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

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

doi:10.1142/S0218001403002629

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

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

Computer Science

Research output: Contribution to journal › Article › peer-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 language	English
Pages (from-to)	721-740
Number of pages	20
Journal	International Journal of Pattern Recognition and Artificial Intelligence
Volume	17
Issue number	5
DOIs	https://doi.org/10.1142/S0218001403002629
State	Published - Aug 2003

Keywords

Bioinformatics
Data mining algorithms
Induction
Machine learning
Mutations

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10.1142/S0218001403002629

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title = "Analysis of mutations in the COLIA1 gene with second-order rule induction",

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.",

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AU - Hewett, Rattikorn

AU - Leuchner, John

AU - Mooney, Sean D.

AU - Klein, Teri E.

PY - 2003/8

Y1 - 2003/8

N2 - 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.

AB - 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.

KW - Bioinformatics

KW - Data mining algorithms

KW - Induction

KW - Machine learning

KW - Mutations

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JO - International Journal of Pattern Recognition and Artificial Intelligence

JF - International Journal of Pattern Recognition and Artificial Intelligence

IS - 5

ER -

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

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Keywords

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