-
Categories
-
Pharmaceutical Intermediates
-
Active Pharmaceutical Ingredients
-
Food Additives
- Industrial Coatings
- Agrochemicals
- Dyes and Pigments
- Surfactant
- Flavors and Fragrances
- Chemical Reagents
- Catalyst and Auxiliary
- Natural Products
- Inorganic Chemistry
-
Organic Chemistry
-
Biochemical Engineering
- Analytical Chemistry
-
Cosmetic Ingredient
- Water Treatment Chemical
-
Pharmaceutical Intermediates
Promotion
ECHEMI Mall
Wholesale
Weekly Price
Exhibition
News
-
Trade Service
Recently, the crop molecular breeding technology and application innovation team of the Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, together with a number of companies, proposed a new intelligent breeding strategy driven by big data and artificial intelligence - genome-environment group integration prediction
.
The proposal of this new strategy will change the breeding mode that mainly uses genotype and phenotype selection in the past, so that future breeding can accurately predict the performance of specific genotypes in specific environments with the support of all-round information of genotype-phenotype-environment, and realize directional breeding
that adapts to climate change and adapts to specific environments in the true sense.
The relevant research results were published in Molecular Plant
.
.
The proposal of this new strategy will change the breeding mode that mainly uses genotype and phenotype selection in the past, so that future breeding can accurately predict the performance of specific genotypes in specific environments with the support of all-round information of genotype-phenotype-environment, and realize directional breeding
that adapts to climate change and adapts to specific environments in the true sense.
The relevant research results were published in Molecular Plant
.
A plant's phenotype is the result of
its genotype working together with its environment.
Most of the genomic selection techniques used in breeding predict phenotypes based on an individual's genotype, and little consideration is given to the influence
of an individual's environment on phenotypic prediction.
By subjecting an individual with a fully understood genotype to conditions known to environmental factors, it is theoretically possible to accurately predict
his phenotype.
its genotype working together with its environment.
Most of the genomic selection techniques used in breeding predict phenotypes based on an individual's genotype, and little consideration is given to the influence
of an individual's environment on phenotypic prediction.
By subjecting an individual with a fully understood genotype to conditions known to environmental factors, it is theoretically possible to accurately predict
his phenotype.
This study comprehensively introduces the challenges of big data to artificial intelligence generated by the introduction of environmental type variation in different spatiotemporal spaces and its integration with genotype and phenotype, and discusses the concepts related to genome-environment group integration prediction and the construction, optimization and implementation
of its models.
Information about genes, metabolic pathways and networks can be translated into new dimensions of genotype data and integrated into predictive models
.
Prediction-based crop redesign can target genes, metabolism, and networks at the micro level, and individuals, populations, and species
at the macro level.
This study looks forward to how to use intelligent breeding to improve the genetic gain of plant improvement, including integrating and using various breeding technologies and methods, through the sharing of platforms, technologies, facilities, populations, data, models, and even breeding materials, to maximize resource utilization and breeding efficiency, and effectively carry out intelligent breeding with the support of open source breeding
and service platforms.
New strategies for integrated genomic-environmental prediction will drive major changes
in areas such as intelligent breeding and crop cultivation physiology.
(Correspondent Wei Fei)
of its models.
Information about genes, metabolic pathways and networks can be translated into new dimensions of genotype data and integrated into predictive models
.
Prediction-based crop redesign can target genes, metabolism, and networks at the micro level, and individuals, populations, and species
at the macro level.
This study looks forward to how to use intelligent breeding to improve the genetic gain of plant improvement, including integrating and using various breeding technologies and methods, through the sharing of platforms, technologies, facilities, populations, data, models, and even breeding materials, to maximize resource utilization and breeding efficiency, and effectively carry out intelligent breeding with the support of open source breeding
and service platforms.
New strategies for integrated genomic-environmental prediction will drive major changes
in areas such as intelligent breeding and crop cultivation physiology.
(Correspondent Wei Fei)
Original link:
