Identification of related gene/protein names based on an HMM of name variations

被引：15

作者：

Yeganova, L ^{[1
]}

Smith, L ^{[1
]}

Wilbur, WJ ^{[1
]}

机构：

[1] Natl Lib Med, Natl Ctr Biotechnol Informat, Computat Biol Branch, NIH, Bethesda, MD 20894 USA

来源：

COMPUTATIONAL BIOLOGY AND CHEMISTRY | 2004年 / 28卷 / 02期

关键词：

automatic term recognition; gene name variation; hidden Markov model; information extraction;

D O I：

10.1016/j.compbiolchem.2003.12.003

中图分类号：

Q [生物科学];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

Gene and protein names follow few, if any, true naming conventions and are subject to great variation in different occurrences of the same name. This gives rise to two important problems in natural language processing. First, can one locate the names of genes or proteins in free text, and second, can one determine when two names denote the same gene or protein? The first of these problems is a special case of the problem of named entity recognition, while the second is a special case of the problem of automatic term recognition (ATR), We study the second problem, that of gene or protein name variation. Here we describe a system which, given a query gene or protein name, identifies related gene or protein names in a large list. The system is based on a dynamic programming algorithm for sequence alignment in which the mutation matrix is allowed to vary under the control of a fully trainable hidden Markov model. (C) 2003 Published by Elsevier Ltd.

引用

页码：97 / 107

页数：11

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