title: On the sensitivity of feature ranked lists for large-scale biological data.
abstract: The problem of feature selection for large-scale genomic data, for example from DNA microarray experiments, is one of the fundamental and well-investigated problems in modern computational biology. From the computational point of view, a selected gene list should be characterized by good predictive power and should be understood and well explained from the biological point of view. Recently, another feature of selected gene lists is increasingly investigated, namely their stability which measures how the content and/or the gene order change when the data are perturbed. In this paper we propose a new approach to analysis of gene list stability, termed the sensitivity index, that does not require any data perturbation and allows the gene list that is most reliable in a biological sense to be chosen.
authors.affiliation: Silesian University of Technology, Institute of Automatic Control, Akademicka 16, 44-100 Gliwice, Poland. danuta.gawel@polsl.pl
authors.forename: Danuta
authors.initials: D
authors.lastname: Gaweł
authors.forename: Krzysztof
authors.initials: K
authors.lastname: Fujarewicz
mesh_terms: D000704:Analysis of Variance; D001499:Bayes Theorem; D003110:Colonic Neoplasms / Q000235:genetics; D019295:Computational Biology; D003627:Data Interpretation, Statistical; D030541:Databases, Genetic / Q000706:statistics & numerical data*; D005260:Female; D006801:Humans; D016014:Linear Models; D055641:Mathematical Concepts; D008957:Models, Genetic; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data; D010051:Ovarian Neoplasms / Q000235:genetics; D011157:Population Dynamics; D059629:Signal-To-Noise Ratio; D060388:Support Vector Machine; D049490:Systems Biology