| Title |
Insights into the classification of small GTPases
|
|---|---|
| Published in |
Advances and Applications in Bioinformatics and Chemistry : AABC, May 2010
|
| DOI | 10.2147/aabc.s8891 |
| Pubmed ID | |
| Authors |
Dominik Heider, Sascha Hauke, Martin Pyka, Daniel Kessler |
| Abstract |
In this study we used a Random Forest-based approach for an assignment of small guanosine triphosphate proteins (GTPases) to specific subgroups. Small GTPases represent an important functional group of proteins that serve as molecular switches in a wide range of fundamental cellular processes, including intracellular transport, movement and signaling events. These proteins have further gained a special emphasis in cancer research, because within the last decades a huge variety of small GTPases from different subgroups could be related to the development of all types of tumors. Using a random forest approach, we were able to identify the most important amino acid positions for the classification process within the small GTPases superfamily and its subgroups. These positions are in line with the results of earlier studies and have been shown to be the essential elements for the different functionalities of the GTPase families. Furthermore, we provide an accurate and reliable software tool (GTPasePred) to identify potential novel GTPases and demonstrate its application to genome sequences. |
Mendeley readers
The data shown below were compiled from readership statistics for 23 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 2 | 9% |
| Unknown | 21 | 91% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 6 | 26% |
| Student > Postgraduate | 3 | 13% |
| Researcher | 3 | 13% |
| Student > Master | 2 | 9% |
| Student > Bachelor | 2 | 9% |
| Other | 2 | 9% |
| Unknown | 5 | 22% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 6 | 26% |
| Agricultural and Biological Sciences | 5 | 22% |
| Psychology | 2 | 9% |
| Computer Science | 1 | 4% |
| Arts and Humanities | 1 | 4% |
| Other | 2 | 9% |
| Unknown | 6 | 26% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. 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 05 December 2011.
All research outputs
#8,535,472
of 25,374,647 outputs
Outputs from Advances and Applications in Bioinformatics and Chemistry : AABC
#12
of 55 outputs
Outputs of similar age
#38,466
of 104,704 outputs
Outputs of similar age from Advances and Applications in Bioinformatics and Chemistry : AABC
#1
of 1 outputs
Altmetric has tracked 25,374,647 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 55 research outputs from this source. They receive a mean Attention Score of 2.5. This one has gotten more attention than average, scoring higher than 67% 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 104,704 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 21st percentile – i.e., 21% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them