| Title |
A mesh generation and machine learning framework for Drosophilagene expression pattern image analysis
|
|---|---|
| Published in |
BMC Bioinformatics, December 2013
|
| DOI | 10.1186/1471-2105-14-372 |
| Pubmed ID | |
| Authors |
Wenlu Zhang, Daming Feng, Rongjian Li, Andrey Chernikov, Nikos Chrisochoides, Christopher Osgood, Charlotte Konikoff, Stuart Newfeld, Sudhir Kumar, Shuiwang Ji |
| Abstract |
Multicellular organisms consist of cells of many different types that are established during development. Each type of cell is characterized by the unique combination of expressed gene products as a result of spatiotemporal gene regulation. Currently, a fundamental challenge in regulatory biology is to elucidate the gene expression controls that generate the complex body plans during development. Recent advances in high-throughput biotechnologies have generated spatiotemporal expression patterns for thousands of genes in the model organism fruit fly Drosophila melanogaster. Existing qualitative methods enhanced by a quantitative analysis based on computational tools we present in this paper would provide promising ways for addressing key scientific questions. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Norway | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 50% |
| Members of the public | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 28 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Sweden | 1 | 4% |
| Unknown | 27 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 5 | 18% |
| Student > Master | 4 | 14% |
| Researcher | 4 | 14% |
| Student > Bachelor | 3 | 11% |
| Professor | 1 | 4% |
| Other | 2 | 7% |
| Unknown | 9 | 32% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 5 | 18% |
| Engineering | 4 | 14% |
| Agricultural and Biological Sciences | 4 | 14% |
| Biochemistry, Genetics and Molecular Biology | 2 | 7% |
| Medicine and Dentistry | 2 | 7% |
| Other | 1 | 4% |
| Unknown | 10 | 36% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 06 January 2014.
All research outputs
#15,866,607
of 23,577,654 outputs
Outputs from BMC Bioinformatics
#5,477
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Outputs of similar age
#194,414
of 309,055 outputs
Outputs of similar age from BMC Bioinformatics
#81
of 111 outputs
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