@inproceedings{5ced6f24eb7c41638943f70f081cbe83,
title = "A Unified Deep Biological Sequence Representation Learning with Pretrained Encoder-Decoder Model",
abstract = "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.",
keywords = "Deep learning, Pre-trained model, Representation learning, Seq2Seq, Sequence analysis",
author = "Yi, {Hai Cheng} and You, {Zhu Hong} and Su, {Xiao Rui} and Huang, {De Shuang} and Guo, {Zhen Hao}",
note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 16th International Conference on Intelligent Computing, ICIC 2020 ; Conference date: 02-10-2020 Through 05-10-2020",
year = "2020",
doi = "10.1007/978-3-030-60802-6_30",
language = "英语",
isbn = "9783030608019",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "339--347",
editor = "De-Shuang Huang and Kang-Hyun Jo",
booktitle = "Intelligent Computing Theories and Application - 16th International Conference, ICIC 2020, Proceedings",
}