Title |
Inferring microRNA and transcription factor regulatory networks in heterogeneous data
|
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Published in |
BMC Bioinformatics, March 2013
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DOI | 10.1186/1471-2105-14-92 |
Pubmed ID | |
Authors |
Thuc D Le, Lin Liu, Bing Liu, Anna Tsykin, Gregory J Goodall, Kenji Satou, Jiuyong Li |
Abstract |
Transcription factors (TFs) and microRNAs (miRNAs) are primary metazoan gene regulators. Regulatory mechanisms of the two main regulators are of great interest to biologists and may provide insights into the causes of diseases. However, the interplay between miRNAs and TFs in a regulatory network still remains unearthed. Currently, it is very difficult to study the regulatory mechanisms that involve both miRNAs and TFs in a biological lab. Even at data level, a network involving miRNAs, TFs and genes will be too complicated to achieve. Previous research has been mostly directed at inferring either miRNA or TF regulatory networks from data. However, networks involving a single type of regulator may not fully reveal the complex gene regulatory mechanisms, for instance, the way in which a TF indirectly regulates a gene via a miRNA. |
X Demographics
Geographical breakdown
Country | Count | As % |
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Japan | 1 | 20% |
France | 1 | 20% |
Australia | 1 | 20% |
Unknown | 2 | 40% |
Demographic breakdown
Type | Count | As % |
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Scientists | 4 | 80% |
Members of the public | 1 | 20% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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United Kingdom | 3 | 3% |
France | 1 | <1% |
Australia | 1 | <1% |
India | 1 | <1% |
Bangladesh | 1 | <1% |
Singapore | 1 | <1% |
Denmark | 1 | <1% |
Korea, Republic of | 1 | <1% |
United States | 1 | <1% |
Other | 0 | 0% |
Unknown | 97 | 90% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 36 | 33% |
Student > Ph. D. Student | 24 | 22% |
Student > Master | 15 | 14% |
Professor | 7 | 6% |
Professor > Associate Professor | 7 | 6% |
Other | 14 | 13% |
Unknown | 5 | 5% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 59 | 55% |
Biochemistry, Genetics and Molecular Biology | 16 | 15% |
Computer Science | 13 | 12% |
Nursing and Health Professions | 2 | 2% |
Mathematics | 2 | 2% |
Other | 6 | 6% |
Unknown | 10 | 9% |