TY - GEN
T1 - Predicting miRNA-Disease Associations via a New MeSH Headings Representation of Diseases and eXtreme Gradient Boosting
AU - Ji, Bo Ya
AU - You, Zhu Hong
AU - Wang, Lei
AU - Wong, Leon
AU - Su, Xiao Rui
AU - Zhao, Bo Wei
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - Taking into account the intrinsic high cost and time-consuming in traditional Vitro studies, a computational approach that can enable researchers to easily predict the potential miRNA-disease associations is imminently required. In this paper, we propose a computational method to predict potential associations between miRNAs and diseases via a new MeSH headings representation of diseases and eXtreme Gradient Boosting algorithm. Particularly, a novel MeSHHeading2vec method is first utilized to obtain a higher-quality MeSH heading representation of diseases, and then it is fused with miRNA functional similarity, disease semantic similarity and Gaussian interaction profile kernel similarity information to efficiently represent miRNA-disease pairs. Second, the deep auto-encoder neural network is adopted to extract the more representative feature subspace from the initial feature set. Finally, the eXtreme Gradient Boosting (XGBoost) algorithm is implemented for training and prediction. In the 5-fold cross-validation experiment, our method obtained average accuracy and AUC of 0.8668 and 0.9407, which performed better than many existing works.
AB - Taking into account the intrinsic high cost and time-consuming in traditional Vitro studies, a computational approach that can enable researchers to easily predict the potential miRNA-disease associations is imminently required. In this paper, we propose a computational method to predict potential associations between miRNAs and diseases via a new MeSH headings representation of diseases and eXtreme Gradient Boosting algorithm. Particularly, a novel MeSHHeading2vec method is first utilized to obtain a higher-quality MeSH heading representation of diseases, and then it is fused with miRNA functional similarity, disease semantic similarity and Gaussian interaction profile kernel similarity information to efficiently represent miRNA-disease pairs. Second, the deep auto-encoder neural network is adopted to extract the more representative feature subspace from the initial feature set. Finally, the eXtreme Gradient Boosting (XGBoost) algorithm is implemented for training and prediction. In the 5-fold cross-validation experiment, our method obtained average accuracy and AUC of 0.8668 and 0.9407, which performed better than many existing works.
KW - Deep auto-encoder neural network
KW - eXtreme Gradient Boosting
KW - MeSH headings representation
KW - miRNA-disease associations
KW - Multiple similarities fusion
UR - http://www.scopus.com/inward/record.url?scp=85113821494&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-84532-2_5
DO - 10.1007/978-3-030-84532-2_5
M3 - 会议稿件
AN - SCOPUS:85113821494
SN - 9783030845315
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 49
EP - 56
BT - Intelligent Computing Theories and Application - 17th International Conference, ICIC 2021, Proceedings
A2 - Huang, De-Shuang
A2 - Jo, Kang-Hyun
A2 - Li, Jianqiang
A2 - Gribova, Valeriya
A2 - Bevilacqua, Vitoantonio
PB - Springer Science and Business Media Deutschland GmbH
T2 - 17th International Conference on Intelligent Computing, ICIC 2021
Y2 - 12 August 2021 through 15 August 2021
ER -