Status | 已發表Published |
Classification based Integration of Quantifications for LC-MS Analysis | |
Li,Tianjun; Chen,Long![]() | |
2018-12-01 | |
Source Publication | 2018 International Conference on Security, Pattern Analysis, and Cybernetics, SPAC 2018
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Pages | 97-102 |
Abstract | A classification based integration of quantification method for the Liquid Chromatography - Mass Spectrometry (LC-MS) analysis is described in this paper. Typically, one biological tissue may be sent to the LC-MS many times in practice to generate multiple LC-MS data. Due to the precise level or the profile of the search engine, these multiple individual quantitative results of the multiple LC-MS data may be partially identical. Here we proposed a method to integrate the quantitative results for the case where there are multiple individual measurements but the results are only partially identical. This proposed method applies a classifier to the peptides and treats the predicted probabilities of the classification as the weights to combine these multiple individual quantitative results into a better one. Experimental results show that in the task of quantitative LC-MS, the results generated by this integration method perform better than the ones produced by other individual measurements. |
DOI | 10.1109/SPAC46244.2018.8965613 |
URL | View the original |
Language | 英語English |
Scopus ID | 2-s2.0-85079175561 |
Fulltext Access | |
Citation statistics | |
Document Type | Conference paper |
Collection | DEPARTMENT OF COMPUTER AND INFORMATION SCIENCE |
Affiliation | University of Macau,Department of Computer and Information Science,Taipa,Macao |
First Author Affilication | University of Macau |
Recommended Citation GB/T 7714 | Li,Tianjun,Chen,Long,Wei,Huiqin. Classification based Integration of Quantifications for LC-MS Analysis[C], 2018, 97-102. |
APA | Li,Tianjun., Chen,Long., & Wei,Huiqin (2018). Classification based Integration of Quantifications for LC-MS Analysis. 2018 International Conference on Security, Pattern Analysis, and Cybernetics, SPAC 2018, 97-102. |
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