Point of view filming and the elicitation interview

Face-to-face interviews are a fundamental research tool in qualitative research. Whilst this form of data collection can provide many valuable insights, it can often fall short of providing a complete picture of a research subject's experiences. Point of view (PoV) interviewing is an elicitation technique used in the social sciences as a means of enriching data obtained from research interviews. Recording research subjects' first person perspectives, for example by wearing digital video glasses, can afford deeper insights into their experiences. PoV interviewing can promote making visible the unverbalizable and does not rely as much on memory as the traditional interview. The use of such relatively inexpensive technology is gaining interest in health profession educational research and pedagogy, such as dynamic simulation-based learning and research activities. In this interview, Dr Gerry Gormley (a medical education researcher) talks to Dr Jonathan Skinner (an anthropologist with an interest in PoV interviewing), exploring some of the many crossover implications with PoV interviewing for medical education research and practice.

Bagging statistical network inference from large-scale gene expression data.

Modern biology and medicine aim at hunting molecular and cellular causes of biological functions and diseases. Gene regulatory networks (GRN) inferred from gene expression data are considered an important aid for this research by providing a map of molecular interactions. Hence, GRNs have the potential enabling and enhancing basic as well as applied research in the life sciences. In this paper, we introduce a new method called BC3NET for inferring causal gene regulatory networks from large-scale gene expression data. BC3NET is an ensemble method that is based on bagging the C3NET algorithm, which means it corresponds to a Bayesian approach with noninformative priors. In this study we demonstrate for a variety of simulated and biological gene expression data from S. cerevisiae that BC3NET is an important enhancement over other inference methods that is capable of capturing biochemical interactions from transcription regulation and protein-protein interaction sensibly. An implementation of BC3NET is freely available as an R package from the CRAN repository. © 2012 de Matos Simoes, Emmert-Streib.