2001 | OriginalPaper | Buchkapitel
Knowledge Discovery in Multi-label Phenotype Data
verfasst von : Amanda Clare, Ross D. King
Erschienen in: Principles of Data Mining and Knowledge Discovery
Verlag: Springer Berlin Heidelberg
Enthalten in: Professional Book Archive
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
The biological sciences are undergoing an explosion in the amount of available data. New data analysis methods are needed to deal with the data. We present work using KDD to analyse data from mutant phenotype growth experiments with the yeast S. cerevisiae to predict novel gene functions. The analysis of the data presented a number of challenges: multi-class labels, a large number of sparsely populated classes, the need to learn a set of accurate rules (not a complete classification), and a very large amount of missing values. We developed resampling strategies and modified the algorithm C4.5 to deal with these problems. Rules were learnt which are accurate and biologically meaningful. The rules predict function of 83 putative genes of currently unknown function at an estimated accuracy of > 80%.