Mathematica notebook to compare methods of machine learning for ribosom density on mRNA prediction (without redundant nucleotide features).
The outcome of "Growth and division in mathematics and medicine: A collaborative incubator" workshop https://www.homepages.ucl.ac.uk/~ucakwat/gd.html
Dao Duc K, Song YS. The impact of ribosomal interference, codon usage, and exit tunnel interactions on translation elongation rate variation. PLoS Genet. 2018 Jan 16;14(1):e1007166. doi: 10.1371/journal.pgen.1007166 https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1007166
Tunney R, McGlincy NJ, Graham ME, Naddaf N, Pachter L, Lareau LF. Accurate design of translational output by a neural network model of ribosome distribution. Nat Struct Mol Biol. 2018 Jul;25(7):577-582. doi: 10.1038/s41594-018-0080-2. https://www.nature.com/articles/s41594-018-0080-2
Number of genes for training n=567, number of genes for testing m=283.
| Method | "NeuralNetwork" | "LinearRegression" | "RandomForest" | "NearestNeighbors" |
|---|---|---|---|---|
| Pearson correlation | 0.496767 | 0.43612 | 0.392796 | 0.307008 |