I have a database of 3190 instances of DNA consisting of 60 sequential DNA nucleotide positions classified according to 3 types: EI, IE, Other.I want to formulate a supervised classifier.My present approach is to formulate a 2nd order Markov Transition Matrix for each instance and apply the resulting data to a Neural Network.How best to approach this classification problem, given that the Sequence of the data should be relevant? Is there a better approach than the one I came up with?
Classification of DNA Sequences
sequence
One way would be to create 20 features (each feature representing a codon). In this way, you would have a dataset with 3190 instances and 20 categorical features. There is no need to treat the sequence as a Markov chain. Once the dataset has been featurized as suggested above, any supervised classifier can work well. I would suggest using a gradient boosting machine as it might be better suited to handle categorical features. 