用于跨上下文DNA-蛋白质结合预测的传输串核
Transfer String Kernel for Cross-Context DNA-Protein Binding Prediction
摘要：
通过基于序列的分类，本文试图准确地预测DNA结合位点的转录因子（TF）在一个未注释的细胞背景。文献中的相关方法不能准确地进行这样的预测，因为它们没有考虑序列片段从注释（源）背景到未注释（靶）背景的样本分布偏移。因此，我们提出了一种称为“传输字符串内核”（TSK）的方法，通过跨上下文样本适应，使用知识转移来实现对转录因子结合位点（TFBS）的改进预测。TSK映射序列段到一个高维特征空间，使用一个有区别的不匹配字符串内核框架。在这个高维空间中，源上下文的标记示例被重新加权，使得修订的样本分布更紧密地匹配目标上下文。我们已经通过实验验证了TSK的TFBS标识在十四个不同的TF下的跨生物体设置。我们发现，TSK始终优于国家的最先进的TFBS工具，特别是当与TF的结合序列不保守的上下文中。我们还展示了TSK的普遍性，通过展示其在不同的一组跨上下文任务的MHC肽结合预测的尖端性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
