کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
416665 681393 2006 13 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Multiclass sparse logistic regression for classification of multiple cancer types using gene expression data
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نظریه محاسباتی و ریاضیات
پیش نمایش صفحه اول مقاله
Multiclass sparse logistic regression for classification of multiple cancer types using gene expression data
چکیده انگلیسی

Monitoring gene expression profiles is a novel approach to cancer diagnosis. Several studies have showed that the sparse logistic regression is a useful classification method for gene expression data. Not only does it give a sparse solution with high accuracy, it provides the user with explicit probabilities of classification apart from the class information. However, its optimal extension to more than two classes is not obvious. In this paper, we propose a multiclass extension of sparse logistic regression. Analysis of five publicly available gene expression data sets shows that the proposed method outperforms the standard multinomial logistic model in prediction accuracy as well as gene selectivity.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computational Statistics & Data Analysis - Volume 51, Issue 3, 1 December 2006, Pages 1643–1655
نویسندگان
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