Student's Corner

Master student Johanna Driever explains how she used a natural language processing model in combination with Electronic Health Records to predict Parkinson’s Disease.

PhD student Vinay Bharadhwaj talks about his recent work on identifying shared mechanisms between psychiatric disorders using knowledge-driven and gene expression data-driven analysis.

Master’s student Aya Abdelbaky describes the work she has submitted on prediction of disease diagnosis using Graph Neural Networks on transcriptomics data.

PhD candidate Negin Babaiha talks about her work on integrating Natural Language Processing pipelines into causal biomedical knowledge graphs.

Zexin Li presents her work on data-driven disease subtyping, which is a promising approach to overcome these challenges in a mechanism-agnostic manner.

Danqi Wang presents her work on the Development of an early alert model for COVID-19 pandemic situations.

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.