Expert Opinions in Cancer Metastasis: Uncertainty, Discrepancies, Range and Models


Anna Divoli, PhD


March 18, 2011, 2:00 pm – 3:00 pm


Searle 240A, 5735 S. Ellis Avenue
The University of Chicago,


Computation Institute- Data Lunch Seminar (DLS)

Speaker: Anna Divoli, Postdoctoral Scholar, Department of Medicine, Institute for Genomics and Systems Biology
Date: March 18, 2011
Time: 12:00 PM - 1:00 PM
Location: The University of Chicago, Searle 240A, 5735 S. Ellis Avenue

Expert Opinions in Cancer Metastasis: Uncertainty, Discrepancies, Range and Models

Research in computational biology is often contingent on principal notions. Mathematical modeling is relying on valid initial assumptions. Text mining algorithms can only retrieve or extract information found in text. Knowledge representation requires a degree of knowledge consensus. Our understanding of certain areas in biology, however, is still in its infancy having a ripple effect in computational efforts.

In this talk I discuss a study on cancer metastasis - a complex biological phenomenon with vast clinical importance. Individual viewpoints from 28 experts in clinical or molecular aspects of cancer metastasis were harvested and summarized computationally. Detailed analysis of the data reveals areas of disagreement and a range of opinions on underlying causes and processes in metastasis. The language that experts used while communicating their views was also examined. The experts use gripping metaphors and much hedging. Extensive automatic analysis reveals high use of language associated with cognitive processes (certainty and insight, in particular) - language commonly under-represented in scientific text. The results from this study show that in reality knowledge is not as crisp as the view one might obtain by looking at textbooks and the scientific literature. There is speculation, uncertainty and difference of opinion.

These findings have ramifications in (i) building mathematical models of biological processes such as cancer metastasis, and (ii) formally representing metastasis. I propose probabilistic models and ontologies that systematically factor experts’ hunches and speculations. I will also discuss the repercussions of this difference of opinion in scientific paper and grant reviewing.

Anna is a postdoctoral scholar in Andrey Rzhetsky’s group. Prior coming to Chicago, she completed her PhD in biomedical text mining at the University of Manchester and carried out postdoctoral research in biomedical user search interfaces in the School of Information at the University of California, Berkeley. Her research focuses on developing methodologies for acquiring biomedical knowledge from textual data and studying the effect of human factors in that process.

Latest News

Start-up founded by IGSB faculty wins $250,000 Polsky Center award

BiomeSense, a startup developing biosensors that can detect particular kinds of bacteria in patients’ feces that could help improve the efficacy of clinical trials, won the University of Chicago’s Innovation Fund finals and an investment of up to $250,000 from the college.

Massive data analysis shows what drives the spread of flu in the US

Models built with data from health claims, weather, geography and Twitter predict how the flu spreads from the south and southeastern coast

Subscribe to RSS Feed