User-centered evaluation of Arizona BioPathway: an information extraction, integration, and visualization system

Full text clustering and relationship network analysis of biomedical publications

Rapid developments in the biomedical sciences have increased the demand for automatic clustering of biomedical publications. In contrast to current approaches to text clustering, which focus exclusively on the contents of abstracts, a novel method is proposed for clustering and analysis of complete biomedical article texts. To reduce dimensionality, Cosine Coefficient is used on a sub-space of only two vectors, instead of computing the Euclidean distance within the space of all vectors. Then a strategy and algorithm is introduced for Semi-supervised Affinity Propagation (SSAP) to improve analysis efficiency, using biomedical journal names as an evaluation background. Experimental results show that by avoiding high-dimensional sparse matrix computations, SSAP outperforms conventional k-means methods and improves upon the standard Affinity Propagation algorithm. In constructing a directed relationship network and distribution matrix for the clustering results, it can be noted that overlaps in scope and interests among BioMed publications can be easily identified, providing a valuable analytical tool for editors, authors and readers.

Studying PubMed usages in the field for complex problem solving: Implications for tool design

Many recent studies on MEDLINE-based information seeking have shed light on scientists' behaviors and associated tool innovations that may improve efficiency and effectiveness. Few if any studies, however, examine scientists' problem-solving uses of PubMed in actual contexts of work and corresponding needs for better tool support. Addressing this gap, we conducted a field study of novice scientists (14 upper level undergraduate majors in molecular biology) as they engaged in a problem solving activity with PubMed in a laboratory setting. Findings reveal many common stages and patterns of information seeking across users as well as variations, especially variations in cognitive search styles. Based on findings, we suggest tool improvements that both confirm and qualify many results found in other recent studies. Our findings highlight the need to use results from context-rich studies to inform decisions in tool design about when to offer improved features to users.