ProteinLasso: A Lasso regression approach to protein inference problem in shotgun proteomics
ProteinLasso: A Lasso regression approach to protein inference problem in shotgun proteomics
复制标题
ProteinLasso：鸟枪法蛋白质组学中蛋白质推理问题的套索回归方法
DOI：
10.1016/j.compbiolchem.2012.12.008
复制
发表时间：
2013-04-01
影响因子：
3.1
通讯作者：
He, Zengyou
中科院分区：
文献类型：
--
作者：
Huang, Ting;Gong, Haipeng;He, Zengyou
蛋白质推断是蛋白质组学研究中的一个重要问题。其主要目标是选择最能解释所观察到的肽的候选蛋白质的适当子集。尽管已经提出了许多方法来解决这个问题，但诸如肽简并性和一击奇迹等几个问题仍未得到解决。因此，准确识别样品中真实存在的蛋白质仍然是一项具有挑战性的任务。基于肽可检测性的概念，我们将蛋白质推理问题表述为约束Lasso回归问题，可以通过坐标下降过程非常有效地解决。新的推理算法被命名为 ProteinLasso，它探索了一种集成学习策略来解决 Lasso 模型中的稀疏参数选择问题。我们在三个数据集上测试 ProteinLasso 的性能。实验结果表明，ProteinLasso 在识别精度和运行效率方面都优于那些最先进的蛋白质推理算法。此外，我们还表明 ProteinLasso 在不同的参数规格下都是稳定的。我们算法的源代码可在以下网址找到：http://sourceforge.net/projects/ Proteinlasso。 (C) 2013 Elsevier Ltd. 保留所有权利。
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.