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Mendeley readers
Attention Score in Context
| Title |
Identification of cancer-specific motifs in mimotope profiles of serum antibody repertoire
|
|---|---|
| Published in |
BMC Bioinformatics, June 2017
|
| DOI | 10.1186/s12859-017-1661-5 |
| Pubmed ID | |
| Authors |
Ekaterina Gerasimov, Alex Zelikovsky, Ion Măndoiu, Yurij Ionov |
| Abstract |
For fighting cancer, earlier detection is crucial. Circulating auto-antibodies produced by the patient's own immune system after exposure to cancer proteins are promising bio-markers for the early detection of cancer. Since an antibody recognizes not the whole antigen but 4-7 critical amino acids within the antigenic determinant (epitope), the whole proteome can be represented by a random peptide phage display library. This opens the possibility to develop an early cancer detection test based on a set of peptide sequences identified by comparing cancer patients' and healthy donors' global peptide profiles of antibody specificities. Due to the enormously large number of peptide sequences contained in global peptide profiles generated by next generation sequencing, the large number of cancer and control sera is required to identify cancer-specific peptides with high degree of statistical significance. To decrease the number of peptides in profiles generated by nextgen sequencing without losing cancer-specific sequences we used for generation of profiles the phage library enriched by panning on the pool of cancer sera. To further decrease the complexity of profiles we used computational methods for transforming a list of peptides constituting the mimotope profiles to the list motifs formed by similar peptide sequences. We have shown that the amino-acid order is meaningful in mimotope motifs since they contain significantly more peptides than motifs among peptides where amino-acids are randomly permuted. Also the single sample motifs significantly differ from motifs in peptides drawn from multiple samples. Finally, multiple cancer-specific motifs have been identified. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Mexico | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 50% |
| Practitioners (doctors, other healthcare professionals) | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 19 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 19 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 4 | 21% |
| Student > Postgraduate | 3 | 16% |
| Student > Ph. D. Student | 3 | 16% |
| Student > Bachelor | 2 | 11% |
| Student > Master | 2 | 11% |
| Other | 1 | 5% |
| Unknown | 4 | 21% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 4 | 21% |
| Agricultural and Biological Sciences | 2 | 11% |
| Chemical Engineering | 1 | 5% |
| Linguistics | 1 | 5% |
| Computer Science | 1 | 5% |
| Other | 3 | 16% |
| Unknown | 7 | 37% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 24 May 2019.
All research outputs
#15,557,505
of 23,881,329 outputs
Outputs from BMC Bioinformatics
#5,153
of 7,454 outputs
Outputs of similar age
#191,896
of 319,254 outputs
Outputs of similar age from BMC Bioinformatics
#73
of 118 outputs
Altmetric has tracked 23,881,329 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,454 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one is in the 26th percentile – i.e., 26% 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 319,254 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 36th percentile – i.e., 36% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 118 others from the same source and published within six weeks on either side of this one. This one is in the 33rd percentile – i.e., 33% of its contemporaries scored the same or lower than it.