You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output.
Click here to find out more.
X Demographics
Mendeley readers
Attention Score in Context
| Chapter title |
DrugE-Rank: Predicting Drug-Target Interactions by Learning to Rank
|
|---|---|
| Chapter number | 14 |
| Book title |
Data Mining for Systems Biology
|
| Published in |
Methods in molecular biology, January 2018
|
| DOI | 10.1007/978-1-4939-8561-6_14 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-8560-9, 978-1-4939-8561-6
|
| Authors |
Jieyao Deng, Qingjun Yuan, Hiroshi Mamitsuka, Shanfeng Zhu, Deng, Jieyao, Yuan, Qingjun, Mamitsuka, Hiroshi, Zhu, Shanfeng |
| Abstract |
Identifying drug-target interactions is crucial for the success of drug discovery. Approaches based on machine learning for this problem can be divided into two types: feature-based and similarity-based methods. By utilizing the "Learning to rank" framework, we propose a new method, DrugE-Rank, to combine these two different types of methods for improving the prediction performance of new candidate drugs and targets. DrugE-Rank is available at http://datamining-iip.fudan.edu.cn/service/DrugE-Rank/ . |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Japan | 1 | 33% |
| Unknown | 2 | 67% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 67% |
| Scientists | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 4 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 4 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Professor | 1 | 25% |
| Student > Bachelor | 1 | 25% |
| Researcher | 1 | 25% |
| Unknown | 1 | 25% |
| Readers by discipline | Count | As % |
|---|---|---|
| Pharmacology, Toxicology and Pharmaceutical Science | 1 | 25% |
| Computer Science | 1 | 25% |
| Agricultural and Biological Sciences | 1 | 25% |
| Unknown | 1 | 25% |
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 27 July 2018.
All research outputs
#15,177,072
of 23,344,526 outputs
Outputs from Methods in molecular biology
#4,835
of 13,338 outputs
Outputs of similar age
#257,756
of 444,166 outputs
Outputs of similar age from Methods in molecular biology
#510
of 1,502 outputs
Altmetric has tracked 23,344,526 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 13,338 research outputs from this source. They receive a mean Attention Score of 3.4. This one has gotten more attention than average, scoring higher than 59% 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 444,166 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 1,502 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 60% of its contemporaries.