首页|Predicting fitness related traits using gene expression and machine learning
Predicting fitness related traits using gene expression and machine learning
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According to news reporting based on a preprint abstract, our journalists obtained the following quote sourced from biorxiv.org: "Evolution by natural selection occurs at its most basic through the change in frequencies of alleles; connecting those genomic targets to phenotypic selection is an important goal for evolutionary biology in the genomics era. The relative abundance of gene products expressed in a tissue can be considered a phenotype intermediate to the genes and genomic regulatory elements themselves, and more traditionally measured macroscopic phenotypic traits such as flowering time, size, or growth. The high-dimensionality, low sample size nature of transcriptomic sequence data is a double-edged sword, however, as it provides abundant information but makes traditional statistics difficult. Machine learning has many features which handle high-dimensional data well and is thus useful in genetic sequence applications. "Here we examined the association of fitness-components with gene expression data in Ipomoea hederacea (Ivyleaf Morning Glory) grown under field conditions.
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