Analysis of mutations in the COLIA1 gene with second-order rule induction

Rattikorn Hewett, John Leuchner, Sean D. Mooney, Teri E. Klein

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Mutations are structural changes in DNA that can cause protein malfunction and genetic disease. This paper describes a machine learning approach to analyzing mutations associated to Osteogenesis Imperfecta (OI), also known as brittle bone disease, We apply SORCER, a second-order rule induction system to predict clinical phenotypes of OI from mutation and neighboring amino acid sequences in the COLIA1 gene. On the average, over ten 10-fold cross-validations, SORCER gives more accurate results than C4.5 with average accuracy of about 81.2%. The paper discusses the advantages and limitations of SORCER and demonstrates its use to provide initial exploration of biological sequences.

Original languageEnglish
Pages (from-to)721-740
Number of pages20
JournalInternational Journal of Pattern Recognition and Artificial Intelligence
Volume17
Issue number5
DOIs
StatePublished - Aug 2003

Keywords

  • Bioinformatics
  • Data mining algorithms
  • Induction
  • Machine learning
  • Mutations

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