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Mendeley readers
| Title |
A structure-based Multiple-Instance Learning approach to predicting in vitrotranscription factor-DNA interaction
|
|---|---|
| Published in |
BMC Genomics, April 2015
|
| DOI | 10.1186/1471-2164-16-s4-s3 |
| Pubmed ID | |
| Authors |
Zhen Gao, Jianhua Ruan |
| Abstract |
Understanding the mechanism of transcriptional regulation remains an inspiring stage of molecular biology. Recently, in vitro protein-binding microarray experiments have greatly improved the understanding of transcription factor-DNA interaction. We present a method - MIL3D - which predicts in vitro transcription factor binding by multiple-instance learning with structural properties of DNA. Evaluation on in vitro data of twenty mouse transcription factors shows that our method outperforms a method based on simple-instance learning with DNA structural properties, and the widely used k-mer counting method, for nineteen out of twenty of the transcription factors. Our analysis showed that the MIL3D approach can utilize subtle structural similarities when a strong sequence consensus is not available. Combining multiple-instance learning and structural properties of DNA has promising potential for studying biological regulatory networks. |
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 % |
|---|---|---|
| Unknown | 23 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 5 | 22% |
| Researcher | 4 | 17% |
| Student > Doctoral Student | 2 | 9% |
| Student > Master | 2 | 9% |
| Student > Bachelor | 1 | 4% |
| Other | 1 | 4% |
| Unknown | 8 | 35% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 4 | 17% |
| Computer Science | 3 | 13% |
| Agricultural and Biological Sciences | 2 | 9% |
| Immunology and Microbiology | 2 | 9% |
| Business, Management and Accounting | 1 | 4% |
| Other | 3 | 13% |
| Unknown | 8 | 35% |