| Title |
Building Tools for Machine Learning and Artificial Intelligence in Cancer Research: Best Practices and a Case Study with the PathML Toolkit for Computational Pathology
|
|---|---|
| Published in |
Molecular Cancer Research, December 2021
|
| DOI | 10.1158/1541-7786.mcr-21-0665 |
| Pubmed ID | |
| Authors |
Jacob Rosenthal, Ryan Carelli, Mohamed Omar, David Brundage, Ella Halbert, Jackson Nyman, Surya N. Hari, Eliezer M. Van Allen, Luigi Marchionni, Renato Umeton, Massimo Loda |
X Demographics
The data shown below were collected from the profiles of 10 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 | 5 | 50% |
| Argentina | 1 | 10% |
| Unknown | 4 | 40% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 7 | 70% |
| Scientists | 2 | 20% |
| Practitioners (doctors, other healthcare professionals) | 1 | 10% |
Mendeley readers
The data shown below were compiled from readership statistics for 29 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 29 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 8 | 28% |
| Student > Ph. D. Student | 5 | 17% |
| Student > Bachelor | 2 | 7% |
| Professor | 2 | 7% |
| Student > Doctoral Student | 1 | 3% |
| Other | 1 | 3% |
| Unknown | 10 | 34% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 6 | 21% |
| Biochemistry, Genetics and Molecular Biology | 4 | 14% |
| Medicine and Dentistry | 3 | 10% |
| Agricultural and Biological Sciences | 1 | 3% |
| Social Sciences | 1 | 3% |
| Other | 3 | 10% |
| Unknown | 11 | 38% |
Attention Score in Context
This research output has an Altmetric Attention Score of 8. 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 13 April 2022.
All research outputs
#4,334,067
of 23,530,272 outputs
Outputs from Molecular Cancer Research
#280
of 1,911 outputs
Outputs of similar age
#103,626
of 514,072 outputs
Outputs of similar age from Molecular Cancer Research
#7
of 33 outputs
Altmetric has tracked 23,530,272 research outputs across all sources so far. Compared to these this one has done well and is in the 81st percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,911 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.1. This one has done well, scoring higher than 85% of its peers.
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 514,072 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 79% of its contemporaries.
We're also able to compare this research output to 33 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 78% of its contemporaries.