Fast and accurate inference of gene regulatory networks through robust precision matrix estimation
Bioinformatics, ISSN: 1460-2059, Vol: 38, Issue: 10, Page: 2802-2809
2022
- 9Citations
- 18Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations9
- Citation Indexes9
- CrossRef9
- Captures18
- Readers18
- 18
Article Description
Motivation: Transcriptional regulation mechanisms allow cells to adapt and respond to external stimuli by altering gene expression. The possible cell transcriptional states are determined by the underlying gene regulatory network (GRN), and reliably inferring such network would be invaluable to understand biological processes and disease progression. Results: In this article, we present a novel method for the inference of GRNs, called PORTIA, which is based on robust precision matrix estimation, and we show that it positively compares with state-of-the-art methods while being orders of magnitude faster. We extensively validated PORTIA using the DREAM and MERLIN+P datasets as benchmarks. In addition, we propose a novel scoring metric that builds on graph-theoretical concepts.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85132347938&origin=inward; http://dx.doi.org/10.1093/bioinformatics/btac178; http://www.ncbi.nlm.nih.gov/pubmed/35325022; http://www.ncbi.nlm.nih.gov/pubmed/35561176; https://academic.oup.com/bioinformatics/article/38/10/2802/6553011; https://dx.doi.org/10.1093/bioinformatics/btac178
Oxford University Press (OUP)
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