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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 improv...

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Autores principales: Liang, Xijun, Xia, Zhonghang, Jian, Ling, Wang, Yongxiang, Niu, Xinnan, Link, Andrew J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7183122/
https://www.ncbi.nlm.nih.gov/pubmed/32334531
http://dx.doi.org/10.1186/s12864-020-6693-y
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author Liang, Xijun
Xia, Zhonghang
Jian, Ling
Wang, Yongxiang
Niu, Xinnan
Link, Andrew J.
author_facet Liang, Xijun
Xia, Zhonghang
Jian, Ling
Wang, Yongxiang
Niu, Xinnan
Link, Andrew J.
author_sort Liang, Xijun
collection PubMed
description 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.
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spelling pubmed-71831222020-04-28 A cost-sensitive online learning method for peptide identification Liang, Xijun Xia, Zhonghang Jian, Ling Wang, Yongxiang Niu, Xinnan Link, Andrew J. BMC Genomics Methodology Article 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. BioMed Central 2020-04-25 /pmc/articles/PMC7183122/ /pubmed/32334531 http://dx.doi.org/10.1186/s12864-020-6693-y Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Methodology Article
Liang, Xijun
Xia, Zhonghang
Jian, Ling
Wang, Yongxiang
Niu, Xinnan
Link, Andrew J.
A cost-sensitive online learning method for peptide identification
title A cost-sensitive online learning method for peptide identification
title_full A cost-sensitive online learning method for peptide identification
title_fullStr A cost-sensitive online learning method for peptide identification
title_full_unstemmed A cost-sensitive online learning method for peptide identification
title_short A cost-sensitive online learning method for peptide identification
title_sort cost-sensitive online learning method for peptide identification
topic Methodology Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7183122/
https://www.ncbi.nlm.nih.gov/pubmed/32334531
http://dx.doi.org/10.1186/s12864-020-6693-y
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