title: A heuristic for gene selection and visual prediction of sample type.
abstract: In this paper, we introduce a heuristic method for gene selection. We target this method, coupled with RadViz visualisation, to the visual prediction of tissue samples which may exist in normal and disease states. As a result of this coupling, the gene selection process, predictive model training and evaluation as well as the model's application for tissue sample prediction can all be intuitively visualised. Such integrated visual analytics enhance the insight provided by classical statistics and machine learning methods. The case study shows our proposed method is cost effective and achieves competitive performance when compared with several widely used techniques.
authors.affiliation: Department of Computer Science, University of Massachusetts Lowell, 1 University Ave., Lowell, MA 01854, USA. jianpingz@gmail.com
authors.forename: Jianping
authors.initials: J
authors.lastname: Zhou
authors.forename: Georges
authors.initials: G
authors.lastname: Grinstein
authors.forename: Kenneth
authors.initials: K
authors.lastname: Marx
mesh_terms: D001185:Artificial Intelligence; D003627:Data Interpretation, Statistical; D020869:Gene Expression Profiling / Q000145:classification* / Q000379:methods; D020411:Oligonucleotide Array Sequence Analysis; D010363:Pattern Recognition, Automated