Mapping and Modification of Gene Network Endophenotypes
Summary
The USPTO published patent application US20260100246A1 by Inari Agriculture Technology, Inc., disclosing a machine learning method for predicting endophenotypes of interacting partner genes. The method partitions endophenotype profiles into two sets, modifies one set to a desired level, and uses a trained ML model to predict resulting changes in the unmodified set. The invention enables targeted modification of gene networks to achieve desired agricultural traits.
What changed
The USPTO published patent application US20260100246A1 disclosing a machine learning method for predicting endophenotypes of interacting partner genes in agricultural applications. The method involves obtaining endophenotype profiles corresponding to a genotype, partitioning profiles into two sets, modifying one set to a desired level, and inputting both sets into a trained ML model to predict resulting changes in the second set.
Agricultural biotechnology companies developing gene editing or precision breeding solutions should review this application for potential prior art or licensing considerations. The method's focus on predicting phenotypic outcomes from targeted gene modifications could impact R&D strategies for crop improvement programs.
What to do next
- Monitor for patent grant or rejection notices
- Review claims for potential licensing opportunities
Archived snapshot
Apr 10, 2026GovPing captured this document from the original source. If the source has since changed or been removed, this is the text as it existed at that time.
MAPPING AND MODIFICATION OF GENE NETWORK ENDOPHENOTYPES
Application US20260100246A1 Kind: A1 Apr 09, 2026
Assignee
Inari Agriculture Technology, Inc.
Inventors
Ross Everett ALTMAN, Karl Anton Grothe KREMLING
Abstract
A method for predicting endophenotypes of interacting partner genes includes obtaining one or more endophenotype profiles corresponding to a genotype, partitioning the one or more endophenotype profiles into a first set of endophenotypes and a second set of endophenotypes, and receiving an input to modify the first set of endophenotypes to a desired level. The method thus includes inputting the modified first set of endophenotypes and unmodified second set of endophenotypes into a trained machine-learning model to obtain a prediction of an updated second set of endophenotypes. The updated second set of endophenotypes represents an updated version of the second set of endophenotypes after interacting with the modified subset of the first set of endophenotypes.
CPC Classifications
G16B 20/00 C12N 9/222 C12N 15/11 C12N 15/8213 G16B 40/20 C12N 2310/20
Filing Date
2023-06-23
Application No.
18877684
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