A cost-sensitive online learning method for peptide identification

Background Post-database search is a key procedure in peptide identification with tandem mass spectrometry (MS/MS) strategies for refining peptide-spectrum matches (PSMs) generated by database search engines. Although many statistical and machine learning-based methods have been developed to improve...

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Vydané v:	BMC genomics Ročník 21; číslo 1; s. 324 - 13
Hlavní autori:	Liang, Xijun, Xia, Zhonghang, Jian, Ling, Wang, Yongxiang, Niu, Xinnan, Link, Andrew J.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	London BioMed Central 25.04.2020 BioMed Central Ltd Springer Nature B.V BMC
Predmet:	Accuracy Algorithms Analysis Animal Genetics and Genomics Biomedical and Life Sciences Boolean Classification Complexity Computational Biology - methods Computer applications Data mining Databases, Protein Datasets Distance learning Genomics Internet Kernels Learning algorithms Life Sciences Machine learning Mass spectrometry Mass spectroscopy Methodology Methodology Article Methods Microarrays Microbial Genetics and Genomics Neural networks Online education Online learning Online searching Peptide identification Peptides Peptides - analysis Peptides - chemistry Plant Genetics and Genomics Proteins Proteomics Proteomics - methods Reproducibility of Results Scientific imaging Search Engine - methods Search engines Software Support vector machines Tandem Mass Spectrometry - methods Support vector machines Peptide identification Mass spectrometry Online learning Classification
ISSN:	1471-2164, 1471-2164
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Popis
Shrnutí:	Background Post-database search is a key procedure in peptide identification with tandem mass spectrometry (MS/MS) strategies for refining peptide-spectrum matches (PSMs) generated by database search engines. Although many statistical and machine learning-based methods have been developed to improve the accuracy of peptide identification, the challenge remains on large-scale datasets and datasets with a distribution of unbalanced PSMs. A more efficient learning strategy is required for improving the accuracy of peptide identification on challenging datasets. While complex learning models have larger power of classification, they may cause overfitting problems and introduce computational complexity on large-scale datasets. Kernel methods map data from the sample space to high dimensional spaces where data relationships can be simplified for modeling. Results In order to tackle the computational challenge of using the kernel-based learning model for practical peptide identification problems, we present an online learning algorithm, OLCS-Ranker, which iteratively feeds only one training sample into the learning model at each round, and, as a result, the memory requirement for computation is significantly reduced. Meanwhile, we propose a cost-sensitive learning model for OLCS-Ranker by using a larger loss of decoy PSMs than that of target PSMs in the loss function. Conclusions The new model can reduce its false discovery rate on datasets with a distribution of unbalanced PSMs. Experimental studies show that OLCS-Ranker outperforms other methods in terms of accuracy and stability, especially on datasets with a distribution of unbalanced PSMs. Furthermore, OLCS-Ranker is 15–85 times faster than CRanker.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	1471-2164 1471-2164
DOI:	10.1186/s12864-020-6693-y