| Title |
Feasibility of Multimodal MRI-Based Deep Learning Prediction of High Amino Acid Uptake Regions and Survival in Patients With Glioblastoma
|
|---|---|
| Published in |
Frontiers in Neurology, December 2019
|
| DOI | 10.3389/fneur.2019.01305 |
| Pubmed ID | |
| Authors |
Jeong-Won Jeong, Min-Hee Lee, Flóra John, Natasha L. Robinette, Alit J. Amit-Yousif, Geoffrey R. Barger, Sandeep Mittal, Csaba Juhász |
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 % |
|---|---|---|
| United States | 1 | 50% |
| Switzerland | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 50% |
| Members of the public | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 26 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 26 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 6 | 23% |
| Student > Ph. D. Student | 4 | 15% |
| Researcher | 3 | 12% |
| Student > Postgraduate | 2 | 8% |
| Unspecified | 1 | 4% |
| Other | 3 | 12% |
| Unknown | 7 | 27% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 5 | 19% |
| Medicine and Dentistry | 3 | 12% |
| Engineering | 3 | 12% |
| Biochemistry, Genetics and Molecular Biology | 2 | 8% |
| Neuroscience | 1 | 4% |
| Other | 1 | 4% |
| Unknown | 11 | 42% |
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 20 December 2019.
All research outputs
#15,541,252
of 23,182,015 outputs
Outputs from Frontiers in Neurology
#6,775
of 12,101 outputs
Outputs of similar age
#257,438
of 430,755 outputs
Outputs of similar age from Frontiers in Neurology
#192
of 282 outputs
Altmetric has tracked 23,182,015 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 12,101 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.3. This one is in the 42nd percentile – i.e., 42% 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 430,755 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 39th percentile – i.e., 39% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 282 others from the same source and published within six weeks on either side of this one. This one is in the 30th percentile – i.e., 30% of its contemporaries scored the same or lower than it.