I have the following data
feat_1 feat_2 ... feat_n label gene_1 100.33 10.2 ... 90.23 great gene_2 13.32 87.9 ... 77.18 soso .... gene_m 213.32 63.2 ... 12.23 quite_good
The size of
M is large ~30K rows, and
N is much smaller ~10 columns.
My question is what is the appropriate Deep Learning structure to learn
and test the data like above.
At the end of the day, the user will give a vector of genes with expression.
gene_1 989.00 gene_2 77.10 ... gene_N 100.10
And the system will label which label does each gene apply e.g. great or soso, etc...
By structure I mean one of these:
- Convolutional Neural Network (CNN)
- Deep Belief Network (DBN)
- Restricted Boltzman Machine