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X Demographics
Mendeley readers
Attention Score in Context
| Title |
A rank weighted classification for plasma proteomic profiles based on case-based reasoning
|
|---|---|
| Published in |
BMC Medical Informatics and Decision Making, May 2018
|
| DOI | 10.1186/s12911-018-0610-1 |
| Pubmed ID | |
| Authors |
Amy M. Kwon |
| Abstract |
It is a challenge to precisely classify plasma proteomic profiles into their clinical status based solely on their patterns even though distinct patterns of plasma proteomic profiles are regarded as potential to be a biomarker because the profiles have large within-subject variances. The present study proposes a rank-based weighted CBR classifier (RWCBR). We hypothesized that a CBR classifier is advantageous when individual patterns are specific and do not follow the general patterns like proteomic profiles, and robust feature weights can enhance the performance of the CBR classifier. To validate RWCBR, we conducted numerical experiments, which predict the clinical status of the 70 subjects using plasma proteomic profiles by comparing the performances to previous approaches. According to the numerical experiment, SVM maintained the highest minimum values of Precision and Recall, but RWCBR showed highest average value in all information indices, and it maintained the smallest standard deviation in F-1 score and G-measure. RWCBR approach showed potential as a robust classifier in predicting the clinical status of the subjects for plasma proteomic profiles. |
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 % |
|---|---|---|
| Unknown | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 20 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 20 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 5 | 25% |
| Student > Master | 2 | 10% |
| Professor > Associate Professor | 2 | 10% |
| Other | 1 | 5% |
| Lecturer | 1 | 5% |
| Other | 4 | 20% |
| Unknown | 5 | 25% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 5 | 25% |
| Medicine and Dentistry | 3 | 15% |
| Social Sciences | 2 | 10% |
| Business, Management and Accounting | 1 | 5% |
| Agricultural and Biological Sciences | 1 | 5% |
| Other | 2 | 10% |
| Unknown | 6 | 30% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 20 March 2019.
All research outputs
#18,635,458
of 23,085,832 outputs
Outputs from BMC Medical Informatics and Decision Making
#1,587
of 2,012 outputs
Outputs of similar age
#255,918
of 331,175 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#15
of 17 outputs
Altmetric has tracked 23,085,832 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,012 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 9th percentile – i.e., 9% of its peers scored the same or lower than it.
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We're also able to compare this research output to 17 others from the same source and published within six weeks on either side of this one. This one is in the 5th percentile – i.e., 5% of its contemporaries scored the same or lower than it.