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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Computational Biomarker Pipeline from Discovery to Clinical Implementation: Plasma Proteomic Biomarkers for Cardiac Transplantation
|
|---|---|
| Published in |
PLoS Computational Biology, April 2013
|
| DOI | 10.1371/journal.pcbi.1002963 |
| Pubmed ID | |
| Authors |
Gabriela V. Cohen Freue, Anna Meredith, Derek Smith, Axel Bergman, Mayu Sasaki, Karen K. Y. Lam, Zsuzsanna Hollander, Nina Opushneva, Mandeep Takhar, David Lin, Janet Wilson-McManus, Robert Balshaw, Paul A. Keown, Christoph H. Borchers, Bruce McManus, Raymond T. Ng, W. Robert McMaster, for the Biomarkers in Transplantation and the NCE CECR Prevention of Organ Failure Centre of Excellence Teams |
| Abstract |
Recent technical advances in the field of quantitative proteomics have stimulated a large number of biomarker discovery studies of various diseases, providing avenues for new treatments and diagnostics. However, inherent challenges have limited the successful translation of candidate biomarkers into clinical use, thus highlighting the need for a robust analytical methodology to transition from biomarker discovery to clinical implementation. We have developed an end-to-end computational proteomic pipeline for biomarkers studies. At the discovery stage, the pipeline emphasizes different aspects of experimental design, appropriate statistical methodologies, and quality assessment of results. At the validation stage, the pipeline focuses on the migration of the results to a platform appropriate for external validation, and the development of a classifier score based on corroborated protein biomarkers. At the last stage towards clinical implementation, the main aims are to develop and validate an assay suitable for clinical deployment, and to calibrate the biomarker classifier using the developed assay. The proposed pipeline was applied to a biomarker study in cardiac transplantation aimed at developing a minimally invasive clinical test to monitor acute rejection. Starting with an untargeted screening of the human plasma proteome, five candidate biomarker proteins were identified. Rejection-regulated proteins reflect cellular and humoral immune responses, acute phase inflammatory pathways, and lipid metabolism biological processes. A multiplex multiple reaction monitoring mass-spectrometry (MRM-MS) assay was developed for the five candidate biomarkers and validated by enzyme-linked immune-sorbent (ELISA) and immunonephelometric assays (INA). A classifier score based on corroborated proteins demonstrated that the developed MRM-MS assay provides an appropriate methodology for an external validation, which is still in progress. Plasma proteomic biomarkers of acute cardiac rejection may offer a relevant post-transplant monitoring tool to effectively guide clinical care. The proposed computational pipeline is highly applicable to a wide range of biomarker proteomic studies. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Canada | 1 | 33% |
| Germany | 1 | 33% |
| Unknown | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 67% |
| Scientists | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 79 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Japan | 1 | 1% |
| Italy | 1 | 1% |
| Unknown | 77 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 18 | 23% |
| Student > Ph. D. Student | 17 | 22% |
| Student > Master | 8 | 10% |
| Other | 6 | 8% |
| Professor > Associate Professor | 6 | 8% |
| Other | 12 | 15% |
| Unknown | 12 | 15% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 19 | 24% |
| Agricultural and Biological Sciences | 13 | 16% |
| Biochemistry, Genetics and Molecular Biology | 10 | 13% |
| Pharmacology, Toxicology and Pharmaceutical Science | 5 | 6% |
| Computer Science | 5 | 6% |
| Other | 12 | 15% |
| Unknown | 15 | 19% |
Attention Score in Context
This research output has an Altmetric Attention Score of 5. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 12 September 2018.
All research outputs
#6,828,672
of 25,604,262 outputs
Outputs from PLoS Computational Biology
#4,615
of 9,014 outputs
Outputs of similar age
#54,500
of 213,123 outputs
Outputs of similar age from PLoS Computational Biology
#55
of 158 outputs
Altmetric has tracked 25,604,262 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 9,014 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one is in the 48th percentile – i.e., 48% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 213,123 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 73% of its contemporaries.
We're also able to compare this research output to 158 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 65% of its contemporaries.