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BTNET : boosted tree based gene regulatory network inference algorithm using time-course measurement data

Overview of attention for article published in BMC Systems Biology, March 2018
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Title
BTNET : boosted tree based gene regulatory network inference algorithm using time-course measurement data
Published in
BMC Systems Biology, March 2018
DOI 10.1186/s12918-018-0547-0
Pubmed ID
Authors

Sungjoon Park, Jung Min Kim, Wonho Shin, Sung Won Han, Minji Jeon, Hyun Jin Jang, Ik-Soon Jang, Jaewoo Kang

Abstract

Identifying gene regulatory networks is an important task for understanding biological systems. Time-course measurement data became a valuable resource for inferring gene regulatory networks. Various methods have been presented for reconstructing the networks from time-course measurement data. However, existing methods have been validated on only a limited number of benchmark datasets, and rarely verified on real biological systems. We first integrated benchmark time-course gene expression datasets from previous studies and reassessed the baseline methods. We observed that GENIE3-time, a tree-based ensemble method, achieved the best performance among the baselines. In this study, we introduce BTNET, a boosted tree based gene regulatory network inference algorithm which improves the state-of-the-art. We quantitatively validated BTNET on the integrated benchmark dataset. The AUROC and AUPR scores of BTNET were higher than those of the baselines. We also qualitatively validated the results of BTNET through an experiment on neuroblastoma cells treated with an antidepressant. The inferred regulatory network from BTNET showed that brachyury, a transcription factor, was regulated by fluoxetine, an antidepressant, which was verified by the expression of its downstream genes. We present BTENT that infers a GRN from time-course measurement data using boosting algorithms. Our model achieved the highest AUROC and AUPR scores on the integrated benchmark dataset. We further validated BTNET qualitatively through a wet-lab experiment and showed that BTNET can produce biologically meaningful results.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 41 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 41 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 20%
Researcher 8 20%
Student > Master 5 12%
Student > Doctoral Student 5 12%
Other 2 5%
Other 6 15%
Unknown 7 17%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 15 37%
Agricultural and Biological Sciences 5 12%
Engineering 3 7%
Mathematics 2 5%
Business, Management and Accounting 2 5%
Other 6 15%
Unknown 8 20%
Attention Score in Context

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 22 March 2018.
All research outputs
#17,934,709
of 23,028,364 outputs
Outputs from BMC Systems Biology
#772
of 1,144 outputs
Outputs of similar age
#241,468
of 332,288 outputs
Outputs of similar age from BMC Systems Biology
#21
of 43 outputs
Altmetric has tracked 23,028,364 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,144 research outputs from this source. They receive a mean Attention Score of 3.6. This one is in the 27th percentile – i.e., 27% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 332,288 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 22nd percentile – i.e., 22% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 43 others from the same source and published within six weeks on either side of this one. This one is in the 41st percentile – i.e., 41% of its contemporaries scored the same or lower than it.