TY - GEN
T1 - Identification of miRNA-lncRNA Underlying Interactions Through Representation for Multiplex Heterogeneous Network
AU - Zhou, Jiren
AU - You, Zhuhong
AU - Shang, Xuequn
AU - Niu, Rui
AU - Yun, Yue
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - In several investigations of cancers, non-coding RNAs, especially lncRNAs (long non-coding RNAs) and miRNAs(microRNAs) have been proven that they are strongly relevant to diseases. For instance, neoplasms and non-small cell lung cancer are regulated by miRNA and lncRNA. However, it is complex that cancer could be co-regulated by multiple genes at the same time. Furthermore, different miRNAs and lncRNAs may also have interactions and regulations with others. The interactions among multiple genes still need to be interpreted. Traditional biology experiments are time-consumed and expensive. Increasing number of computational predictions of lncRNA-miRNA interactions have been seen as an alternative strategy to the biology methods for predict potential interactions. Considering that the complexity of molecular interactions, it should be more globally in identification underlying associations. We proposed a method using representation learning for attributed multiplex heterogeneous network. We conduct systematical evaluations for the model. The proposed method achieved ROC-AUC of 0.9180, PR-AUC of 0.8438, F1 scores of 0.7677. This method incorporated more biomolecular network information and provided more possibilities for discovering underlying bioinformatic associations.
AB - In several investigations of cancers, non-coding RNAs, especially lncRNAs (long non-coding RNAs) and miRNAs(microRNAs) have been proven that they are strongly relevant to diseases. For instance, neoplasms and non-small cell lung cancer are regulated by miRNA and lncRNA. However, it is complex that cancer could be co-regulated by multiple genes at the same time. Furthermore, different miRNAs and lncRNAs may also have interactions and regulations with others. The interactions among multiple genes still need to be interpreted. Traditional biology experiments are time-consumed and expensive. Increasing number of computational predictions of lncRNA-miRNA interactions have been seen as an alternative strategy to the biology methods for predict potential interactions. Considering that the complexity of molecular interactions, it should be more globally in identification underlying associations. We proposed a method using representation learning for attributed multiplex heterogeneous network. We conduct systematical evaluations for the model. The proposed method achieved ROC-AUC of 0.9180, PR-AUC of 0.8438, F1 scores of 0.7677. This method incorporated more biomolecular network information and provided more possibilities for discovering underlying bioinformatic associations.
KW - Heterogeneous association types
KW - miRNA-disease
KW - Multi-layer heterogeneous network embedding
UR - http://www.scopus.com/inward/record.url?scp=85139828718&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-13829-4_22
DO - 10.1007/978-3-031-13829-4_22
M3 - 会议稿件
AN - SCOPUS:85139828718
SN - 9783031138287
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 270
EP - 277
BT - Intelligent Computing Theories and Application - 18th International Conference, ICIC 2022, Proceedings
A2 - Huang, De-Shuang
A2 - Jo, Kang-Hyun
A2 - Jing, Junfeng
A2 - Premaratne, Prashan
A2 - Bevilacqua, Vitoantonio
A2 - Hussain, Abir
PB - Springer Science and Business Media Deutschland GmbH
T2 - 18th International Conference on Intelligent Computing, ICIC 2022
Y2 - 7 August 2022 through 11 August 2022
ER -
