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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Reconstructing cancer drug response networks using multitask learning
|
|---|---|
| Published in |
BMC Systems Biology, October 2017
|
| DOI | 10.1186/s12918-017-0471-8 |
| Pubmed ID | |
| Authors |
Matthew Ruffalo, Petar Stojanov, Venkata Krishna Pillutla, Rohan Varma, Ziv Bar-Joseph |
| Abstract |
Translating in vitro results to clinical tests is a major challenge in systems biology. Here we present a new Multi-Task learning framework which integrates thousands of cell line expression experiments to reconstruct drug specific response networks in cancer. The reconstructed networks correctly identify several shared key proteins and pathways while simultaneously highlighting many cell type specific proteins. We used top proteins from each drug network to predict survival for patients prescribed the drug. Predictions based on proteins from the in-vitro derived networks significantly outperformed predictions based on known cancer genes indicating that Multi-Task learning can indeed identify accurate drug response networks. |
X Demographics
The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Italy | 1 | 25% |
| Germany | 1 | 25% |
| Unknown | 2 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 3 | 75% |
| Members of the public | 1 | 25% |
Mendeley readers
The data shown below were compiled from readership statistics for 51 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 51 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 16 | 31% |
| Student > Bachelor | 7 | 14% |
| Researcher | 7 | 14% |
| Student > Postgraduate | 5 | 10% |
| Student > Master | 4 | 8% |
| Other | 6 | 12% |
| Unknown | 6 | 12% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 16 | 31% |
| Agricultural and Biological Sciences | 10 | 20% |
| Computer Science | 6 | 12% |
| Medicine and Dentistry | 6 | 12% |
| Engineering | 2 | 4% |
| Other | 5 | 10% |
| Unknown | 6 | 12% |
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 October 2017.
All research outputs
#14,083,124
of 23,005,189 outputs
Outputs from BMC Systems Biology
#520
of 1,144 outputs
Outputs of similar age
#173,151
of 324,392 outputs
Outputs of similar age from BMC Systems Biology
#11
of 22 outputs
Altmetric has tracked 23,005,189 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,144 research outputs from this source. They receive a mean Attention Score of 3.6. This one has gotten more attention than average, scoring higher than 52% 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 324,392 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 44th percentile – i.e., 44% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 22 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 50% of its contemporaries.