I want to develop a prediction model (e.g. using SVM, Neural Networks...etc) to predict the relationship between a protein and its DNA target. Each proteins is represented using ~100 continuous [-infinity,+infinity] numerical variables + one categorical (nominal) variable. However, its DNA target is a sequence of A,C,G and T letter and will be represented in also a categorical variable.
One feature vector should combine features (variables) from both of the protein and its target DNA sequence. So, I have to represent mixture of continuous and categorical (nominal) variables.
The categorical (nominal variables) are two types:
1) One type is to represent DNA Sequence (e.g. AACTT) [Note: we have four possibilities for DNA letters: A,C,G or T]
2) Another type is the category of the protein (I have 69 classes).
So, my questions are:
1) I am wondering what is the best representation for both types of categorical variables? (e.g. I saw people represent A,C,G and T as 0001,0010,0100 and 1000, respectively, while two binary digits were sufficient). What about the 69 classes variable?
2) Can I combine the continuous and categorical variables in one feature vector?
I have looked into similar questions in this group, but could not find relevant answer to what I have.