A high-order representation and classification method for transcription factor binding sites recognition in Escherichia coli

作者：

Highlights：

• A novel tensor-based representation for TFBSs is proposed.

• Tensor-based representation captures more information than vector representation.

• Tensor-based representation captures interactions among physicochemical properties.

• Tensor-based representation alleviates the risk of over-fitting.

摘要

Highlights•A novel tensor-based representation for TFBSs is proposed.•Tensor-based representation captures more information than vector representation.•Tensor-based representation captures interactions among physicochemical properties.•Tensor-based representation alleviates the risk of over-fitting.

论文关键词：Tensor,Partial least squares,Transcription factor binding sites,Machine learning,Classification,Computational biology

论文评审过程：Received 7 June 2016, Accepted 23 November 2016, Available online 1 December 2016, Version of Record 22 December 2016.

论文官网地址：https://doi.org/10.1016/j.artmed.2016.11.004