Robotics & Machine Learning Daily News2024,Issue(Nov.4) :238-239.

Predicting the natural yeast phenotypic landscape with machine learning

Robotics & Machine Learning Daily News2024,Issue(Nov.4) :238-239.

Predicting the natural yeast phenotypic landscape with machine learning

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Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – According to news reporting based on a preprint abstract, our journalists obtained the following quote sourced from bi orxiv.org: “Most organisms\’ traits result from the complex inter play of many genetic and environmental factors, making their prediction from gen otypes difficult. Here, we used machine learning models to explore genotype-phen otype connections for 223 life history traits measured across 1011 genome-sequen ced Saccharomyces cerevisiae strains. Firstly, we used genome-wide association s tudies to connect genetic variants with the phenotypes. Next, we benchmarked an automated machine learning pipeline that includes preprocessing, feature selecti on, and hyperparameters optimization in combination with multiple linear and com plex machine learning methods.

Key words

Cyborgs/Emerging Technologies/Genetics/Machine Learning

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出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

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