Title |
A structure-based Multiple-Instance Learning approach to predicting in vitrotranscription factor-DNA interaction
|
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Published in |
BMC Genomics, April 2015
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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
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 23 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
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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% |