In this work, we consider a transfer learning approach based on K-means for splice site recognition. We use different representations for the sequences, based on n-gram graphs. In addition, a novel represen- tation based on the secondary structure of the sequences is proposed. We evaluate our approach on genomic sequence data from model organisms of varying evolutionary distance. The first obtained results indicate that the proposed representations are promising for the problem of splice site recognition.
['Georgios Giannoulis', 'Anastasia Krithara', 'Christos Karatsalos', 'Georgios Paliouras']
Splice Site Recognition Using Transfer Learning