Professor, Biostatistics and Medical Informatics
Mark Craven is interested in developing and applying machine learning methods for uncovering the regulatory mechanisms of cells. This work involves automatically constructing predictive models for such tasks as (i) recognizing transcription control signals, (ii) assigning genes to operons, and (iii) identifying expression relationships among genes. This involves sequence, gene-expression, functional annotation and textual data sources. Craven is also interested in developing automated methods that enable text sources to be better exploited for discovery and decision making in biomedical domains.