Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/TCBB.2016.2609918 | IEEE/ACM transactions on computational biology and bioinformatics |
Keywords | Field | DocType |
Context,Kernel,Genomics,DNA,Proteins,Support vector machines,Bioinformatics | Sampling distribution,Computer science,Genomics,Artificial intelligence,String kernel,Discriminative model,Kernel (linear algebra),Feature vector,Pattern recognition,DNA binding site,Support vector machine,Bioinformatics,Machine learning | Journal |
Volume | Issue | ISSN |
abs/1609.03490 | 5 | 1545-5963 |
Citations | PageRank | References |
0 | 0.34 | 32 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ritambhara Singh | 1 | 40 | 6.95 |
| Jack Lanchantin | 2 | 65 | 8.01 |
| Gabriel Robins | 3 | 1264 | 216.68 |
| Qi, Yanjun | 4 | 684 | 45.77 |