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Computomics presents at 2021 ASA, CSSA, SSSA International Annual Meeting 7-10 November

Dr. Sebastian Schultheiss is presenting on Tuesday, 9 November 2021 at the 2021 ASA, CSSA, SSSA International Annual Meeting.

The Meeting is taking place 7-10 November, 2021 in person in Salt Lake City, UT. However there is also a virtual option.

This Annual Meeting is one of the few gatherings that bring together thousands of scientific leaders from industry, government agencies, and academic institutions in one environment. It’s the premier opportunity for professionals working in agronomic, crop, soil, and related sciences to hear about the latest research, meet and learn from their peers, expand their knowledge base, and take advantage of networking opportunities to enhance their careers. The Societies Annual Meeting features thousands of technical presentations, along with a host of networking events and award ceremonies. The world-class exhibition displays the latest scientific equipment, supplies, services, and reference materials available.


Dr. Sebastian Schulthieiss is presenting at the symposium “Application of Machine Learning and Artificial Intelligence in the Plant Breeding” for the Biometry and Statistical Computing Section.

Title: Higher-Order Machine Learning Models Act As an Approximation of Biological Regulatory Mechanisms
Date: Tuesday, 9 November 2021
Time: 7:30 PM - 8:00 PM

Plant breeding needs to accelerate to supply new varieties for a growing population and a rapidly changing climate. New breeding technologies like gene editing and genomic prediction help bring about this acceleration, but are often used independently without sharing useful preexisting knowledge. Here, we present a method for discovering both new gene editing targets and higher-accuracy predictions. By using interpretable machine learning models specifically developed for genomic data, complex genetic mechanisms can be rapidly understood and visualized. Multi-genic traits show up in the visualization of feature importance and positional genomic importance. We apply this method to a dataset derived from a shelf-life experiments for 200 Capsicum varieties. Genotypes, manual scoring and plant image data are correlated to train a regression machine learning algorithm that identifies an ethylene-linked gene cluster responsible for shelf life and plant senescence. New breeding technologies require these kinds of insights into biological regulation to identify new editing targets quickly and reliably.

The preliminary program can be found here.

If you want to know more about our machine learning technology you can reach out to us.

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