Machine Learning Predicts Gene Sequence Effects on Endophenotypes
Summary
USPTO published patent application US20260100241A1 by Inari Agriculture Technology, Inc. describing a machine-learning method for predicting how gene regulatory sequences affect endophenotypes. The method involves inputting gene regulatory sequences into a trained model to generate effect predictions and selecting sequences based on desired phenotypic profiles.
What changed
USPTO published patent application US20260100241A1 for a machine-learning method that predicts effects of gene regulatory sequences on endophenotypes. The method involves obtaining gene regulatory sequences, inputting them into a trained machine-learning model to generate effect predictions across multiple endophenotypes, and selecting regulatory sequences that achieve desired phenotypic profiles.
For researchers and companies in agricultural biotechnology, crop improvement, and genomic research, this published application establishes intellectual property that may constrain future development in machine-learning-assisted gene regulatory sequence design. Freedom-to-operate analyses should consider these claims when developing similar computational genomics methods.
What to do next
- Monitor for patent prosecution updates and final grant
- Review claims for freedom-to-operate analysis
- Assess potential licensing opportunities if operating in agricultural biotech
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.
PREDICTING EFFECTS OF GENE REGULATORY SEQUENCES ON ENDOPHENOTYPES USING MACHINE LEARNING
Application US20260100241A1 Kind: A1 Apr 09, 2026
Assignee
Inari Agriculture Technology, Inc.
Inventors
Ross Everett ALTMAN, Karl Anton Grothe KREMLING
Abstract
A method for generating a gene regulatory sequence with a desired endophenotype profile includes obtaining a plurality of gene regulatory sequences and inputting the plurality of gene regulatory sequences into a machine-learning model trained to obtain a plurality of effect predictions corresponding to a plurality of endophenotypes. The method further includes selecting one or more desired endophenotypes based on the plurality of endophenotypes and selecting a gene regulatory sequence in accordance with the one or more desired endophenotypes.
CPC Classifications
G16B 5/20 G06N 20/00 G16B 20/50 G16B 40/20
Filing Date
2023-06-23
Application No.
18877673
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