A5001561348- Genetics and Plant Breeding
- Genetic and phenotypic traits in livestock
- Genetic and Environmental Crop Studies
- Analytical Chemistry and Chromatography
- Corrosion Behavior and Inhibition
- Marine Biology and Environmental Chemistry
- Lubricants and Their Additives
- Computational Drug Discovery Methods
- Water Quality Monitoring and Analysis
- Machine Learning in Materials Science
North Dakota State University
2024
Dakota State University
2024
Phenotypic evaluation and efficient utilization of germplasm collections can be time-intensive, laborious, expensive. However, with the plummeting costs next-generation sequencing addition genomic selection to plant breeder's toolbox, we now more efficiently tap genetic diversity within large collections. In this study, applied evaluated prediction's potential a set 482 pea (Pisum sativum L.) accessions-genotyped 30,600 single nucleotide polymorphic (SNP) markers phenotyped for seed yield...
Chlorinated compounds are generally known to be non-readily biodegradable. The insight into the structural features that allow chlorinated readily biodegrade is crucial information needs unveiled. Combined in silico modeling and machine learning approach predict desirable compound properties has proven an effective tool, enabling chemists save time resources compared web lab experimentation. Here we present two learning-based quantitative structure – biodegradability relationship (QSBR)...
Abstract Phenotypic evaluation and efficient utilization of germplasm collections can be time-intensive, laborious, expensive. However, with the plummeting costs next-generation sequencing addition genomic selection to plant breeder’s toolbox, we now more efficiently tap genetic diversity within large collections. In this study, applied evaluated selection’s potential a set 482 pea accessions – genotyped 30,600 single nucleotide polymorphic (SNP) markers phenotyped for seed yield...