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.