Journal
BRIEFINGS IN BIOINFORMATICS
Volume 23, Issue 2, Pages -Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bib/bbac040
Keywords
feature selection; OMIC study; diagnostic accuracy; robustness; ensemble learning
Funding
- National Natural Science Foundation of China [U1909208, 81872798]
- Natural Science Foundation of Zhejiang Province [LR21H300001]
- Leading Talent of the 'Ten Thousand Plan' - National High - Level Talents Special Support Plan of China
- Fundamental Research Fund for Central Universities [2018QNA7023]
- 'Double Top - Class' University Project [181201*194232101]
- Key R&D Program of Zhejiang Province [2020003010]
- Westlake Laboratory (Westlake Laboratory of Life Sciences and Biomedicine)
- Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare
- Information Technology Center of Zhejiang University
- Alibaba Cloud
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Mass spectrometry-based proteomic technique is essential in studying biological processes. However, current statistical frameworks neglect the reproducibility among identified features. Thus, developing a tool to identify reproducible and generalizable proteomic signatures is crucial.
Mass spectrometry-based proteomic technique has become indispensable in current exploration of complex and dynamic biological processes. Instrument development has largely ensured the effective production of proteomic data, which necessitates commensurate advances in statistical framework to discover the optimal proteomic signature. Current framework mainly emphasizes the generalizability of the identified signature in predicting the independent data but neglects the reproducibility among signatures identified from independently repeated trials on different sub-dataset. These problems seriously restricted the wide application of the proteomic technique in molecular biology and other related directions. Thus, it is crucial to enable the generalizable and reproducible discovery of the proteomic signature with the subsequent indication of phenotype association. However, no such tool has been developed and available yet. Herein, an online tool, POSREG, was therefore constructed to identify the optimal signature for a set of proteomic data. It works by (i) identifying the proteomic signature of good reproducibility and aggregating them to ensemble feature ranking by ensemble learning, (ii) assessing the generalizability of ensemble feature ranking to acquire the optimal signature and (iii) indicating the phenotype association of discovered signature. POSREG is unique in its capacity of discovering the proteomic signature by simultaneously optimizing its reproducibility and generalizability. It is now accessible free of charge without any registration or login requirement at
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