Hi all
I have a confession to make. About two years ago, I was contemplating writing a comprehensive piece on what is known about honey bee genomics. Pretty soon I was overwhelmed by the quantity of information on the topic. I wrote to several key players in the field to try to get help to narrow down the topic, but they basically pointed to ways in which it should be expanded.
So I succumbed, and decided to write about the history of beekeeping instead. There's just as much information there, but it's chronological, linear, and not undergoing drastic revision all the time. Like it or not, genomics is not linear, but multi-dimensional and probably is too complex to be understood by human minds unaided by so-called Machine Learning.
> Machine learning methods have been applied in Genome-Wide Association Studies for identification of candidate genes, epistasis detection, gene network pathway analyses and genomic prediction of phenotypic values. ... In this study, using 38,082 SNP markers and body weight phenotypes from 2,093 Brahman cattle (1,097 bulls as a discovery population and 996 cows as a validation population)
> Unlike parametric models (e.g., a linear mixed model) for GWAS in which the analysis generally provides the parameter estimates such as individual SNP allele substitution effect and a corresponding significance P value, the non-parametric models provide SNP VIM values to indicate the contributions of individual SNPs to the MSE.
Li, B., Zhang, N., Wang, Y. G., George, A. W., Reverter, A., & Li, Y. (2018). Genomic prediction of breeding values using a subset of SNPs identified by three machine learning methods. Frontiers in genetics, 9, 237.
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I get the same experience reading Stephen Hawking. He gently leads you in until before you know it you are already in way over your head. As I said before, I worked for ten years in a lab studying very specific genetic sequences in mice, trying to tease out what they do or don't do. Of course, not one of them acts alone, any more than one book can tell you very much about knowledge.
PLB
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