Machine learning of functional class from phenotype data

• Published in: Bioinformatics 2002-01, Vol.18 (1), p.160-166
• Main Authors: Clare, Amanda, King, Ross D.
• Format: Article
• Language: English
• Subjects: Artificial Intelligence; Biological and medical sciences; Computational Biology; Databases, Genetic; Fundamental and applied biological sciences. Psychology; General aspects; Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects); Mutation; Open Reading Frames; Phenotype; Saccharomyces; Saccharomyces cerevisiae - genetics
• ISSN: 1367-4803; 1460-2059; 1367-4811
• DOI: 10.1093/bioinformatics/18.1.160

Motivation: Mutant phenotype growth experiments are an important novel source of functional genomics data which have received little attention in bioinformatics. We applied supervised machine learning to the problem of using phenotype data to predict the functional class of Open Reading Frames (ORFs) in Saccaromyces cerevisiae. Three sources of data were used: TRansposon-Insertion Phenotypes, Localization and Expression in Saccharomyces (TRIPLES), European Functional Analysis Network (EUROFAN) and Munich Information Center for Protein Sequences (MIPS). The analysis of the data presented a number of challenges to machine learning: 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 modified the algorithm C4.5 to deal with these problems. Results: Rules were learnt which are accurate and biologically meaningful. The rules predict function of 83 ORFs of unknown function at an estimated accuracy of ⩾ 80%. Availability: The data and complete results are available at http://users.aber.ac.uk/ajc99/phenotype/. Contact: [e-mail]