The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
Application of ’omics technologies to biomarker discovery in inflammatory lung diseases
Open accessAbstract
Inflammatory lung diseases are highly complex in respect of pathogenesis and relationships between inflammation, clinical disease and response to treatment. Sophisticated large-scale analytical methods to quantify gene expression (transcriptomics), proteins (proteomics), lipids (lipidomics) and metabolites (metabolomics) in the lungs, blood and urine are now available to identify biomarkers that define disease in terms of combined clinical, physiological and patho-biological abnormalities. The aspiration is that these approaches will improve diagnosis,i.e.define pathological phenotypes, and facilitate the monitoring of disease and therapy, and also, unravel underlying molecular pathways. Biomarker studies can either select predefined biomarker(s) measured by specific methods or apply an “unbiased” approach involving detection platforms that are indiscriminate in focus. This article reviews the technologies presently available to study biomarkers of lung disease within the ’omics field. The contributions of the individual ’omics analytical platforms to the field of respiratory diseases are summarised, with the goal of providing background on their respective abilities to contribute to systems medicine-based studies of lung disease.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Cytoscape: A software Environment for integrated models of biomolecular interaction networks
Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., Amin, N., Schwikowski, B., & Ideker, T. (2003) · Genome Research · doi:10.1101/gr.1239303 · Cited by 51,888
Mapping and quantifying mammalian transcriptomes by RNA-Seq
Mortazavi, A., Williams, B. A., McCue, K., Schaeffer, L., & Wold, B. (2008) · Nature Methods · doi:10.1038/nmeth.1226 · Cited by 13,696
RNA-Seq: a revolutionary tool for transcriptomics
Wang, Z., Gerstein, M., & Snyder, M. (2009) · Nature Reviews Genetics · doi:10.1038/nrg2484 · Cited by 13,174
Electrospray Ionization for Mass Spectrometry of Large Biomolecules
Fenn, J. B., Mann, M., Meng, C. K., Wong, S. F., & Whitehouse, C. M. (1989) · Science · doi:10.1126/science.2675315 · Cited by 7,634
Anderson, N. L., & Anderson, N. G. (2002) · Molecular & Cellular Proteomics · doi:10.1074/mcp.r200007-mcp200 · Cited by 4,414
KEGG for representation and analysis of molecular networks involving diseases and drugs
Kanehisa, M., Goto, S., Furumichi, M., Tanabe, M., & Hirakawa, M. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkp896 · Cited by 2,322
Absolute Quantification of Proteins by LCMSE
Silva, J. C., Gorenstein, M. V., Li, G.-Z., Vissers, J. P. C., & Geromanos, S. J. (2006) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m500230-mcp200 · Cited by 1,529
Network‐based classification of breast cancer metastasis
Chuang, H., Lee, E., Liu, Y., Lee, D., & Ideker, T. (2007) · Molecular Systems Biology · doi:10.1038/msb4100180 · Cited by 1,431
Personal omics profiling reveals dynamic molecular and medical phenotypes
Chen, R., Mias, G. I., Li-Pook-Than, J., Jiang, L., Lam, H. Y. K., Chen, R., Miriami, E., Karczewski, K. J., Hariharan, M., Dewey, F. E., et al. (2012) · Cell · doi:10.1016/j.cell.2012.02.009 · Cited by 1,291
Exhaled breath condensate: methodological recommendations and unresolved questions
Horváth, I., Hunt, J., & Barnes, P. J. (2005) · European Respiratory Journal · doi:10.1183/09031936.05.00029705 · Cited by 1,284
Self-citation
Cited by
Modules, networks and systems medicine for understanding disease and aiding diagnosis
Gustafsson, M., Nestor, C. E., Zhang, H., Barabási, A.-L., Baranzini, S., Brunak, S., Chung, K. F., Federoff, H. J., Gavin, A.-C., Meehan, R. R., et al. (2014) · Genome Medicine · doi:10.1186/s13073-014-0082-6 · Cited by 194
Self-citationEdwards, M. R., Saglani, S., Schwarze, J., Skevaki, C., Smith, J. A., Ainsworth, B., Almond, M., Andreakos, E., Belvisi, M. G., Chung, K. F., et al. (2017) · European Respiratory Journal · doi:10.1183/13993003.02448-2016 · Cited by 55
Self-citationPeel, A. M., Wilkinson, M., Sinha, A., Loke, Y. K., Fowler, S. J., & Wilson, A. M. (2020) · Respiratory Medicine · doi:10.1016/j.rmed.2020.105984 · Cited by 40
Self-citationSpatiotemporal multi-omics: exploring molecular landscapes in aging and regenerative medicine
Chu, L.-X., Wang, W.-J., Gu, X.-P., Wu, P., Gao, C., Zhang, Q., Wu, J., Jiang, D.-W., Huang, J.-Q., Ying, X.-W., et al. (2024) · Military Medical Research · doi:10.1186/s40779-024-00537-4 · Cited by 29
Plasma metabolomics in calves with acute bronchopneumonia
Basoglu, A., Baspinar, N., Tenori, L., Vignoli, A., & Yildiz, R. (2016) · Metabolomics · doi:10.1007/s11306-016-1074-x · Cited by 27
Evaluation of elevated urinary enolase I levels in patients with endometriosis
Yun, B. H., Lee, Y. S., Chon, S. J., Jung, Y. S., Yim, S. Y., Kim, H. Y., Park, J. H., Seo, S. K., Cho, S., Choi, Y. S., et al. (2014) · Biomarkers · doi:10.3109/1354750x.2013.863973 · Cited by 17
Metadata
- Authors
- Wheelock, C. E., Goss, V. M., Balgoma, D., Nicholas, B., Brandsma, J., Skipp, P. J., Snowden, S., Burg, D., D'Amico, A., Horvath, I., Chaiboonchoe, A., Ahmed, H., Ballereau, S., Rossios, C., Chung, K. F., Montuschi, P., Fowler, S. J., Adcock, I. M., Postle, A. D., Dahlén, S.-E., Rowe, A., Sterk, P. J., Auffray, C., Djukanović, R., & the U-BIOPRED Study Group
- Venue
- European Respiratory Journal
- Publisher
- European Respiratory Society (ERS)
- Published
- 2013-02-08
- Volume
- 42
- Issue
- 3
- Pages
- 802-825
- ISSN
- 0903-1936 (ISSN-L)
- 1399-3003
- OpenAlex
- W2111793469
- Cited by
- 272
- References
- 20
- FWCI
- 19.18
- Type
- journal article
- Language
- en
- Open access
- bronze
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
