Title |
The Local Edge Machine: inference of dynamic models of gene regulation
|
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Published in |
Genome Biology, October 2016
|
DOI | 10.1186/s13059-016-1076-z |
Pubmed ID | |
Authors |
Kevin A. McGoff, Xin Guo, Anastasia Deckard, Christina M. Kelliher, Adam R. Leman, Lauren J. Francey, John B. Hogenesch, Steven B. Haase, John L. Harer |
Abstract |
We present a novel approach, the Local Edge Machine, for the inference of regulatory interactions directly from time-series gene expression data. We demonstrate its performance, robustness, and scalability on in silico datasets with varying behaviors, sizes, and degrees of complexity. Moreover, we demonstrate its ability to incorporate biological prior information and make informative predictions on a well-characterized in vivo system using data from budding yeast that have been synchronized in the cell cycle. Finally, we use an atlas of transcription data in a mammalian circadian system to illustrate how the method can be used for discovery in the context of large complex networks. |
X Demographics
Geographical breakdown
Country | Count | As % |
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United Kingdom | 2 | 33% |
Germany | 1 | 17% |
Unknown | 3 | 50% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 3 | 50% |
Scientists | 2 | 33% |
Science communicators (journalists, bloggers, editors) | 1 | 17% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 2 | 3% |
Canada | 1 | 2% |
Unknown | 58 | 95% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 19 | 31% |
Student > Ph. D. Student | 15 | 25% |
Student > Doctoral Student | 4 | 7% |
Student > Master | 4 | 7% |
Student > Bachelor | 3 | 5% |
Other | 10 | 16% |
Unknown | 6 | 10% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 23 | 38% |
Biochemistry, Genetics and Molecular Biology | 18 | 30% |
Computer Science | 5 | 8% |
Mathematics | 4 | 7% |
Physics and Astronomy | 2 | 3% |
Other | 3 | 5% |
Unknown | 6 | 10% |