کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
505874 864544 2007 10 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Gene expression profile class prediction using linear Bayesian classifiers
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نرم افزارهای علوم کامپیوتر
پیش نمایش صفحه اول مقاله
Gene expression profile class prediction using linear Bayesian classifiers
چکیده انگلیسی

Due to recent advances in DNA microarray technology, using gene expression profiles, diagnostic category of tissue samples can be predicted with high accuracy. In this study, we discuss shortcomings of some existing gene expression profile classification methods and propose a new approach based on linear Bayesian classifiers. In our approach, we first construct gene-level linear classifiers to identify genes that provide high class-prediction accuracies, i.e., low error rates. After this screening phase, starting with the gene that offers the lowest error rate, we construct a multi-dimensional linear classifier by incorporating next best-performing genes, until the prediction error becomes minimum or 0, if possible. When we compared classification performance of our approach against prediction analysis of microarrays (PAM) and support vector machines (SVM) based approaches, we found that our method outperforms PAM and produces comparable results with SVM. In addition, we observed that the gene selection scheme of PAM could be misleading. Albeit SVM achieves relatively higher prediction performance, it has two major disadvantages: Complexity and lack of insight about important genes. Our intuitive approach offers competing performance and also an efficient means for finding important genes.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computers in Biology and Medicine - Volume 37, Issue 12, December 2007, Pages 1690–1699
نویسندگان
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