  Through sequence-based classification, this paper tries to accurately predict
the DNA binding sites of transcription factors (TFs) in an unannotated cellular
context. Related methods in the literature fail to perform such predictions
accurately, since they do not consider sample distribution shift of sequence
segments from an annotated (source) context to an unannotated (target) context.
We, therefore, propose a method called "Transfer String Kernel" (TSK) that
achieves improved prediction of transcription factor binding site (TFBS) using
knowledge transfer via cross-context sample adaptation. TSK maps sequence
segments to a high-dimensional feature space using a discriminative mismatch
string kernel framework. In this high-dimensional space, labeled examples of
the source context are re-weighted so that the revised sample distribution
matches the target context more closely. We have experimentally verified TSK
for TFBS identifications on fourteen different TFs under a cross-organism
setting. We find that TSK consistently outperforms the state-of the-art TFBS
tools, especially when working with TFs whose binding sequences are not
conserved across contexts. We also demonstrate the generalizability of TSK by
showing its cutting-edge performance on a different set of cross-context tasks
for the MHC peptide binding predictions.
