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
| Title |
Predicting Essential Genes and Proteins Based on Machine Learning and Network Topological Features: A Comprehensive Review
|
|---|---|
| Published in |
Frontiers in Physiology, March 2016
|
| DOI | 10.3389/fphys.2016.00075 |
| Pubmed ID | |
| Authors |
Xue Zhang, Marcio Luis Acencio, Ney Lemke |
| Abstract |
Essential proteins/genes are indispensable to the survival or reproduction of an organism, and the deletion of such essential proteins will result in lethality or infertility. The identification of essential genes is very important not only for understanding the minimal requirements for survival of an organism, but also for finding human disease genes and new drug targets. Experimental methods for identifying essential genes are costly, time-consuming, and laborious. With the accumulation of sequenced genomes data and high-throughput experimental data, many computational methods for identifying essential proteins are proposed, which are useful complements to experimental methods. In this review, we show the state-of-the-art methods for identifying essential genes and proteins based on machine learning and network topological features, point out the progress and limitations of current methods, and discuss the challenges and directions for further research. |
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 % |
|---|---|---|
| India | 1 | 25% |
| Switzerland | 1 | 25% |
| Unknown | 2 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 4 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 143 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | <1% |
| Ireland | 1 | <1% |
| Unknown | 141 | 99% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 30 | 21% |
| Student > Bachelor | 23 | 16% |
| Researcher | 22 | 15% |
| Student > Master | 21 | 15% |
| Student > Postgraduate | 10 | 7% |
| Other | 12 | 8% |
| Unknown | 25 | 17% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 29 | 20% |
| Computer Science | 26 | 18% |
| Biochemistry, Genetics and Molecular Biology | 25 | 17% |
| Medicine and Dentistry | 9 | 6% |
| Engineering | 7 | 5% |
| Other | 18 | 13% |
| Unknown | 29 | 20% |
Attention Score in Context
This research output has an Altmetric Attention Score of 5. 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 14 January 2021.
All research outputs
#6,160,805
of 22,854,458 outputs
Outputs from Frontiers in Physiology
#2,865
of 13,646 outputs
Outputs of similar age
#86,225
of 299,380 outputs
Outputs of similar age from Frontiers in Physiology
#36
of 140 outputs
Altmetric has tracked 22,854,458 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 13,646 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.6. This one has done well, scoring higher than 78% 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 299,380 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 70% of its contemporaries.
We're also able to compare this research output to 140 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 74% of its contemporaries.