Abstract
Urine is a valuable source of biomarkers. Current proteomic technologies can identify hundreds of differentially expressed proteins between disease and control samples in a single experiment; however, selection of promise biomarker candidates for further validation study remains difficult. UPBD (Urinary Protein Biomarker Database) was established to collect information of urinary biomarkers or biomarker candidates from published literature in 2011. Both proteomic and non-proteomic studies on all kinds of urine specimens from patients or experimental animals were included in UPBD. To ensure the quality of the database, all research articles were manually curated. This database was updated to version 2.0 in 2017. Standardization of database content was conducted by using terms from several commonly used ontologies and controlled vocabularies. The potential usage of each biomarker (e.g., diagnosis, prognosis) was added as a new field. A new, user-friendly website was developed to provide free browse, search, and download services for nonprofit users. The URL of UPBD 2.0 is http://upbd.bmicc.cn/.
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References
Anderson DC, Kodukula K. Biomarkers in pharmacology and drug discovery. Biochem Pharmacol. 2014;87(1):172–88.
Bandrowski A, et al. The ontology for biomedical investigations. PLoS One. 2016;11(4):e0154556.
BEST (Biomarkers, EndpointS, and other Tools) Resource. FDA-NIH biomarker working group. Silver Spring/Bethesda: Food and Drug Administration/National Institutes of Health; 2016.
Bravo A, et al. A knowledge-driven approach to extract disease-related biomarkers from the literature. Biomed Res Int. 2014;2014:253128.
Buckler AJ, et al. Quantitative imaging biomarker ontology (QIBO) for knowledge representation of biomedical imaging biomarkers. J Digit Imaging. 2013;26(4):630–41.
Califf RM. Biomarker definitions and their applications. Exp Biol Med (Maywood). 2018;243(3):213–21.
Ceusters W, Smith B. Biomarkers in the ontology for general medical science. Stud Health Technol Inform. 2015;210:155–9.
Habibi M, et al. Deep learning with word embeddings improves biomedical named entity recognition. Bioinformatics. 2017;33(14):i37–48.
Harpole M, Davis J, Espina V. Current state of the art for enhancing urine biomarker discovery. Expert Rev Proteomics. 2016;13(6):609–26.
Jordan R, Visweswaran S, Gopalakrishnan V. Semi-automated literature mining to identify putative biomarkers of disease from multiple biofluids. J Clin Bioinforma. 2014;4:13.
Khan A, Packer NH. Simple urinary sample preparation for proteomic analysis. J Proteome Res. 2006;5(10):2824–38.
Leng W, et al. Proof-of-concept workflow for establishing reference intervals of human urine proteome for monitoring physiological and pathological changes. EBioMedicine. 2017;18:300–10.
Liu X, et al. An individual urinary proteome analysis in normal human beings to define the minimal sample number to represent the normal urinary proteome. Proteome Sci. 10(1):70.
Majewski T, et al. Detection of bladder cancer using proteomic profiling of urine sediments. PLoS One. 2012;7(8):e42452.
Oh J, et al. Establishment of a near-standard two-dimensional human urine proteomic map. Proteomics. 2004;4(11):3485–97.
Oliveira Arcolino F, et al. Human urine as a noninvasive source of kidney cells. Stem Cells Int. 2015;2015:362562.
Shao C, et al. A tool for biomarker discovery in the urinary proteome: a manually curated human and animal urine protein biomarker database. Mol Cell Proteomics. 2011;10(11):M111 010975.
Street JM, et al. Urine Exosomes: an emerging trove of biomarkers. Adv Clin Chem. 2017;78:103–22.
Thomas S, et al. Biomarker discovery in mass spectrometry-based urinary proteomics. Proteomics Clin Appl. 2016;10(4):358–70.
Zhu Q, et al. GRAM-CNN: a deep learning approach with local context for named entity recognition in biomedical text. Bioinformatics. 2018;34(9):1547–54.
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Shao, C. (2019). Urinary Protein Biomarker Database 2.0: A Literature-Curated Database for Protein Biomarkers in Urine. In: Gao, Y. (eds) Urine. Springer, Singapore. https://doi.org/10.1007/978-981-13-9109-5_7
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DOI: https://doi.org/10.1007/978-981-13-9109-5_7
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