A Unified Deep Biological Sequence Representation Learning with Pretrained Encoder Decoder Model

Machine learning methods are increasingly being applied to model and predict biomolecular interactions, while efficient feature representation plays a vital role. To this end, a unified biological sequence deep representation learning framework BioSeq2vec is proposed to extract discriminative features of any type of biological sequence. For arbitrary-length sequence input, the BioSeq2vec produces fixed-length efficient feature representation, which can be applied to various learning models. The performance of BioSeq2vec is evaluated on lncRNA-protein interaction prediction tasks. Experimental results reveal the superior performance of BioSeq2vec in biological sequence feature representation and broad prospects in various genome informatics and computational biology studies.