Synthesising Executable Gene Regulatory Networks from Single-Cell Gene Expression Data
- Submitting institution
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The University of Leicester
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1401
- Type
- E - Conference contribution
- DOI
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10.1007/978-3-319-21690-4_38
- Title of conference / published proceedings
- 27th International Conference, CAV 2015, San Francisco, CA, USA, July 18-24, 2015
- First page
- 544
- Volume
- 9206
- Issue
- -
- ISSN
- 0302-9743
- Open access status
- Out of scope for open access requirements
- Month of publication
- July
- Year of publication
- 2015
- URL
-
-
- Supplementary information
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https://doi.org/10.1007/978-3-319-21690-4_38
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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3
- Research group(s)
-
-
- Citation count
- 6
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The technique described in this paper was used to suggest a model explaining the development of blood in mice, described in a counterpart biological paper (Moignard et al., Nature Biotech 2015). The Nature Biotech paper is highly-cited (FWCI 12.8 as of Jan21) including citations in Nature (3), Science (1), PNAS (2) and other Nature group journals (22). The technique was developed into a tool called SCNS (Woodhouse et al. BMC Biology, 2018). This paper and SNCS have been discussed in surveys on single-cell genomics (Wagner et al. Nature Biotech 2016, Fiers et al. Briefings in Functional Genomics, 2018).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -