One hot encoding DNA
One hot encoding is a way to represent categorical data as binary vectors
Machine learning algorithms cannot work with categorical data directly. Instead we use character-level one-hot encoding to represent the categorical data(for us DNA).
One Hot Encoding and DNA sequences using numpy.
Each base is encoded as a vector of all zeros except one in a specific position, A is encoded as (1,0,0,0), T as (0,0,0,1), C as (0,1,0,0), and G as (0,0,1,0).
DNA bases are encoded following there ascii order
In case you are working with raw data, or data containing errors in other words, DNA sequences containing characters other than {“A”, “T”, “C”, “G”}, if sequencing errors has no significant your studies; we can do this little trick by encoding any error with a vector of zeros (0,0,0,0)
One Hot Encoding and DNA sequences using scikit learn
See you next time.
