Predicting growth, water-use efficiency and drought response through machine learning, GWAS and differential expression in Ponderosa pine.
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| Title: | Predicting growth, water-use efficiency and drought response through machine learning, GWAS and differential expression in Ponderosa pine. |
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| Authors: | Collins SM; School of Forestry, Northern Arizona University, 200 E. Pine Knoll, Flagstaff, AZ, 86011, USA., Cathey MJ; School of Forestry, Northern Arizona University, 200 E. Pine Knoll, Flagstaff, AZ, 86011, USA., Barrera M; School of Forestry, Northern Arizona University, 200 E. Pine Knoll, Flagstaff, AZ, 86011, USA., Harris B; School of Forestry, Northern Arizona University, 200 E. Pine Knoll, Flagstaff, AZ, 86011, USA., Baesen K; School of Forestry, Northern Arizona University, 200 E. Pine Knoll, Flagstaff, AZ, 86011, USA., Lincoln A; Department of Interior, Bureau of Land Management Grand Junction Field Office, Grand Junction, CO, 81506, USA., Dixit A; Department of Natural Resource Ecology & Management, Oklahoma State University Stillwater, Stillwater, OK, USA., De La Torre AR; School of Forestry, Northern Arizona University, 200 E. Pine Knoll, Flagstaff, AZ, 86011, USA. Amanda.de-la-torre@nau.edu. |
| Source: | BMC plant biology [BMC Plant Biol] 2026 May 29; Vol. 26 (1). Date of Electronic Publication: 2026 May 29. |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: BioMed Central Country of Publication: England NLM ID: 100967807 Publication Model: Electronic Cited Medium: Internet ISSN: 1471-2229 (Electronic) Linking ISSN: 14712229 NLM ISO Abbreviation: BMC Plant Biol Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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