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Machine learning shows association between genetic variability inPPARGand cerebral connectivity in preterm infants
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Krishnan, Michelle L., Wang, Zi, Aljabar, Paul, Ball, Gareth, Mirza, Ghazala, Saxena, Alka, Counsell, Serena J., Hajnal, Joseph V., Montana, Giovanni and Edwards, A. David (2017) Machine learning shows association between genetic variability inPPARGand cerebral connectivity in preterm infants. Proceedings of the National Academy of Sciences, 114 (52). pp. 13744-13749. doi:10.1073/pnas.1704907114 ISSN 0027-8424.
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Official URL: http://dx.doi.org/10.1073/pnas.1704907114
Abstract
Preterm birth affects 11% of births globally; 35% of infants develop long-term neurocognitive problems, and prematurity leads to the loss of 75 million disability adjusted life years per annum worldwide. Imaging studies have shown that these infants have extensive alterations in brain development, but little is known about the molecular or cellular mechanisms involved. This imaging genetics study found a strong association between abnormal cerebral connectivity and variability in the PPARG gene, implicating PPARG signaling in abnormal white-matter development in preterm infants and suggesting a tractable new target for therapeutic research.
| Item Type: | Journal Article | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| Divisions: | Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) | ||||||||
| Journal or Publication Title: | Proceedings of the National Academy of Sciences | ||||||||
| Publisher: | National Academy of Sciences | ||||||||
| ISSN: | 0027-8424 | ||||||||
| Official Date: | 26 December 2017 | ||||||||
| Dates: |
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| Volume: | 114 | ||||||||
| Number: | 52 | ||||||||
| Page Range: | pp. 13744-13749 | ||||||||
| DOI: | 10.1073/pnas.1704907114 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Access rights to Published version: | Open Access (Creative Commons) |
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