Understanding visualization: a formal approach using category theory and semiotics.

This paper combines the vocabulary of semiotics and category theory to provide a formal analysis of visualization. It shows how familiar processes of visualization fit the semiotic frameworks of both Saussure and Peirce, and extends these structures using the tools of category theory to provide a general framework for understanding visualization in practice, including: Relationships between systems, data collected from those systems, renderings of those data in the form of representations, the reading of those representations to create visualizations, and the use of those visualizations to create knowledge and understanding of the system under inspection. The resulting framework is validated by demonstrating how familiar information visualization concepts (such as literalness, sensitivity, redundancy, ambiguity, generalizability, and chart junk) arise naturally from it and can be defined formally and precisely.

Predicting functional residues of protein sequence alignments as a feature selection task.

Determining which residues within a multiple alignment of protein sequences are most responsible for protein function is a difficult and important task in bioinformatics. Here, we show that this task is an application of the standard Feature Selection (FS) problem. We show the comparison of standard FS techniques with more specialised algorithms on a range of data sets backed by experimental evidence, and find that some standard algorithms perform as well as specialised ones.

Converting partial hospitals to community integrated recovery centers.

This paper describes the conversion of partial hospitals into recovery-oriented programs as part of system transformation. Steps included: participatory planning with stakeholders; strength based assessment of resources and needs; technical assistance; and changing funding strategies. Over a period of 8 years, use of partial hospitals decreased as persons with serious mental illnesses were transitioned to community integrated recovery centers.

Targeted projection pursuit for visualizing gene expression data classifications.

Unlabelled: We present a novel method for finding low-dimensional views of high-dimensional data: Targeted Projection Pursuit. The method proceeds by finding projections of the data that best approximate a target view. Two versions of the method are introduced; one version based on Procrustes analysis and one based on an artificial neural network.