 percent regardless of how the training and testing sets were formed. Fine-tuning the algorithms and parameter sets may generate sufficient accuracy to be informative as a standalone estimate of disease activity.
At BioBuzz, we’re dedicated to bringing you interesting and important stories that emerge from the thriving life sciences ecosystem that is the BioHealth Capital Region (BHCR).
The heart of the BHCR biohealth cluster is Maryland, with its vast network of government research agencies, the strong university system, and a deep network of established, emerging, and startup life science companies across a wide range of fields, including cell and gene therapy, advanced biomanufacturing, phage therapy, medical devices and more traditional biotech and pharma organizations. That’s not even to mention the remarkable efforts of myriad Maryland vaccine companies to develop the first approved, safe, and efficacious SARS-CoV-2 vaccine.
Maryland is certainly the most mature market within the wider BHCR, and the one we tend to cover the most, especially recently, but it isn’t the only growing and thriving BHCR sector.
In addition to exciting developments in Washington, DC, Charlottesville, Virginia is quietly emerging as an exciting hub for life science companies and promising startups that have spun out of the University of Virginia (UVA). UVA and CVilleBioHub, which was founded in 2016 by biotech industry leaders to support the growing biohealth ecosystem in and around Charlottesville, Virginia, have been catalysts for growth in what they call the CVille ecosystem. CVilleBioHub’s mission is to double the size of the biohealth cluster it serves by 2030 through strong engagement, deft resourcing, valuable programming, and grassroots advocacy.
It seems the collaboration between UVA and CVilleBioHub is working. There are a host of exciting, innovative startup companies that call Charlottesville home. We’re excited to highlight these nine innovative Charlottesville, Virginia startup companies and look forward to telling their stories well into the future.