Talk: Human factors in computational biology – from mathematical models to user interfaces

Anna DivoliOur speaker for April is Anna Divoli, who will  give a talk entitled  “Human factors in computational biology – from mathematical models to user interfaces“.

WHEN: Wednesday, April 13, 14h00
WHERE: M203 (the function room next to Murrays restaurant, where the HSF meetings take place), Wellcome Trust Genome Campus, Hinxton

Anna is a postdoc in the Dept of Medicine, University of Chicago, where she carries out research in the field of text mining, search and user interface design.

Her talk will address how taking a user-centered approach can ensure successful design and allow bioscientists to get the most out of the algorithms behind the user interfaces.

Anna will also discuss the development of the web interface of the BioText Search Engine, and how it was carefully designed to be useful and usable for scientists.

Full abstract of the talk

In Computational Biology we constantly need the input of experts, in all stages of method and tool development. We require domain expertise while: (1) exploring biological information still under investigation, (2) acquiring biological knowledge in consensus, (3) evaluating results for algorithm refinement, and (4) conducting usability studies for end-user tools.

The majority of computational researchers interact with biologists for stages (2) and (3). For instance, biological information is used to build formal knowledge representations or converted to a set of parameters for building mathematical models. The community also systematically conducts evaluations to ensure optimal performance of the methods used.

In this talk I will discuss two very different projects examining the two somewhat neglected roles of experts in stages (1) and (4). Understanding how to utilize subjective information from experts (information still speculative and not officially considered scientific knowledge yet) can help build tools with great impact in guiding research. Usability studies can ensure successful design where bioscientists can get the most out of the algorithms behind the user interfaces.

Language and cancer metastasis

The first project is 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 of 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 show that in reality knowledge is not as crisp as portrayed in 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.

The BioText search engine

The other project is the development of the web interface of the BioText Search Engine. A number of usability studies were designed and conducted throughout the development of BioText and users were consulted systematically. The aim was to understand what type of information bioscientists are interested in while searching through articles and what is the best way to display such information. I will discuss the methodology we followed and outline the main results on
searching in full text articles and displaying full text, figures, tables, captions and metadata, as well as enabling query expansion for gene/protein names in combination with organism information.

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