期刊
COMPUTATIONAL BIOLOGY AND CHEMISTRY
卷 43, 期 -, 页码 46-54出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.compbiolchem.2012.12.008
关键词
Proteomics; Protein inference; Lasso; Coordinate descent; Ensemble learning
资金
- Natural Science Foundation of China [61003176]
Protein inference is an important issue in proteomics research. Its main objective is to select a proper subset of candidate proteins that best explain the observed peptides. Although many methods have been proposed for solving this problem, several issues such as peptide degeneracy and one-hit wonders still remain unsolved. Therefore, the accurate identification of proteins that are truly present in the sample continues to be a challenging task. Based on the concept of peptide detectability, we formulate the protein inference problem as a constrained Lasso regression problem, which can be solved very efficiently through a coordinate descent procedure. The new inference algorithm is named as ProteinLasso, which explores an ensemble learning strategy to address the sparsity parameter selection problem in Lasso model. We test the performance of ProteinLasso on three datasets. As shown in the experimental results, ProteinLasso outperforms those state-of-the-art protein inference algorithms in terms of both identification accuracy and running efficiency. In addition, we show that ProteinLasso is stable under different parameter specifications. The source code of our algorithm is available at: http://sourceforge.net/projects/proteinlasso. (C) 2013 Elsevier Ltd. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据