Student's Corner

Astghik Sargsyan presents her work on the COVID-19 Ontology, describing the applications.

Here, Master’s student Lauren Nicole DeLong describes the work she plans to submit in which she used a network structure of protein-protein interactions combined with COVID-19 gene expression data to predict novel drug targets for COVID-19.

Tamara Raschka writes about here recent work on an AI strategy to quantitatively model biological mechanisms for systems medicine in Alzheimer’s.

Master’s student, Rebeca Figueiredo, discusses her recently submitted work on overlaying pathway knowledge with co-expression networks to identify disease-specific mechanisms.

In this post, Sepehr discusses his recent work on simulating drug responses in patients by calibrating their pathway activity scores using machine learning methods and a novel scoring algorithm.

In this post, doctoral student, Sarah Mubeen, outlines her recent work on a web application to assist researchers in the interpretation of high throughput data with popular pathway analysis methods, such as GSEA and ORA. Her user-friendly tool will soon be available online.

Bruce Schultz discusses his recent paper on synthesizing data and information in the context of COVID-19 in order to predict drug repurposing candidates.

PhD student, Yasamin Salimi, details her ongoing work with investigating and mapping the Alzheimer’s disease patient data landscape to construct an expansive overview and researcher-friendly tool for clinical cohort exploration and selection.