Journal of Computer Science and Technology ›› 2021, Vol. 36 ›› Issue (2): 299-309.doi: 10.1007/s11390-021-0804-3

Special Issue: Emerging Areas

• Special Section on AI and Big Data Analytics in Biology and Medicine • Previous Articles     Next Articles

GAEBic: A Novel Biclustering Analysis Method for miRNA-Targeted Gene Data Based on Graph Autoencoder

Li Wang1, Hao Zhang1,2,*, Senior Member, CCF, Hao-Wu Chang2, Qing-Ming Qin3, Bo-Rui Zhang4, Xue-Qing Li2, Tian-Heng Zhao2, and Tian-Yue Zhang2        

  1. 1 College of Software, Jilin University, Changchun 130012, China;
    2 College of Computer Science and Technology, Jilin University, Changchun 130012, China;
    3 College of Plant Science, Jilin University, Changchun 130062, China;
    4 Department of Biochemistry, University of Illinois at Urbana-Champaign, Champaign 61820, U.S.A
  • Received:2020-07-14 Revised:2021-03-05 Online:2021-03-05 Published:2021-04-01
  • Contact: Hao Zhang
  • About author:Li Wang is a Master candidate of College of Software, Jilin University, Changchun. His research interests include data mining, machine learning, bioinformatics, pattern recognition, image processing, and neural network.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China under Grant No. 62072210 and the Project of the Development and Reform Commission of Jilin Province of China under Grant No. 2019C053-6.

Unlike traditional clustering analysis, the biclustering algorithm works simultaneously on two dimensions of samples (row) and variables (column). In recent years, biclustering methods have been developed rapidly and widely applied in biological data analysis, text clustering, recommendation system and other fields. The traditional clustering algorithms cannot be well adapted to process high-dimensional data and/or large-scale data. At present, most of the biclustering algorithms are designed for the differentially expressed big biological data. However, there is little discussion on binary data clustering mining such as miRNA-targeted gene data. Here, we propose a novel biclustering method for miRNA-targeted gene data based on graph autoencoder named as GAEBic. GAEBic applies graph autoencoder to capture the similarity of sample sets or variable sets, and takes a new irregular clustering strategy to mine biclusters with excellent generalization. Based on the miRNA-targeted gene data of soybean, we benchmark several different types of the biclustering algorithm, and find that GAEBic performs better than Bimax, Bibit and the Spectral Biclustering algorithm in terms of target gene enrichment. This biclustering method achieves comparable performance on the high throughput miRNA data of soybean and it can also be used for other species.

Key words: biclustering; graph autoencoder; miRNA-targeted gene; binary data;

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