Useful Representation of Continuous and Nominal variables

Question

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.

Answer 1

To clarify, the DNA may be represented as {ATCGGATCAAGCTT....(20 such characters)} and protein as {1,38,-705,50,986,-5,7,-890,...(100 such numbers)}+{1 of 69 categories}. And you want to combine and make a another entity which has one DNA and one protein. So the new entity will have 3 components.

It seems that for protein part you want to show 101 features: 100 as numbers that represent the chemical properties of the protein (e.g. its hydrophobic score..etc) and last 1 as category (1 of 69 categories). It is like a table showing different features of different persons:

person_name height weight waist age gender family_name ...

So your can create a table with following columns:

DNA_sequence
Protein_name
Chemical_feature1
Chemical_feature2
Chemical_feature3
..
Protein_category_1_to_69

Then you can try to find which chemical feature or category is associated with which DNA sequence.

I believe DNA sequences can also be broken down to 'triplets'. That may also be helpful in finding associations.

I think you should break down your information into separate variables for better analysis. Each row of above table will be your combined entity.

Edit: To have analysis as described in the comment below, one can simply create a text string from 100 chemical features of proteins, eg:

"100,-52,-1,0.5,259,365,...."

This will then become one categorical variable (column) from 100 different numeric variables (columns). One can use 'paste' or 'paste0' function of R for this.